In [10]:

    

import os
import csv
import platform
import pandas as pd
import networkx as nx
from graph_partitioning import GraphPartitioning, utils

run_metrics = True

cols = ["WASTE", "CUT RATIO", "EDGES CUT", "TOTAL COMM VOLUME", "Qds", "CONDUCTANCE", "MAXPERM", "NMI", "FSCORE", "FSCORE RELABEL IMPROVEMENT", "LONELINESS"]
#cols = ["WASTE", "CUT RATIO", "EDGES CUT", "TOTAL COMM VOLUME", "Q", "Qds", "CONDUCTANCE", "LONELINESS", "NETWORK PERMANENCE", "NORM. MUTUAL INFO", "EDGE CUT WEIGHT", "FSCORE", "FSCORE RELABEL IMPROVEMENT"]
#cols = ["WASTE", "CUT RATIO", "EDGES CUT", "TOTAL COMM VOLUME", "MODULARITY", "LONELINESS", "NETWORK PERMANENCE", "NORM. MUTUAL INFO", "EDGE CUT WEIGHT", "FSCORE", "FSCORE RELABEL IMPROVEMENT"]

pwd = %pwd

config = {

    "DATA_FILENAME": os.path.join(pwd, "data", "predition_model_tests", "network", "network_1.txt"),
    "OUTPUT_DIRECTORY": os.path.join(pwd, "output"),

    # Set which algorithm is run for the PREDICTION MODEL.
    # Either: 'FENNEL' or 'SCOTCH'
    "PREDICTION_MODEL_ALGORITHM": "FENNEL",

    # Alternativly, read input file for prediction model.
    # Set to empty to generate prediction model using algorithm value above.
    "PREDICTION_MODEL": "",

    
    "PARTITIONER_ALGORITHM": "FENNEL",

    # File containing simulated arrivals. This is used in simulating nodes
    # arriving at the shelter. Nodes represented by line number; value of
    # 1 represents a node as arrived; value of 0 represents the node as not
    # arrived or needing a shelter.
    "SIMULATED_ARRIVAL_FILE": os.path.join(pwd,
                                           "data",
                                           "predition_model_tests",
                                           "dataset_1_shift_rotate",
                                           "simulated_arrival_list",
                                           "percentage_of_prediction_correct_100",
                                           "arrival_100_1.txt"
                                          ),

    # File containing the prediction of a node arriving. This is different to the
    # simulated arrivals, the values in this file are known before the disaster.
    "PREDICTION_LIST_FILE": os.path.join(pwd,
                                         "data",
                                         "predition_model_tests",
                                         "dataset_1_shift_rotate",
                                         "prediction_list",
                                         "prediction_1.txt"
                                        ),

    # File containing the geographic location of each node, in "x,y" format.
    "POPULATION_LOCATION_FILE": os.path.join(pwd,
                                             "data",
                                             "predition_model_tests",
                                             "coordinates",
                                             "coordinates_1.txt"
                                            ),

    # Number of shelters
    "num_partitions": 4,

    # The number of iterations when making prediction model
    "num_iterations": 1,

    # Percentage of prediction model to use before discarding
    # When set to 0, prediction model is discarded, useful for one-shot
    "prediction_model_cut_off": .01,

    # Alpha value used in one-shot (when restream_batches set to 1)
    "one_shot_alpha": 0.5,
    
    # Number of arrivals to batch before recalculating alpha and restreaming.
    "restream_batches": 20,

    # When the batch size is reached: if set to True, each node is assigned
    # individually as first in first out. If set to False, the entire batch
    # is processed and empty before working on the next batch.
    "sliding_window": False,

    # Create virtual nodes based on prediction model
    "use_virtual_nodes": False,

    # Virtual nodes: edge weight
    "virtual_edge_weight": 1.0,
    
    # Loneliness score parameter. Used when scoring a partition by how many
    # lonely nodes exist.
    "loneliness_score_param": 1.2,


    ####
    # GRAPH MODIFICATION FUNCTIONS

    # Also enables the edge calculation function.
    "graph_modification_functions": True,

    # If set, the node weight is set to 100 if the node arrives at the shelter,
    # otherwise the node is removed from the graph.
    "alter_arrived_node_weight_to_100": False,

    # Uses generalized additive models from R to generate prediction of nodes not
    # arrived. This sets the node weight on unarrived nodes the the prediction
    # given by a GAM.
    # Needs POPULATION_LOCATION_FILE to be set.
    "alter_node_weight_to_gam_prediction": False,
    
    # Enables edge expansion when graph_modification_functions is set to true
    "edge_expansion_enabled": True,

    # The value of 'k' used in the GAM will be the number of nodes arrived until
    # it reaches this max value.
    "gam_k_value": 100,

    # Alter the edge weight for nodes that haven't arrived. This is a way to
    # de-emphasise the prediction model for the unknown nodes.
    "prediction_model_emphasis": 1.0,
    
    # This applies the prediction_list_file node weights onto the nodes in the graph
    # when the prediction model is being computed and then removes the weights
    # for the cutoff and batch arrival modes
    "apply_prediction_model_weights": True,

    "SCOTCH_LIB_PATH": os.path.join(pwd, "libs/scotch/macOS/libscotch.dylib")
    if 'Darwin' in platform.system()
    else "/usr/local/lib/libscotch.so",
    
    # Path to the PaToH shared library
    "PATOH_LIB_PATH": os.path.join(pwd, "libs/patoh/lib/macOS/libpatoh.dylib")
    if 'Darwin' in platform.system()
    else os.path.join(pwd, "libs/patoh/lib/linux/libpatoh.so"),
    
    "PATOH_ITERATIONS": 5,
        
    # Expansion modes: 'avg_node_weight', 'total_node_weight', 'smallest_node_weight'
    # 'largest_node_weight'
    # add '_squared' or '_sqrt' at the end of any of the above for ^2 or sqrt(weight)
    # i.e. 'avg_node_weight_squared
    "PATOH_HYPEREDGE_EXPANSION_MODE": 'no_expansion',
    
    # Edge Expansion: average, total, minimum, maximum, product, product_squared, sqrt_product
    "EDGE_EXPANSION_MODE" : 'sqrt_product',
    
    # Whether nodes should be reordered using a centrality metric for optimal node assignments in batch mode
    # This is specific to FENNEL and at the moment Leverage Centrality is used to compute new noder orders
    "FENNEL_NODE_REORDERING_ENABLED": False,
    
    # Whether the Friend of a Friend scoring system is active during FENNEL partitioning.
    # FOAF employs information about a node's friends to determine the best partition when
    # this node arrives at a shelter and no shelter has friends already arrived
    "FENNEL_FRIEND_OF_A_FRIEND_ENABLED": True,
    
    # Alters how much information to print. Keep it at 1 for this notebook.
    # 0 - will print nothing, useful for batch operations.
    # 1 - prints basic information on assignments and operations.
    # 2 - prints more information as it batches arrivals.
    "verbose": 1
}

gp = GraphPartitioning(config)

# Optional: shuffle the order of nodes arriving
# Arrival order should not be shuffled if using GAM to alter node weights
#random.shuffle(gp.arrival_order)

%pylab inline


    

    




Populating the interactive namespace from numpy and matplotlib

In [11]:

    

import scipy

iterations = 1000
#modes = ['total', 'average', 'minimum', 'maximum', 'product', 'product_squared', 'sqrt_product']
#modes = ['no_expansion']
modes = ['no_expansion', 'avg_node_weight_complete', 'total_node_weight_complete', 'smallest_node_weight_complete','largest_node_weight_complete']
#modes = ['no_expansion', 'product_node_weight_complete']

for mode in modes:

    metricsDataPrediction = []
    metricsDataAssign = []
    
    dataQdsOv = []
    dataCondOv = []

    config['EDGE_EXPANSION_MODE'] = mode
    print('Mode', mode)
    for i in range(0, iterations):
        if (i % 50) == 0:
            print('Mode', mode, 'Iteration', str(i))
        
        if mode == 'no_expansion':
            config['edge_expansion_enabled'] = False
                    
        config["DATA_FILENAME"] =  os.path.join(pwd, "data", "predition_model_tests", "network", "network_" + str(i + 1) + ".txt")

        
        gp = GraphPartitioning(config)
        gp.verbose = 0
        gp.load_network()
        gp.init_partitioner()

        m = gp.prediction_model()
        metricsDataPrediction.append(m[0])

        '''

        #write_graph_files
        #
        gp.metrics_timestamp = datetime.datetime.now().strftime('%H%M%S')
        f,_ = os.path.splitext(os.path.basename(gp.DATA_FILENAME))
        gp.metrics_filename = f + "-" + gp.metrics_timestamp


        if not os.path.exists(gp.OUTPUT_DIRECTORY):
            os.makedirs(gp.OUTPUT_DIRECTORY)
        if not os.path.exists(os.path.join(gp.OUTPUT_DIRECTORY, 'oslom')):
            os.makedirs(os.path.join(gp.OUTPUT_DIRECTORY, 'oslom'))

        
        file_oslom = os.path.join(gp.OUTPUT_DIRECTORY, 'oslom', "{}-all".format(gp.metrics_filename) + '-edges-oslom.txt')
        with open(file_oslom, "w") as outf:
            for e in gp.G.edges_iter(data=True):
                outf.write("{}\t{}\t{}\n".format(e[0], e[1], e[2]["weight"]))
        
        #file_oslom = utils.write_graph_files(gp.OUTPUT_DIRECTORY,
        #                             "{}-all".format(gp.metrics_filename),
        #                             gp.G,
        #                             quiet=True)

        community_metrics = utils.run_community_metrics(gp.OUTPUT_DIRECTORY,
                                                        "{}-all".format(gp.metrics_filename),
                                                        file_oslom)
        
        dataQdsOv.append(float(community_metrics['Qds']))
        dataCondOv.append(float(community_metrics['conductance']))
        '''


    ec = ''
    tcv = ''
    qds = ''
    conductance = ''
    maxperm = ''
    nmi = ''
    lonliness = ''
        
    qdsOv = ''
    condOv = ''

    dataEC = []
    dataTCV = [] 
    dataQDS = []
    dataCOND = []
    dataMAXPERM = []
    dataNMI = []
    dataLonliness = []

    
    for i in range(0, iterations):
        dataEC.append(metricsDataPrediction[i][2])
        dataTCV.append(metricsDataPrediction[i][3])
        dataQDS.append(metricsDataPrediction[i][4])
        dataCOND.append(metricsDataPrediction[i][5])
        dataMAXPERM.append(metricsDataPrediction[i][6])
        dataNMI.append(metricsDataPrediction[i][7])        
        dataLonliness.append(metricsDataPrediction[i][10])


        # UNCOMMENT FOR BATCH ARRIVAL
        #dataECB.append(metricsDataAssign[i][2])
        #dataTCVB.append(metricsDataAssign[i][3])

        if(len(ec)):
            ec = ec + ','
        ec = ec + str(metricsDataPrediction[i][2])
        if(len(tcv)):
            tcv = tcv + ','
        tcv = tcv + str(metricsDataPrediction[i][3])

        if(len(qds)):
            qds = qds + ','
        qds = qds + str(metricsDataPrediction[i][4])

        if(len(conductance)):
            conductance = conductance + ','
        conductance = conductance + str(metricsDataPrediction[i][5])

        if(len(maxperm)):
            maxperm = maxperm + ','
        maxperm = maxperm + str(metricsDataPrediction[i][6])

        if(len(nmi)):
            nmi = nmi + ','
        nmi = nmi + str(metricsDataPrediction[i][7])

        if(len(lonliness)):
            lonliness = lonliness + ','
        lonliness = lonliness + str(dataLonliness[i])
        
        '''
        if(len(qdsOv)):
            qdsOv = qdsOv + ','
        qdsOv = qdsOv + str(dataQdsOv[i])

        if(len(condOv)):
            condOv = condOv + ','
        condOv = condOv + str(dataCondOv[i])
        '''

    ec = 'EC_PM,' + config['EDGE_EXPANSION_MODE'] + ',' + str(scipy.mean(dataEC)) + ',' + str(scipy.std(dataEC)) + ',' + ec
    tcv = 'TCV_PM,' + config['EDGE_EXPANSION_MODE'] + ',' + str(scipy.mean(dataTCV)) + ',' + str(scipy.std(dataTCV)) + ',' + tcv

    lonliness = "LONELINESS," + config['EDGE_EXPANSION_MODE'] + ',' + str(scipy.mean(dataLonliness)) + ',' + str(scipy.std(dataLonliness)) + ',' + lonliness
    
    qds = 'QDS_PM,' + config['EDGE_EXPANSION_MODE'] + ',' + str(scipy.mean(dataQDS)) + ',' + str(scipy.std(dataQDS)) + ',' + qds
    conductance = 'CONDUCTANCE_PM,' + config['EDGE_EXPANSION_MODE'] + ',' + str(scipy.mean(dataCOND)) + ',' + str(scipy.std(dataCOND)) + ',' + conductance
    maxperm = 'MAXPERM_PM,' + config['EDGE_EXPANSION_MODE'] + ',' + str(scipy.mean(dataMAXPERM)) + ',' + str(scipy.std(dataMAXPERM)) + ',' + maxperm
    nmi = 'NMI_PM,' + config['EDGE_EXPANSION_MODE'] + ',' + str(scipy.mean(dataNMI)) + ',' + str(scipy.std(dataNMI)) + ',' + nmi

    #qdsOv = 'QDS_OV,' + config['PATOH_HYPEREDGE_EXPANSION_MODE'] + ',' + str(scipy.mean(dataQdsOv)) + ',' + str(scipy.std(dataQdsOv)) + qdsOv
    #condOv = 'CONDUCTANCE_OV,' + config['PATOH_HYPEREDGE_EXPANSION_MODE'] + ',' + str(scipy.mean(dataCondOv)) + ',' + str(scipy.std(dataCondOv)) + condOv

    print(ec)
    print(tcv)
    print(lonliness)
    print(qds)
    print(conductance)
    print(maxperm)
    #print(qdsOv)
    #print(condOv)


    

    




Mode no_expansion
Mode no_expansion Iteration 0
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Mode no_expansion Iteration 950
EC_PM,no_expansion,671.15,48.6617663058,718,781,599,768,700,591,679,709,654,589,705,688,675,636,718,644,715,673,623,664,694,724,648,634,696,691,709,641,592,631,645,649,642,641,730,677,576,642,625,673,724,643,596,644,623,734,560,625,654,587,667,687,638,662,733,664,635,653,720,754,714,660,697,728,697,709,704,661,703,662,633,676,753,638,647,609,699,615,762,635,699,648,685,659,661,639,694,698,683,687,560,687,672,604,633,639,760,646,689,680,605,668,689,618,662,621,733,656,693,658,686,657,647,677,617,689,691,673,642,688,744,651,693,650,693,666,615,639,686,629,711,631,594,643,761,592,699,605,732,634,698,685,712,702,654,633,700,683,628,728,642,693,634,694,613,631,698,633,682,605,669,713,655,665,675,670,746,682,707,661,696,687,693,682,609,712,623,642,687,682,771,752,633,633,673,698,567,664,694,703,643,791,767,779,673,625,622,608,580,675,659,606,610,741,671,676,613,613,661,616,650,767,615,722,591,699,627,672,741,735,785,757,568,667,657,647,668,573,639,692,716,708,689,594,673,677,594,678,740,684,650,665,637,627,601,665,597,695,611,687,659,669,672,719,632,643,594,641,677,647,670,596,648,640,592,625,688,615,653,704,711,733,704,662,605,687,651,698,706,719,726,655,822,698,692,701,693,670,678,724,562,654,697,642,719,668,630,709,686,643,754,618,662,664,723,626,737,705,678,726,750,752,670,564,680,615,754,601,732,673,657,728,753,672,667,735,684,718,725,714,617,596,714,710,593,680,608,667,650,751,709,667,723,713,675,621,801,646,626,690,723,560,770,603,723,700,713,640,736,631,670,701,592,647,722,706,611,695,717,706,804,630,723,678,705,642,646,644,797,670,760,663,611,711,629,645,606,664,667,649,659,708,752,735,641,674,678,723,736,655,712,701,640,692,728,647,760,705,714,603,700,661,707,727,639,644,683,578,688,670,676,746,606,654,711,707,690,621,722,608,707,671,720,622,703,744,665,616,676,786,562,578,679,600,710,682,692,567,735,674,664,714,592,594,666,613,653,644,739,596,668,684,675,610,682,732,754,643,703,689,623,649,755,662,607,691,628,589,609,592,631,664,717,640,598,713,622,660,711,645,606,664,700,643,563,718,655,694,613,686,782,660,646,653,674,578,673,610,702,718,688,632,667,631,615,675,631,600,564,708,725,662,655,715,671,654,753,629,684,726,639,696,636,679,631,638,618,725,652,640,665,633,754,633,651,657,588,742,686,673,665,720,615,636,646,624,729,679,645,703,590,658,752,707,584,629,606,658,628,622,679,661,646,644,660,600,698,628,687,747,673,678,731,743,660,755,700,697,739,652,726,621,641,611,704,695,653,710,659,690,669,649,713,652,690,731,681,712,625,675,608,673,652,645,728,652,706,760,645,671,615,697,702,643,684,719,643,608,672,715,649,648,683,778,627,627,654,792,604,728,698,696,798,672,604,691,550,614,628,717,569,714,624,751,691,666,707,699,736,685,640,657,645,676,656,656,588,644,718,664,735,729,709,699,640,677,679,718,618,592,620,780,722,687,700,760,584,747,714,646,671,677,659,615,724,693,721,812,701,718,689,729,616,627,659,742,677,672,622,632,689,635,662,675,704,738,694,694,627,629,712,592,696,739,653,594,691,620,664,699,653,649,655,707,657,767,699,611,741,644,735,749,724,656,737,698,622,659,654,661,756,674,666,609,680,659,592,698,595,656,650,677,780,700,697,707,656,627,714,702,676,595,695,705,634,657,737,701,677,667,711,606,719,722,564,649,629,628,641,592,620,666,722,738,666,691,748,687,565,694,645,630,740,645,584,698,802,732,708,696,750,637,607,682,698,584,710,685,748,695,643,743,687,599,607,754,715,763,702,707,689,688,638,655,714,695,761,668,654,676,680,604,672,648,619,726,724,649,702,619,629,645,592,646,746,675,611,646,659,663,635,645,715,566,727,700,640,647,593,649,691,695,640,683,679,688,697,561,663,599,672,682,691,664,668,659,705,766,685,623,716,678,782,703,642,662,665,680,632,653,625,743,638,626,577,649,627,564,603,699,606,697,633,654,746,587,692,618,760,770,649,631,644,657,684,645,670,753,648,698,680,625,673,667,657,714,654,810,720,643,702,698,582,670,662,591,717,623,604,563,646,666,740,637,708,640,725,732,662,694,711,740,649,688,700,694,711,667,635,704,675,713,724,723,747,672,726,742,627,669,627,655,545,680,582,683,660,698,583,698,595,626,669,607,650,637,697,681,635,701
TCV_PM,no_expansion,753.303,43.987966434,805,873,725,835,785,701,786,741,781,706,781,768,769,747,808,708,810,735,709,748,791,803,747,759,757,790,775,706,712,735,742,723,738,747,802,755,652,731,728,744,779,730,697,706,733,786,655,683,763,664,762,775,714,734,815,733,743,724,768,826,777,784,725,782,768,812,781,763,766,743,708,777,801,749,723,704,773,695,846,712,746,691,728,774,763,755,770,756,764,797,650,764,758,658,730,725,827,707,791,753,686,745,765,722,766,712,822,748,768,741,796,750,746,767,728,783,788,740,740,788,792,723,785,767,747,735,711,709,780,724,776,704,657,707,853,680,796,709,823,724,778,758,768,794,772,729,770,772,733,798,736,751,747,784,686,688,778,734,745,692,757,758,747,760,761,800,828,757,793,743,755,741,731,760,701,793,725,742,767,785,812,823,743,734,781,772,662,733,765,779,738,838,844,801,730,695,686,709,659,744,758,693,697,795,743,759,685,712,722,711,725,833,735,778,668,784,736,789,835,780,868,854,671,728,714,744,758,670,733,771,788,776,735,696,730,771,725,738,784,739,751,736,693,709,691,768,692,779,693,754,731,782,773,815,703,733,689,735,754,752,764,690,748,709,667,676,778,693,705,785,814,809,772,748,704,779,772,782,790,827,829,748,880,771,769,761,806,755,768,807,631,747,801,708,810,724,733,808,782,767,793,711,773,767,775,703,803,809,763,803,804,821,723,676,748,715,837,731,796,745,751,831,792,729,737,812,781,794,827,848,726,675,778,804,710,754,677,772,741,834,792,776,805,790,785,699,881,725,700,777,780,648,817,697,760,781,819,736,810,726,757,761,693,726,774,774,665,757,790,796,881,700,808,709,755,759,742,725,861,792,835,711,704,778,738,733,715,747,733,709,729,753,829,801,732,754,767,811,765,728,789,782,727,794,784,741,815,816,794,714,757,747,749,781,736,739,761,687,737,755,764,829,715,738,784,794,805,727,827,694,761,763,800,704,775,794,739,712,758,847,670,678,769,717,762,769,760,657,811,738,781,778,691,657,758,707,730,725,821,680,751,750,761,715,754,827,829,721,788,768,721,746,864,771,692,794,750,693,702,686,725,751,809,747,713,792,731,742,797,749,649,738,785,708,656,782,754,786,696,774,854,741,752,736,778,662,767,701,792,762,769,737,740,682,713,747,721,689,644,796,812,741,748,762,799,736,817,712,767,789,726,752,718,779,707,728,693,775,742,724,770,723,844,739,733,761,676,802,765,747,772,778,712,698,713,711,805,787,733,820,674,751,838,831,679,713,673,744,694,725,770,748,734,731,729,696,762,700,760,820,776,732,814,816,755,812,776,758,802,741,794,714,741,747,791,755,766,750,734,765,739,736,792,751,793,803,764,767,719,767,679,734,714,721,804,747,806,797,732,767,740,782,735,720,802,784,710,685,712,796,743,736,782,839,688,690,764,844,692,784,781,799,863,750,722,769,672,713,698,769,682,781,732,830,737,766,795,753,796,788,734,736,733,731,715,777,704,769,803,741,805,822,799,761,756,765,746,780,702,699,725,869,793,769,791,840,692,800,791,699,741,772,738,700,769,767,808,813,779,747,773,799,707,723,732,829,771,739,714,707,788,741,730,722,763,810,762,789,700,762,742,711,772,798,741,678,792,751,765,790,730,727,759,752,764,812,769,691,808,756,768,805,777,724,777,775,717,738,737,770,830,740,738,716,748,709,686,748,718,751,721,773,836,763,775,767,754,725,802,751,749,682,781,795,735,721,794,776,749,741,815,685,796,785,653,745,743,747,727,685,721,761,741,778,760,781,835,772,661,804,728,717,799,743,678,812,851,808,815,734,838,729,682,777,768,670,766,759,784,750,750,807,768,666,717,836,833,837,782,761,768,743,729,715,790,789,841,741,764,730,766,692,761,698,710,798,797,742,784,705,729,748,697,764,779,759,699,728,747,776,701,707,800,654,816,752,725,735,691,746,729,759,720,785,744,755,774,658,732,699,729,788,774,763,743,750,772,860,781,733,792,763,805,778,750,721,750,750,686,729,703,781,710,724,675,715,730,618,691,810,724,800,705,741,819,670,800,728,824,841,738,714,724,755,785,725,756,815,733,743,749,699,758,754,737,805,740,886,780,711,798,763,692,760,731,722,811,714,695,667,712,753,805,731,815,741,791,811,722,740,777,845,728,763,768,749,805,723,724,793,729,797,812,809,794,740,814,812,732,705,682,741,644,740,682,734,769,762,687,751,684,739,750,699,742,674,758,784,756,766
LONELINESS,no_expansion,0.80652608436,0.00547033568117,0.801339859243,0.797526165101,0.807352954555,0.803231396317,0.803272435549,0.804619215948,0.805483722851,0.815538104691,0.801811547157,0.809482524327,0.803526572126,0.806328938587,0.807947585875,0.812029440792,0.802231695334,0.81178483353,0.805595606387,0.795398842655,0.809893598088,0.808982415831,0.797135159239,0.800230758323,0.803394170978,0.802820820344,0.817725688318,0.807899218695,0.797192900432,0.804465487227,0.804318710504,0.811045673842,0.811850751458,0.807422689735,0.808158474518,0.811614178918,0.803585119598,0.802098524702,0.811812189661,0.803588766524,0.804826363535,0.807670834679,0.798662567068,0.801126287801,0.812447313843,0.81362503356,0.799301746272,0.809112072538,0.818507547347,0.812658895477,0.797673415777,0.812587225856,0.797950254144,0.801328782212,0.811269456135,0.80579187356,0.803127493935,0.805471159118,0.802515325443,0.809329686511,0.798935754597,0.804298594069,0.804267903014,0.806539726653,0.807172552908,0.805079352674,0.804117021911,0.800522550343,0.803207715661,0.795164157109,0.801831989214,0.806343835024,0.804521701776,0.798955174864,0.797082942425,0.809084493697,0.809479842366,0.805232183281,0.795644617458,0.814300199389,0.797880330673,0.806671970488,0.81022896677,0.818635005318,0.809406125854,0.801875595832,0.80108762997,0.796865498931,0.813310780225,0.803844383714,0.802653440724,0.7995416305,0.815989051527,0.805990939213,0.810215906821,0.812810952975,0.812315178117,0.816620654647,0.809838742871,0.80994360782,0.801789929603,0.806714905652,0.813707070111,0.806344584324,0.803501339218,0.818725332974,0.801202057863,0.8118103822,0.804103480492,0.808414986782,0.80843721845,0.817277958655,0.800890013934,0.80691981871,0.806362027705,0.814516390243,0.806755330456,0.807817018446,0.798910144196,0.807201927097,0.808939950829,0.810568641079,0.808969820185,0.814679253966,0.811685255254,0.799382728026,0.808714864422,0.816589936249,0.804882531796,0.816015821169,0.807311169585,0.807147029536,0.81420510194,0.814271408459,0.812616311056,0.813647286176,0.803840310013,0.811934520926,0.793690956076,0.816333511492,0.80705148619,0.808476987102,0.81123990594,0.814033021249,0.814379334539,0.801107536809,0.803727170711,0.813028509012,0.805156655925,0.798423630716,0.804117684192,0.796871095986,0.811749901292,0.813193655179,0.808659212833,0.802545709528,0.805269748296,0.811009696301,0.79658217582,0.808431309122,0.804613862948,0.815950310492,0.801941646437,0.80867847999,0.81156031915,0.800489586352,0.798251840892,0.796815782365,0.804967442167,0.802993628126,0.795956185348,0.80910162521,0.805771765038,0.80740184766,0.80323812951,0.808767252831,0.806241372688,0.807073586563,0.805217804809,0.798350167449,0.808731037702,0.802710637927,0.802219870967,0.80724414008,0.802750578322,0.807796871758,0.811349881333,0.800142622648,0.815730418181,0.809148726523,0.810351803606,0.804364319276,0.80893805298,0.800948995183,0.803262910394,0.81422931596,0.815551456425,0.812893847741,0.809852013049,0.801536975692,0.806188832389,0.81029134179,0.804067386343,0.813268409387,0.810586153836,0.804949254571,0.812259550649,0.798189181403,0.809972398993,0.817835218849,0.802163910482,0.811321655762,0.814546637741,0.797298302526,0.812706673999,0.801192314207,0.818012796528,0.809532333733,0.804436811609,0.799544367689,0.800343233247,0.810667593298,0.798912513902,0.794020496128,0.807324488178,0.808690870497,0.811500752034,0.805061236465,0.806247144374,0.808854841451,0.805848477172,0.79586276386,0.801155676211,0.807053625544,0.812732587565,0.815465187486,0.824277964377,0.800476366639,0.803117356816,0.816768308979,0.811017969648,0.814044843145,0.814010686727,0.807301755249,0.808079114698,0.807772255388,0.810234410051,0.807274946469,0.818767620936,0.804948253544,0.808717385344,0.807426486794,0.807089065382,0.806828586998,0.804565522585,0.792948555893,0.808827632308,0.804814543875,0.813370670879,0.811940220235,0.803581622158,0.808500793877,0.80464803835,0.810713619336,0.802748048648,0.816059165014,0.815855710344,0.81566512786,0.799942362518,0.815897746677,0.807540311258,0.811501550749,0.797369990565,0.805715825265,0.817496668452,0.808704696641,0.80044180033,0.80508283435,0.806136298149,0.807919583361,0.810071028763,0.804914907533,0.797939746815,0.806826056484,0.79497331171,0.796188943191,0.808349702704,0.807223438007,0.802362002504,0.804298215768,0.807031771764,0.801939896077,0.809973567361,0.806107655251,0.804401232539,0.812082424839,0.807861028858,0.812374071755,0.810823252395,0.80151095658,0.806965899254,0.805273498452,0.805809599175,0.803670631221,0.803930816661,0.806166572958,0.80584160077,0.813165134064,0.799475486492,0.803673192797,0.807919118285,0.801982083798,0.806427008763,0.811409901008,0.807622794055,0.810071052741,0.813741729579,0.803287977279,0.800124658212,0.799311246586,0.809472592935,0.81280855122,0.798916239917,0.800064690496,0.810393637394,0.81082642142,0.805244394831,0.799526468465,0.806153663892,0.803893481803,0.801441054933,0.80399369967,0.802916377037,0.809828199211,0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Mode avg_node_weight_complete
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Mode avg_node_weight_complete Iteration 900
Mode avg_node_weight_complete Iteration 950
EC_PM,avg_node_weight_complete,671.15,48.6617663058,718,781,599,768,700,591,679,709,654,589,705,688,675,636,718,644,715,673,623,664,694,724,648,634,696,691,709,641,592,631,645,649,642,641,730,677,576,642,625,673,724,643,596,644,623,734,560,625,654,587,667,687,638,662,733,664,635,653,720,754,714,660,697,728,697,709,704,661,703,662,633,676,753,638,647,609,699,615,762,635,699,648,685,659,661,639,694,698,683,687,560,687,672,604,633,639,760,646,689,680,605,668,689,618,662,621,733,656,693,658,686,657,647,677,617,689,691,673,642,688,744,651,693,650,693,666,615,639,686,629,711,631,594,643,761,592,699,605,732,634,698,685,712,702,654,633,700,683,628,728,642,693,634,694,613,631,698,633,682,605,669,713,655,665,675,670,746,682,707,661,696,687,693,682,609,712,623,642,687,682,771,752,633,633,673,698,567,664,694,703,643,791,767,779,673,625,622,608,580,675,659,606,610,741,671,676,613,613,661,616,650,767,615,722,591,699,627,672,741,735,785,757,568,667,657,647,668,573,639,692,716,708,689,594,673,677,594,678,740,684,650,665,637,627,601,665,597,695,611,687,659,669,672,719,632,643,594,641,677,647,670,596,648,640,592,625,688,615,653,704,711,733,704,662,605,687,651,698,706,719,726,655,822,698,692,701,693,670,678,724,562,654,697,642,719,668,630,709,686,643,754,618,662,664,723,626,737,705,678,726,750,752,670,564,680,615,754,601,732,673,657,728,753,672,667,735,684,718,725,714,617,596,714,710,593,680,608,667,650,751,709,667,723,713,675,621,801,646,626,690,723,560,770,603,723,700,713,640,736,631,670,701,592,647,722,706,611,695,717,706,804,630,723,678,705,642,646,644,797,670,760,663,611,711,629,645,606,664,667,649,659,708,752,735,641,674,678,723,736,655,712,701,640,692,728,647,760,705,714,603,700,661,707,727,639,644,683,578,688,670,676,746,606,654,711,707,690,621,722,608,707,671,720,622,703,744,665,616,676,786,562,578,679,600,710,682,692,567,735,674,664,714,592,594,666,613,653,644,739,596,668,684,675,610,682,732,754,643,703,689,623,649,755,662,607,691,628,589,609,592,631,664,717,640,598,713,622,660,711,645,606,664,700,643,563,718,655,694,613,686,782,660,646,653,674,578,673,610,702,718,688,632,667,631,615,675,631,600,564,708,725,662,655,715,671,654,753,629,684,726,639,696,636,679,631,638,618,725,652,640,665,633,754,633,651,657,588,742,686,673,665,720,615,636,646,624,729,679,645,703,590,658,752,707,584,629,606,658,628,622,679,661,646,644,660,600,698,628,687,747,673,678,731,743,660,755,700,697,739,652,726,621,641,611,704,695,653,710,659,690,669,649,713,652,690,731,681,712,625,675,608,673,652,645,728,652,706,760,645,671,615,697,702,643,684,719,643,608,672,715,649,648,683,778,627,627,654,792,604,728,698,696,798,672,604,691,550,614,628,717,569,714,624,751,691,666,707,699,736,685,640,657,645,676,656,656,588,644,718,664,735,729,709,699,640,677,679,718,618,592,620,780,722,687,700,760,584,747,714,646,671,677,659,615,724,693,721,812,701,718,689,729,616,627,659,742,677,672,622,632,689,635,662,675,704,738,694,694,627,629,712,592,696,739,653,594,691,620,664,699,653,649,655,707,657,767,699,611,741,644,735,749,724,656,737,698,622,659,654,661,756,674,666,609,680,659,592,698,595,656,650,677,780,700,697,707,656,627,714,702,676,595,695,705,634,657,737,701,677,667,711,606,719,722,564,649,629,628,641,592,620,666,722,738,666,691,748,687,565,694,645,630,740,645,584,698,802,732,708,696,750,637,607,682,698,584,710,685,748,695,643,743,687,599,607,754,715,763,702,707,689,688,638,655,714,695,761,668,654,676,680,604,672,648,619,726,724,649,702,619,629,645,592,646,746,675,611,646,659,663,635,645,715,566,727,700,640,647,593,649,691,695,640,683,679,688,697,561,663,599,672,682,691,664,668,659,705,766,685,623,716,678,782,703,642,662,665,680,632,653,625,743,638,626,577,649,627,564,603,699,606,697,633,654,746,587,692,618,760,770,649,631,644,657,684,645,670,753,648,698,680,625,673,667,657,714,654,810,720,643,702,698,582,670,662,591,717,623,604,563,646,666,740,637,708,640,725,732,662,694,711,740,649,688,700,694,711,667,635,704,675,713,724,723,747,672,726,742,627,669,627,655,545,680,582,683,660,698,583,698,595,626,669,607,650,637,697,681,635,701
TCV_PM,avg_node_weight_complete,753.303,43.987966434,805,873,725,835,785,701,786,741,781,706,781,768,769,747,808,708,810,735,709,748,791,803,747,759,757,790,775,706,712,735,742,723,738,747,802,755,652,731,728,744,779,730,697,706,733,786,655,683,763,664,762,775,714,734,815,733,743,724,768,826,777,784,725,782,768,812,781,763,766,743,708,777,801,749,723,704,773,695,846,712,746,691,728,774,763,755,770,756,764,797,650,764,758,658,730,725,827,707,791,753,686,745,765,722,766,712,822,748,768,741,796,750,746,767,728,783,788,740,740,788,792,723,785,767,747,735,711,709,780,724,776,704,657,707,853,680,796,709,823,724,778,758,768,794,772,729,770,772,733,798,736,751,747,784,686,688,778,734,745,692,757,758,747,760,761,800,828,757,793,743,755,741,731,760,701,793,725,742,767,785,812,823,743,734,781,772,662,733,765,779,738,838,844,801,730,695,686,709,659,744,758,693,697,795,743,759,685,712,722,711,725,833,735,778,668,784,736,789,835,780,868,854,671,728,714,744,758,670,733,771,788,776,735,696,730,771,725,738,784,739,751,736,693,709,691,768,692,779,693,754,731,782,773,815,703,733,689,735,754,752,764,690,748,709,667,676,778,693,705,785,814,809,772,748,704,779,772,782,790,827,829,748,880,771,769,761,806,755,768,807,631,747,801,708,810,724,733,808,782,767,793,711,773,767,775,703,803,809,763,803,804,821,723,676,748,715,837,731,796,745,751,831,792,729,737,812,781,794,827,848,726,675,778,804,710,754,677,772,741,834,792,776,805,790,785,699,881,725,700,777,780,648,817,697,760,781,819,736,810,726,757,761,693,726,774,774,665,757,790,796,881,700,808,709,755,759,742,725,861,792,835,711,704,778,738,733,715,747,733,709,729,753,829,801,732,754,767,811,765,728,789,782,727,794,784,741,815,816,794,714,757,747,749,781,736,739,761,687,737,755,764,829,715,738,784,794,805,727,827,694,761,763,800,704,775,794,739,712,758,847,670,678,769,717,762,769,760,657,811,738,781,778,691,657,758,707,730,725,821,680,751,750,761,715,754,827,829,721,788,768,721,746,864,771,692,794,750,693,702,686,725,751,809,747,713,792,731,742,797,749,649,738,785,708,656,782,754,786,696,774,854,741,752,736,778,662,767,701,792,762,769,737,740,682,713,747,721,689,644,796,812,741,748,762,799,736,817,712,767,789,726,752,718,779,707,728,693,775,742,724,770,723,844,739,733,761,676,802,765,747,772,778,712,698,713,711,805,787,733,820,674,751,838,831,679,713,673,744,694,725,770,748,734,731,729,696,762,700,760,820,776,732,814,816,755,812,776,758,802,741,794,714,741,747,791,755,766,750,734,765,739,736,792,751,793,803,764,767,719,767,679,734,714,721,804,747,806,797,732,767,740,782,735,720,802,784,710,685,712,796,743,736,782,839,688,690,764,844,692,784,781,799,863,750,722,769,672,713,698,769,682,781,732,830,737,766,795,753,796,788,734,736,733,731,715,777,704,769,803,741,805,822,799,761,756,765,746,780,702,699,725,869,793,769,791,840,692,800,791,699,741,772,738,700,769,767,808,813,779,747,773,799,707,723,732,829,771,739,714,707,788,741,730,722,763,810,762,789,700,762,742,711,772,798,741,678,792,751,765,790,730,727,759,752,764,812,769,691,808,756,768,805,777,724,777,775,717,738,737,770,830,740,738,716,748,709,686,748,718,751,721,773,836,763,775,767,754,725,802,751,749,682,781,795,735,721,794,776,749,741,815,685,796,785,653,745,743,747,727,685,721,761,741,778,760,781,835,772,661,804,728,717,799,743,678,812,851,808,815,734,838,729,682,777,768,670,766,759,784,750,750,807,768,666,717,836,833,837,782,761,768,743,729,715,790,789,841,741,764,730,766,692,761,698,710,798,797,742,784,705,729,748,697,764,779,759,699,728,747,776,701,707,800,654,816,752,725,735,691,746,729,759,720,785,744,755,774,658,732,699,729,788,774,763,743,750,772,860,781,733,792,763,805,778,750,721,750,750,686,729,703,781,710,724,675,715,730,618,691,810,724,800,705,741,819,670,800,728,824,841,738,714,724,755,785,725,756,815,733,743,749,699,758,754,737,805,740,886,780,711,798,763,692,760,731,722,811,714,695,667,712,753,805,731,815,741,791,811,722,740,777,845,728,763,768,749,805,723,724,793,729,797,812,809,794,740,814,812,732,705,682,741,644,740,682,734,769,762,687,751,684,739,750,699,742,674,758,784,756,766
LONELINESS,avg_node_weight_complete,0.80652608436,0.00547033568117,0.801339859243,0.797526165101,0.807352954555,0.803231396317,0.803272435549,0.804619215948,0.805483722851,0.815538104691,0.801811547157,0.809482524327,0.803526572126,0.806328938587,0.807947585875,0.812029440792,0.802231695334,0.81178483353,0.805595606387,0.795398842655,0.809893598088,0.808982415831,0.797135159239,0.800230758323,0.803394170978,0.802820820344,0.817725688318,0.807899218695,0.797192900432,0.804465487227,0.804318710504,0.811045673842,0.811850751458,0.807422689735,0.808158474518,0.811614178918,0.803585119598,0.802098524702,0.811812189661,0.803588766524,0.804826363535,0.807670834679,0.798662567068,0.801126287801,0.812447313843,0.81362503356,0.799301746272,0.809112072538,0.818507547347,0.812658895477,0.797673415777,0.812587225856,0.797950254144,0.801328782212,0.811269456135,0.80579187356,0.803127493935,0.805471159118,0.802515325443,0.809329686511,0.798935754597,0.804298594069,0.804267903014,0.806539726653,0.807172552908,0.805079352674,0.804117021911,0.800522550343,0.803207715661,0.795164157109,0.801831989214,0.806343835024,0.804521701776,0.798955174864,0.797082942425,0.809084493697,0.809479842366,0.805232183281,0.795644617458,0.814300199389,0.797880330673,0.806671970488,0.81022896677,0.818635005318,0.809406125854,0.801875595832,0.80108762997,0.796865498931,0.813310780225,0.803844383714,0.802653440724,0.7995416305,0.815989051527,0.805990939213,0.810215906821,0.812810952975,0.812315178117,0.816620654647,0.809838742871,0.80994360782,0.801789929603,0.806714905652,0.813707070111,0.806344584324,0.803501339218,0.818725332974,0.801202057863,0.8118103822,0.804103480492,0.808414986782,0.80843721845,0.817277958655,0.800890013934,0.80691981871,0.806362027705,0.814516390243,0.806755330456,0.807817018446,0.798910144196,0.807201927097,0.808939950829,0.810568641079,0.808969820185,0.814679253966,0.811685255254,0.799382728026,0.808714864422,0.816589936249,0.804882531796,0.816015821169,0.807311169585,0.807147029536,0.81420510194,0.814271408459,0.812616311056,0.813647286176,0.803840310013,0.811934520926,0.793690956076,0.816333511492,0.80705148619,0.808476987102,0.81123990594,0.814033021249,0.814379334539,0.801107536809,0.803727170711,0.813028509012,0.805156655925,0.798423630716,0.804117684192,0.796871095986,0.811749901292,0.813193655179,0.808659212833,0.802545709528,0.805269748296,0.811009696301,0.79658217582,0.808431309122,0.804613862948,0.815950310492,0.801941646437,0.80867847999,0.81156031915,0.800489586352,0.798251840892,0.796815782365,0.804967442167,0.802993628126,0.795956185348,0.80910162521,0.805771765038,0.80740184766,0.80323812951,0.808767252831,0.806241372688,0.807073586563,0.805217804809,0.798350167449,0.808731037702,0.802710637927,0.802219870967,0.80724414008,0.802750578322,0.807796871758,0.811349881333,0.800142622648,0.815730418181,0.809148726523,0.810351803606,0.804364319276,0.80893805298,0.800948995183,0.803262910394,0.81422931596,0.815551456425,0.812893847741,0.809852013049,0.801536975692,0.806188832389,0.81029134179,0.804067386343,0.813268409387,0.810586153836,0.804949254571,0.812259550649,0.798189181403,0.809972398993,0.817835218849,0.802163910482,0.811321655762,0.814546637741,0.797298302526,0.812706673999,0.801192314207,0.818012796528,0.809532333733,0.804436811609,0.799544367689,0.800343233247,0.810667593298,0.798912513902,0.794020496128,0.807324488178,0.808690870497,0.811500752034,0.805061236465,0.806247144374,0.808854841451,0.805848477172,0.79586276386,0.801155676211,0.807053625544,0.812732587565,0.815465187486,0.824277964377,0.800476366639,0.803117356816,0.816768308979,0.811017969648,0.814044843145,0.814010686727,0.807301755249,0.808079114698,0.807772255388,0.810234410051,0.807274946469,0.818767620936,0.804948253544,0.808717385344,0.807426486794,0.807089065382,0.806828586998,0.804565522585,0.792948555893,0.808827632308,0.804814543875,0.813370670879,0.811940220235,0.803581622158,0.808500793877,0.80464803835,0.810713619336,0.802748048648,0.816059165014,0.815855710344,0.81566512786,0.799942362518,0.815897746677,0.807540311258,0.811501550749,0.797369990565,0.805715825265,0.817496668452,0.808704696641,0.80044180033,0.80508283435,0.806136298149,0.807919583361,0.810071028763,0.804914907533,0.797939746815,0.806826056484,0.79497331171,0.796188943191,0.808349702704,0.807223438007,0.802362002504,0.804298215768,0.807031771764,0.801939896077,0.809973567361,0.806107655251,0.804401232539,0.812082424839,0.807861028858,0.812374071755,0.810823252395,0.80151095658,0.806965899254,0.805273498452,0.805809599175,0.803670631221,0.803930816661,0.806166572958,0.80584160077,0.813165134064,0.799475486492,0.803673192797,0.807919118285,0.801982083798,0.806427008763,0.811409901008,0.807622794055,0.810071052741,0.813741729579,0.803287977279,0.800124658212,0.799311246586,0.809472592935,0.81280855122,0.798916239917,0.800064690496,0.810393637394,0.81082642142,0.805244394831,0.799526468465,0.806153663892,0.803893481803,0.801441054933,0.80399369967,0.802916377037,0.80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Mode total_node_weight_complete
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Mode total_node_weight_complete Iteration 900
Mode total_node_weight_complete Iteration 950
EC_PM,total_node_weight_complete,671.15,48.6617663058,718,781,599,768,700,591,679,709,654,589,705,688,675,636,718,644,715,673,623,664,694,724,648,634,696,691,709,641,592,631,645,649,642,641,730,677,576,642,625,673,724,643,596,644,623,734,560,625,654,587,667,687,638,662,733,664,635,653,720,754,714,660,697,728,697,709,704,661,703,662,633,676,753,638,647,609,699,615,762,635,699,648,685,659,661,639,694,698,683,687,560,687,672,604,633,639,760,646,689,680,605,668,689,618,662,621,733,656,693,658,686,657,647,677,617,689,691,673,642,688,744,651,693,650,693,666,615,639,686,629,711,631,594,643,761,592,699,605,732,634,698,685,712,702,654,633,700,683,628,728,642,693,634,694,613,631,698,633,682,605,669,713,655,665,675,670,746,682,707,661,696,687,693,682,609,712,623,642,687,682,771,752,633,633,673,698,567,664,694,703,643,791,767,779,673,625,622,608,580,675,659,606,610,741,671,676,613,613,661,616,650,767,615,722,591,699,627,672,741,735,785,757,568,667,657,647,668,573,639,692,716,708,689,594,673,677,594,678,740,684,650,665,637,627,601,665,597,695,611,687,659,669,672,719,632,643,594,641,677,647,670,596,648,640,592,625,688,615,653,704,711,733,704,662,605,687,651,698,706,719,726,655,822,698,692,701,693,670,678,724,562,654,697,642,719,668,630,709,686,643,754,618,662,664,723,626,737,705,678,726,750,752,670,564,680,615,754,601,732,673,657,728,753,672,667,735,684,718,725,714,617,596,714,710,593,680,608,667,650,751,709,667,723,713,675,621,801,646,626,690,723,560,770,603,723,700,713,640,736,631,670,701,592,647,722,706,611,695,717,706,804,630,723,678,705,642,646,644,797,670,760,663,611,711,629,645,606,664,667,649,659,708,752,735,641,674,678,723,736,655,712,701,640,692,728,647,760,705,714,603,700,661,707,727,639,644,683,578,688,670,676,746,606,654,711,707,690,621,722,608,707,671,720,622,703,744,665,616,676,786,562,578,679,600,710,682,692,567,735,674,664,714,592,594,666,613,653,644,739,596,668,684,675,610,682,732,754,643,703,689,623,649,755,662,607,691,628,589,609,592,631,664,717,640,598,713,622,660,711,645,606,664,700,643,563,718,655,694,613,686,782,660,646,653,674,578,673,610,702,718,688,632,667,631,615,675,631,600,564,708,725,662,655,715,671,654,753,629,684,726,639,696,636,679,631,638,618,725,652,640,665,633,754,633,651,657,588,742,686,673,665,720,615,636,646,624,729,679,645,703,590,658,752,707,584,629,606,658,628,622,679,661,646,644,660,600,698,628,687,747,673,678,731,743,660,755,700,697,739,652,726,621,641,611,704,695,653,710,659,690,669,649,713,652,690,731,681,712,625,675,608,673,652,645,728,652,706,760,645,671,615,697,702,643,684,719,643,608,672,715,649,648,683,778,627,627,654,792,604,728,698,696,798,672,604,691,550,614,628,717,569,714,624,751,691,666,707,699,736,685,640,657,645,676,656,656,588,644,718,664,735,729,709,699,640,677,679,718,618,592,620,780,722,687,700,760,584,747,714,646,671,677,659,615,724,693,721,812,701,718,689,729,616,627,659,742,677,672,622,632,689,635,662,675,704,738,694,694,627,629,712,592,696,739,653,594,691,620,664,699,653,649,655,707,657,767,699,611,741,644,735,749,724,656,737,698,622,659,654,661,756,674,666,609,680,659,592,698,595,656,650,677,780,700,697,707,656,627,714,702,676,595,695,705,634,657,737,701,677,667,711,606,719,722,564,649,629,628,641,592,620,666,722,738,666,691,748,687,565,694,645,630,740,645,584,698,802,732,708,696,750,637,607,682,698,584,710,685,748,695,643,743,687,599,607,754,715,763,702,707,689,688,638,655,714,695,761,668,654,676,680,604,672,648,619,726,724,649,702,619,629,645,592,646,746,675,611,646,659,663,635,645,715,566,727,700,640,647,593,649,691,695,640,683,679,688,697,561,663,599,672,682,691,664,668,659,705,766,685,623,716,678,782,703,642,662,665,680,632,653,625,743,638,626,577,649,627,564,603,699,606,697,633,654,746,587,692,618,760,770,649,631,644,657,684,645,670,753,648,698,680,625,673,667,657,714,654,810,720,643,702,698,582,670,662,591,717,623,604,563,646,666,740,637,708,640,725,732,662,694,711,740,649,688,700,694,711,667,635,704,675,713,724,723,747,672,726,742,627,669,627,655,545,680,582,683,660,698,583,698,595,626,669,607,650,637,697,681,635,701
TCV_PM,total_node_weight_complete,753.303,43.987966434,805,873,725,835,785,701,786,741,781,706,781,768,769,747,808,708,810,735,709,748,791,803,747,759,757,790,775,706,712,735,742,723,738,747,802,755,652,731,728,744,779,730,697,706,733,786,655,683,763,664,762,775,714,734,815,733,743,724,768,826,777,784,725,782,768,812,781,763,766,743,708,777,801,749,723,704,773,695,846,712,746,691,728,774,763,755,770,756,764,797,650,764,758,658,730,725,827,707,791,753,686,745,765,722,766,712,822,748,768,741,796,750,746,767,728,783,788,740,740,788,792,723,785,767,747,735,711,709,780,724,776,704,657,707,853,680,796,709,823,724,778,758,768,794,772,729,770,772,733,798,736,751,747,784,686,688,778,734,745,692,757,758,747,760,761,800,828,757,793,743,755,741,731,760,701,793,725,742,767,785,812,823,743,734,781,772,662,733,765,779,738,838,844,801,730,695,686,709,659,744,758,693,697,795,743,759,685,712,722,711,725,833,735,778,668,784,736,789,835,780,868,854,671,728,714,744,758,670,733,771,788,776,735,696,730,771,725,738,784,739,751,736,693,709,691,768,692,779,693,754,731,782,773,815,703,733,689,735,754,752,764,690,748,709,667,676,778,693,705,785,814,809,772,748,704,779,772,782,790,827,829,748,880,771,769,761,806,755,768,807,631,747,801,708,810,724,733,808,782,767,793,711,773,767,775,703,803,809,763,803,804,821,723,676,748,715,837,731,796,745,751,831,792,729,737,812,781,794,827,848,726,675,778,804,710,754,677,772,741,834,792,776,805,790,785,699,881,725,700,777,780,648,817,697,760,781,819,736,810,726,757,761,693,726,774,774,665,757,790,796,881,700,808,709,755,759,742,725,861,792,835,711,704,778,738,733,715,747,733,709,729,753,829,801,732,754,767,811,765,728,789,782,727,794,784,741,815,816,794,714,757,747,749,781,736,739,761,687,737,755,764,829,715,738,784,794,805,727,827,694,761,763,800,704,775,794,739,712,758,847,670,678,769,717,762,769,760,657,811,738,781,778,691,657,758,707,730,725,821,680,751,750,761,715,754,827,829,721,788,768,721,746,864,771,692,794,750,693,702,686,725,751,809,747,713,792,731,742,797,749,649,738,785,708,656,782,754,786,696,774,854,741,752,736,778,662,767,701,792,762,769,737,740,682,713,747,721,689,644,796,812,741,748,762,799,736,817,712,767,789,726,752,718,779,707,728,693,775,742,724,770,723,844,739,733,761,676,802,765,747,772,778,712,698,713,711,805,787,733,820,674,751,838,831,679,713,673,744,694,725,770,748,734,731,729,696,762,700,760,820,776,732,814,816,755,812,776,758,802,741,794,714,741,747,791,755,766,750,734,765,739,736,792,751,793,803,764,767,719,767,679,734,714,721,804,747,806,797,732,767,740,782,735,720,802,784,710,685,712,796,743,736,782,839,688,690,764,844,692,784,781,799,863,750,722,769,672,713,698,769,682,781,732,830,737,766,795,753,796,788,734,736,733,731,715,777,704,769,803,741,805,822,799,761,756,765,746,780,702,699,725,869,793,769,791,840,692,800,791,699,741,772,738,700,769,767,808,813,779,747,773,799,707,723,732,829,771,739,714,707,788,741,730,722,763,810,762,789,700,762,742,711,772,798,741,678,792,751,765,790,730,727,759,752,764,812,769,691,808,756,768,805,777,724,777,775,717,738,737,770,830,740,738,716,748,709,686,748,718,751,721,773,836,763,775,767,754,725,802,751,749,682,781,795,735,721,794,776,749,741,815,685,796,785,653,745,743,747,727,685,721,761,741,778,760,781,835,772,661,804,728,717,799,743,678,812,851,808,815,734,838,729,682,777,768,670,766,759,784,750,750,807,768,666,717,836,833,837,782,761,768,743,729,715,790,789,841,741,764,730,766,692,761,698,710,798,797,742,784,705,729,748,697,764,779,759,699,728,747,776,701,707,800,654,816,752,725,735,691,746,729,759,720,785,744,755,774,658,732,699,729,788,774,763,743,750,772,860,781,733,792,763,805,778,750,721,750,750,686,729,703,781,710,724,675,715,730,618,691,810,724,800,705,741,819,670,800,728,824,841,738,714,724,755,785,725,756,815,733,743,749,699,758,754,737,805,740,886,780,711,798,763,692,760,731,722,811,714,695,667,712,753,805,731,815,741,791,811,722,740,777,845,728,763,768,749,805,723,724,793,729,797,812,809,794,740,814,812,732,705,682,741,644,740,682,734,769,762,687,751,684,739,750,699,742,674,758,784,756,766
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Mode smallest_node_weight_complete
Mode smallest_node_weight_complete Iteration 0
Mode smallest_node_weight_complete Iteration 50
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Mode smallest_node_weight_complete Iteration 900
Mode smallest_node_weight_complete Iteration 950
EC_PM,smallest_node_weight_complete,671.15,48.6617663058,718,781,599,768,700,591,679,709,654,589,705,688,675,636,718,644,715,673,623,664,694,724,648,634,696,691,709,641,592,631,645,649,642,641,730,677,576,642,625,673,724,643,596,644,623,734,560,625,654,587,667,687,638,662,733,664,635,653,720,754,714,660,697,728,697,709,704,661,703,662,633,676,753,638,647,609,699,615,762,635,699,648,685,659,661,639,694,698,683,687,560,687,672,604,633,639,760,646,689,680,605,668,689,618,662,621,733,656,693,658,686,657,647,677,617,689,691,673,642,688,744,651,693,650,693,666,615,639,686,629,711,631,594,643,761,592,699,605,732,634,698,685,712,702,654,633,700,683,628,728,642,693,634,694,613,631,698,633,682,605,669,713,655,665,675,670,746,682,707,661,696,687,693,682,609,712,623,642,687,682,771,752,633,633,673,698,567,664,694,703,643,791,767,779,673,625,622,608,580,675,659,606,610,741,671,676,613,613,661,616,650,767,615,722,591,699,627,672,741,735,785,757,568,667,657,647,668,573,639,692,716,708,689,594,673,677,594,678,740,684,650,665,637,627,601,665,597,695,611,687,659,669,672,719,632,643,594,641,677,647,670,596,648,640,592,625,688,615,653,704,711,733,704,662,605,687,651,698,706,719,726,655,822,698,692,701,693,670,678,724,562,654,697,642,719,668,630,709,686,643,754,618,662,664,723,626,737,705,678,726,750,752,670,564,680,615,754,601,732,673,657,728,753,672,667,735,684,718,725,714,617,596,714,710,593,680,608,667,650,751,709,667,723,713,675,621,801,646,626,690,723,560,770,603,723,700,713,640,736,631,670,701,592,647,722,706,611,695,717,706,804,630,723,678,705,642,646,644,797,670,760,663,611,711,629,645,606,664,667,649,659,708,752,735,641,674,678,723,736,655,712,701,640,692,728,647,760,705,714,603,700,661,707,727,639,644,683,578,688,670,676,746,606,654,711,707,690,621,722,608,707,671,720,622,703,744,665,616,676,786,562,578,679,600,710,682,692,567,735,674,664,714,592,594,666,613,653,644,739,596,668,684,675,610,682,732,754,643,703,689,623,649,755,662,607,691,628,589,609,592,631,664,717,640,598,713,622,660,711,645,606,664,700,643,563,718,655,694,613,686,782,660,646,653,674,578,673,610,702,718,688,632,667,631,615,675,631,600,564,708,725,662,655,715,671,654,753,629,684,726,639,696,636,679,631,638,618,725,652,640,665,633,754,633,651,657,588,742,686,673,665,720,615,636,646,624,729,679,645,703,590,658,752,707,584,629,606,658,628,622,679,661,646,644,660,600,698,628,687,747,673,678,731,743,660,755,700,697,739,652,726,621,641,611,704,695,653,710,659,690,669,649,713,652,690,731,681,712,625,675,608,673,652,645,728,652,706,760,645,671,615,697,702,643,684,719,643,608,672,715,649,648,683,778,627,627,654,792,604,728,698,696,798,672,604,691,550,614,628,717,569,714,624,751,691,666,707,699,736,685,640,657,645,676,656,656,588,644,718,664,735,729,709,699,640,677,679,718,618,592,620,780,722,687,700,760,584,747,714,646,671,677,659,615,724,693,721,812,701,718,689,729,616,627,659,742,677,672,622,632,689,635,662,675,704,738,694,694,627,629,712,592,696,739,653,594,691,620,664,699,653,649,655,707,657,767,699,611,741,644,735,749,724,656,737,698,622,659,654,661,756,674,666,609,680,659,592,698,595,656,650,677,780,700,697,707,656,627,714,702,676,595,695,705,634,657,737,701,677,667,711,606,719,722,564,649,629,628,641,592,620,666,722,738,666,691,748,687,565,694,645,630,740,645,584,698,802,732,708,696,750,637,607,682,698,584,710,685,748,695,643,743,687,599,607,754,715,763,702,707,689,688,638,655,714,695,761,668,654,676,680,604,672,648,619,726,724,649,702,619,629,645,592,646,746,675,611,646,659,663,635,645,715,566,727,700,640,647,593,649,691,695,640,683,679,688,697,561,663,599,672,682,691,664,668,659,705,766,685,623,716,678,782,703,642,662,665,680,632,653,625,743,638,626,577,649,627,564,603,699,606,697,633,654,746,587,692,618,760,770,649,631,644,657,684,645,670,753,648,698,680,625,673,667,657,714,654,810,720,643,702,698,582,670,662,591,717,623,604,563,646,666,740,637,708,640,725,732,662,694,711,740,649,688,700,694,711,667,635,704,675,713,724,723,747,672,726,742,627,669,627,655,545,680,582,683,660,698,583,698,595,626,669,607,650,637,697,681,635,701
TCV_PM,smallest_node_weight_complete,753.303,43.987966434,805,873,725,835,785,701,786,741,781,706,781,768,769,747,808,708,810,735,709,748,791,803,747,759,757,790,775,706,712,735,742,723,738,747,802,755,652,731,728,744,779,730,697,706,733,786,655,683,763,664,762,775,714,734,815,733,743,724,768,826,777,784,725,782,768,812,781,763,766,743,708,777,801,749,723,704,773,695,846,712,746,691,728,774,763,755,770,756,764,797,650,764,758,658,730,725,827,707,791,753,686,745,765,722,766,712,822,748,768,741,796,750,746,767,728,783,788,740,740,788,792,723,785,767,747,735,711,709,780,724,776,704,657,707,853,680,796,709,823,724,778,758,768,794,772,729,770,772,733,798,736,751,747,784,686,688,778,734,745,692,757,758,747,760,761,800,828,757,793,743,755,741,731,760,701,793,725,742,767,785,812,823,743,734,781,772,662,733,765,779,738,838,844,801,730,695,686,709,659,744,758,693,697,795,743,759,685,712,722,711,725,833,735,778,668,784,736,789,835,780,868,854,671,728,714,744,758,670,733,771,788,776,735,696,730,771,725,738,784,739,751,736,693,709,691,768,692,779,693,754,731,782,773,815,703,733,689,735,754,752,764,690,748,709,667,676,778,693,705,785,814,809,772,748,704,779,772,782,790,827,829,748,880,771,769,761,806,755,768,807,631,747,801,708,810,724,733,808,782,767,793,711,773,767,775,703,803,809,763,803,804,821,723,676,748,715,837,731,796,745,751,831,792,729,737,812,781,794,827,848,726,675,778,804,710,754,677,772,741,834,792,776,805,790,785,699,881,725,700,777,780,648,817,697,760,781,819,736,810,726,757,761,693,726,774,774,665,757,790,796,881,700,808,709,755,759,742,725,861,792,835,711,704,778,738,733,715,747,733,709,729,753,829,801,732,754,767,811,765,728,789,782,727,794,784,741,815,816,794,714,757,747,749,781,736,739,761,687,737,755,764,829,715,738,784,794,805,727,827,694,761,763,800,704,775,794,739,712,758,847,670,678,769,717,762,769,760,657,811,738,781,778,691,657,758,707,730,725,821,680,751,750,761,715,754,827,829,721,788,768,721,746,864,771,692,794,750,693,702,686,725,751,809,747,713,792,731,742,797,749,649,738,785,708,656,782,754,786,696,774,854,741,752,736,778,662,767,701,792,762,769,737,740,682,713,747,721,689,644,796,812,741,748,762,799,736,817,712,767,789,726,752,718,779,707,728,693,775,742,724,770,723,844,739,733,761,676,802,765,747,772,778,712,698,713,711,805,787,733,820,674,751,838,831,679,713,673,744,694,725,770,748,734,731,729,696,762,700,760,820,776,732,814,816,755,812,776,758,802,741,794,714,741,747,791,755,766,750,734,765,739,736,792,751,793,803,764,767,719,767,679,734,714,721,804,747,806,797,732,767,740,782,735,720,802,784,710,685,712,796,743,736,782,839,688,690,764,844,692,784,781,799,863,750,722,769,672,713,698,769,682,781,732,830,737,766,795,753,796,788,734,736,733,731,715,777,704,769,803,741,805,822,799,761,756,765,746,780,702,699,725,869,793,769,791,840,692,800,791,699,741,772,738,700,769,767,808,813,779,747,773,799,707,723,732,829,771,739,714,707,788,741,730,722,763,810,762,789,700,762,742,711,772,798,741,678,792,751,765,790,730,727,759,752,764,812,769,691,808,756,768,805,777,724,777,775,717,738,737,770,830,740,738,716,748,709,686,748,718,751,721,773,836,763,775,767,754,725,802,751,749,682,781,795,735,721,794,776,749,741,815,685,796,785,653,745,743,747,727,685,721,761,741,778,760,781,835,772,661,804,728,717,799,743,678,812,851,808,815,734,838,729,682,777,768,670,766,759,784,750,750,807,768,666,717,836,833,837,782,761,768,743,729,715,790,789,841,741,764,730,766,692,761,698,710,798,797,742,784,705,729,748,697,764,779,759,699,728,747,776,701,707,800,654,816,752,725,735,691,746,729,759,720,785,744,755,774,658,732,699,729,788,774,763,743,750,772,860,781,733,792,763,805,778,750,721,750,750,686,729,703,781,710,724,675,715,730,618,691,810,724,800,705,741,819,670,800,728,824,841,738,714,724,755,785,725,756,815,733,743,749,699,758,754,737,805,740,886,780,711,798,763,692,760,731,722,811,714,695,667,712,753,805,731,815,741,791,811,722,740,777,845,728,763,768,749,805,723,724,793,729,797,812,809,794,740,814,812,732,705,682,741,644,740,682,734,769,762,687,751,684,739,750,699,742,674,758,784,756,766
LONELINESS,smallest_node_weight_complete,0.80652608436,0.00547033568117,0.801339859243,0.797526165101,0.807352954555,0.803231396317,0.803272435549,0.804619215948,0.805483722851,0.815538104691,0.801811547157,0.809482524327,0.803526572126,0.806328938587,0.807947585875,0.812029440792,0.802231695334,0.81178483353,0.805595606387,0.795398842655,0.809893598088,0.808982415831,0.797135159239,0.800230758323,0.803394170978,0.802820820344,0.817725688318,0.807899218695,0.797192900432,0.804465487227,0.804318710504,0.811045673842,0.811850751458,0.807422689735,0.808158474518,0.811614178918,0.803585119598,0.802098524702,0.811812189661,0.803588766524,0.804826363535,0.807670834679,0.798662567068,0.801126287801,0.812447313843,0.81362503356,0.799301746272,0.809112072538,0.818507547347,0.812658895477,0.797673415777,0.812587225856,0.797950254144,0.801328782212,0.811269456135,0.80579187356,0.803127493935,0.805471159118,0.802515325443,0.809329686511,0.798935754597,0.804298594069,0.804267903014,0.806539726653,0.807172552908,0.805079352674,0.804117021911,0.800522550343,0.803207715661,0.795164157109,0.801831989214,0.806343835024,0.804521701776,0.798955174864,0.797082942425,0.809084493697,0.809479842366,0.805232183281,0.795644617458,0.814300199389,0.797880330673,0.806671970488,0.81022896677,0.818635005318,0.809406125854,0.801875595832,0.80108762997,0.796865498931,0.813310780225,0.803844383714,0.802653440724,0.7995416305,0.815989051527,0.805990939213,0.810215906821,0.812810952975,0.812315178117,0.816620654647,0.809838742871,0.80994360782,0.801789929603,0.806714905652,0.813707070111,0.806344584324,0.803501339218,0.818725332974,0.801202057863,0.8118103822,0.804103480492,0.808414986782,0.80843721845,0.817277958655,0.800890013934,0.80691981871,0.806362027705,0.814516390243,0.806755330456,0.807817018446,0.798910144196,0.807201927097,0.808939950829,0.810568641079,0.808969820185,0.814679253966,0.811685255254,0.799382728026,0.808714864422,0.816589936249,0.804882531796,0.816015821169,0.807311169585,0.807147029536,0.81420510194,0.814271408459,0.812616311056,0.813647286176,0.803840310013,0.811934520926,0.793690956076,0.816333511492,0.80705148619,0.808476987102,0.81123990594,0.814033021249,0.814379334539,0.801107536809,0.803727170711,0.813028509012,0.805156655925,0.798423630716,0.804117684192,0.796871095986,0.811749901292,0.813193655179,0.808659212833,0.802545709528,0.805269748296,0.811009696301,0.79658217582,0.808431309122,0.804613862948,0.815950310492,0.801941646437,0.80867847999,0.81156031915,0.800489586352,0.798251840892,0.796815782365,0.804967442167,0.802993628126,0.795956185348,0.80910162521,0.805771765038,0.80740184766,0.80323812951,0.808767252831,0.806241372688,0.807073586563,0.805217804809,0.798350167449,0.808731037702,0.802710637927,0.802219870967,0.80724414008,0.802750578322,0.807796871758,0.811349881333,0.800142622648,0.815730418181,0.809148726523,0.810351803606,0.804364319276,0.80893805298,0.800948995183,0.803262910394,0.81422931596,0.815551456425,0.812893847741,0.809852013049,0.801536975692,0.806188832389,0.81029134179,0.804067386343,0.813268409387,0.810586153836,0.804949254571,0.812259550649,0.798189181403,0.809972398993,0.817835218849,0.802163910482,0.811321655762,0.814546637741,0.797298302526,0.812706673999,0.801192314207,0.818012796528,0.809532333733,0.804436811609,0.799544367689,0.800343233247,0.810667593298,0.798912513902,0.794020496128,0.807324488178,0.808690870497,0.811500752034,0.805061236465,0.806247144374,0.808854841451,0.805848477172,0.79586276386,0.801155676211,0.807053625544,0.812732587565,0.815465187486,0.824277964377,0.800476366639,0.803117356816,0.816768308979,0.811017969648,0.814044843145,0.814010686727,0.807301755249,0.808079114698,0.807772255388,0.810234410051,0.807274946469,0.818767620936,0.804948253544,0.808717385344,0.807426486794,0.807089065382,0.806828586998,0.804565522585,0.792948555893,0.808827632308,0.804814543875,0.813370670879,0.811940220235,0.803581622158,0.808500793877,0.80464803835,0.810713619336,0.802748048648,0.816059165014,0.815855710344,0.81566512786,0.799942362518,0.815897746677,0.807540311258,0.811501550749,0.797369990565,0.805715825265,0.817496668452,0.808704696641,0.80044180033,0.80508283435,0.806136298149,0.807919583361,0.810071028763,0.804914907533,0.797939746815,0.806826056484,0.79497331171,0.796188943191,0.808349702704,0.807223438007,0.802362002504,0.804298215768,0.807031771764,0.801939896077,0.809973567361,0.806107655251,0.804401232539,0.812082424839,0.807861028858,0.812374071755,0.810823252395,0.80151095658,0.806965899254,0.805273498452,0.805809599175,0.803670631221,0.803930816661,0.806166572958,0.80584160077,0.813165134064,0.799475486492,0.803673192797,0.807919118285,0.801982083798,0.806427008763,0.811409901008,0.807622794055,0.810071052741,0.813741729579,0.803287977279,0.800124658212,0.799311246586,0.809472592935,0.81280855122,0.798916239917,0.800064690496,0.810393637394,0.81082642142,0.805244394831,0.799526468465,0.806153663892,0.803893481803,0.801441054933,0.80399369967,0.802916377037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Mode largest_node_weight_complete
Mode largest_node_weight_complete Iteration 0
Mode largest_node_weight_complete Iteration 50
Mode largest_node_weight_complete Iteration 100
Mode largest_node_weight_complete Iteration 150
Mode largest_node_weight_complete Iteration 200
Mode largest_node_weight_complete Iteration 250
Mode largest_node_weight_complete Iteration 300
Mode largest_node_weight_complete Iteration 350
Mode largest_node_weight_complete Iteration 400
Mode largest_node_weight_complete Iteration 450
Mode largest_node_weight_complete Iteration 500
Mode largest_node_weight_complete Iteration 550
Mode largest_node_weight_complete Iteration 600
Mode largest_node_weight_complete Iteration 650
Mode largest_node_weight_complete Iteration 700
Mode largest_node_weight_complete Iteration 750
Mode largest_node_weight_complete Iteration 800
Mode largest_node_weight_complete Iteration 850
Mode largest_node_weight_complete Iteration 900
Mode largest_node_weight_complete Iteration 950
EC_PM,largest_node_weight_complete,671.15,48.6617663058,718,781,599,768,700,591,679,709,654,589,705,688,675,636,718,644,715,673,623,664,694,724,648,634,696,691,709,641,592,631,645,649,642,641,730,677,576,642,625,673,724,643,596,644,623,734,560,625,654,587,667,687,638,662,733,664,635,653,720,754,714,660,697,728,697,709,704,661,703,662,633,676,753,638,647,609,699,615,762,635,699,648,685,659,661,639,694,698,683,687,560,687,672,604,633,639,760,646,689,680,605,668,689,618,662,621,733,656,693,658,686,657,647,677,617,689,691,673,642,688,744,651,693,650,693,666,615,639,686,629,711,631,594,643,761,592,699,605,732,634,698,685,712,702,654,633,700,683,628,728,642,693,634,694,613,631,698,633,682,605,669,713,655,665,675,670,746,682,707,661,696,687,693,682,609,712,623,642,687,682,771,752,633,633,673,698,567,664,694,703,643,791,767,779,673,625,622,608,580,675,659,606,610,741,671,676,613,613,661,616,650,767,615,722,591,699,627,672,741,735,785,757,568,667,657,647,668,573,639,692,716,708,689,594,673,677,594,678,740,684,650,665,637,627,601,665,597,695,611,687,659,669,672,719,632,643,594,641,677,647,670,596,648,640,592,625,688,615,653,704,711,733,704,662,605,687,651,698,706,719,726,655,822,698,692,701,693,670,678,724,562,654,697,642,719,668,630,709,686,643,754,618,662,664,723,626,737,705,678,726,750,752,670,564,680,615,754,601,732,673,657,728,753,672,667,735,684,718,725,714,617,596,714,710,593,680,608,667,650,751,709,667,723,713,675,621,801,646,626,690,723,560,770,603,723,700,713,640,736,631,670,701,592,647,722,706,611,695,717,706,804,630,723,678,705,642,646,644,797,670,760,663,611,711,629,645,606,664,667,649,659,708,752,735,641,674,678,723,736,655,712,701,640,692,728,647,760,705,714,603,700,661,707,727,639,644,683,578,688,670,676,746,606,654,711,707,690,621,722,608,707,671,720,622,703,744,665,616,676,786,562,578,679,600,710,682,692,567,735,674,664,714,592,594,666,613,653,644,739,596,668,684,675,610,682,732,754,643,703,689,623,649,755,662,607,691,628,589,609,592,631,664,717,640,598,713,622,660,711,645,606,664,700,643,563,718,655,694,613,686,782,660,646,653,674,578,673,610,702,718,688,632,667,631,615,675,631,600,564,708,725,662,655,715,671,654,753,629,684,726,639,696,636,679,631,638,618,725,652,640,665,633,754,633,651,657,588,742,686,673,665,720,615,636,646,624,729,679,645,703,590,658,752,707,584,629,606,658,628,622,679,661,646,644,660,600,698,628,687,747,673,678,731,743,660,755,700,697,739,652,726,621,641,611,704,695,653,710,659,690,669,649,713,652,690,731,681,712,625,675,608,673,652,645,728,652,706,760,645,671,615,697,702,643,684,719,643,608,672,715,649,648,683,778,627,627,654,792,604,728,698,696,798,672,604,691,550,614,628,717,569,714,624,751,691,666,707,699,736,685,640,657,645,676,656,656,588,644,718,664,735,729,709,699,640,677,679,718,618,592,620,780,722,687,700,760,584,747,714,646,671,677,659,615,724,693,721,812,701,718,689,729,616,627,659,742,677,672,622,632,689,635,662,675,704,738,694,694,627,629,712,592,696,739,653,594,691,620,664,699,653,649,655,707,657,767,699,611,741,644,735,749,724,656,737,698,622,659,654,661,756,674,666,609,680,659,592,698,595,656,650,677,780,700,697,707,656,627,714,702,676,595,695,705,634,657,737,701,677,667,711,606,719,722,564,649,629,628,641,592,620,666,722,738,666,691,748,687,565,694,645,630,740,645,584,698,802,732,708,696,750,637,607,682,698,584,710,685,748,695,643,743,687,599,607,754,715,763,702,707,689,688,638,655,714,695,761,668,654,676,680,604,672,648,619,726,724,649,702,619,629,645,592,646,746,675,611,646,659,663,635,645,715,566,727,700,640,647,593,649,691,695,640,683,679,688,697,561,663,599,672,682,691,664,668,659,705,766,685,623,716,678,782,703,642,662,665,680,632,653,625,743,638,626,577,649,627,564,603,699,606,697,633,654,746,587,692,618,760,770,649,631,644,657,684,645,670,753,648,698,680,625,673,667,657,714,654,810,720,643,702,698,582,670,662,591,717,623,604,563,646,666,740,637,708,640,725,732,662,694,711,740,649,688,700,694,711,667,635,704,675,713,724,723,747,672,726,742,627,669,627,655,545,680,582,683,660,698,583,698,595,626,669,607,650,637,697,681,635,701
TCV_PM,largest_node_weight_complete,753.303,43.987966434,805,873,725,835,785,701,786,741,781,706,781,768,769,747,808,708,810,735,709,748,791,803,747,759,757,790,775,706,712,735,742,723,738,747,802,755,652,731,728,744,779,730,697,706,733,786,655,683,763,664,762,775,714,734,815,733,743,724,768,826,777,784,725,782,768,812,781,763,766,743,708,777,801,749,723,704,773,695,846,712,746,691,728,774,763,755,770,756,764,797,650,764,758,658,730,725,827,707,791,753,686,745,765,722,766,712,822,748,768,741,796,750,746,767,728,783,788,740,740,788,792,723,785,767,747,735,711,709,780,724,776,704,657,707,853,680,796,709,823,724,778,758,768,794,772,729,770,772,733,798,736,751,747,784,686,688,778,734,745,692,757,758,747,760,761,800,828,757,793,743,755,741,731,760,701,793,725,742,767,785,812,823,743,734,781,772,662,733,765,779,738,838,844,801,730,695,686,709,659,744,758,693,697,795,743,759,685,712,722,711,725,833,735,778,668,784,736,789,835,780,868,854,671,728,714,744,758,670,733,771,788,776,735,696,730,771,725,738,784,739,751,736,693,709,691,768,692,779,693,754,731,782,773,815,703,733,689,735,754,752,764,690,748,709,667,676,778,693,705,785,814,809,772,748,704,779,772,782,790,827,829,748,880,771,769,761,806,755,768,807,631,747,801,708,810,724,733,808,782,767,793,711,773,767,775,703,803,809,763,803,804,821,723,676,748,715,837,731,796,745,751,831,792,729,737,812,781,794,827,848,726,675,778,804,710,754,677,772,741,834,792,776,805,790,785,699,881,725,700,777,780,648,817,697,760,781,819,736,810,726,757,761,693,726,774,774,665,757,790,796,881,700,808,709,755,759,742,725,861,792,835,711,704,778,738,733,715,747,733,709,729,753,829,801,732,754,767,811,765,728,789,782,727,794,784,741,815,816,794,714,757,747,749,781,736,739,761,687,737,755,764,829,715,738,784,794,805,727,827,694,761,763,800,704,775,794,739,712,758,847,670,678,769,717,762,769,760,657,811,738,781,778,691,657,758,707,730,725,821,680,751,750,761,715,754,827,829,721,788,768,721,746,864,771,692,794,750,693,702,686,725,751,809,747,713,792,731,742,797,749,649,738,785,708,656,782,754,786,696,774,854,741,752,736,778,662,767,701,792,762,769,737,740,682,713,747,721,689,644,796,812,741,748,762,799,736,817,712,767,789,726,752,718,779,707,728,693,775,742,724,770,723,844,739,733,761,676,802,765,747,772,778,712,698,713,711,805,787,733,820,674,751,838,831,679,713,673,744,694,725,770,748,734,731,729,696,762,700,760,820,776,732,814,816,755,812,776,758,802,741,794,714,741,747,791,755,766,750,734,765,739,736,792,751,793,803,764,767,719,767,679,734,714,721,804,747,806,797,732,767,740,782,735,720,802,784,710,685,712,796,743,736,782,839,688,690,764,844,692,784,781,799,863,750,722,769,672,713,698,769,682,781,732,830,737,766,795,753,796,788,734,736,733,731,715,777,704,769,803,741,805,822,799,761,756,765,746,780,702,699,725,869,793,769,791,840,692,800,791,699,741,772,738,700,769,767,808,813,779,747,773,799,707,723,732,829,771,739,714,707,788,741,730,722,763,810,762,789,700,762,742,711,772,798,741,678,792,751,765,790,730,727,759,752,764,812,769,691,808,756,768,805,777,724,777,775,717,738,737,770,830,740,738,716,748,709,686,748,718,751,721,773,836,763,775,767,754,725,802,751,749,682,781,795,735,721,794,776,749,741,815,685,796,785,653,745,743,747,727,685,721,761,741,778,760,781,835,772,661,804,728,717,799,743,678,812,851,808,815,734,838,729,682,777,768,670,766,759,784,750,750,807,768,666,717,836,833,837,782,761,768,743,729,715,790,789,841,741,764,730,766,692,761,698,710,798,797,742,784,705,729,748,697,764,779,759,699,728,747,776,701,707,800,654,816,752,725,735,691,746,729,759,720,785,744,755,774,658,732,699,729,788,774,763,743,750,772,860,781,733,792,763,805,778,750,721,750,750,686,729,703,781,710,724,675,715,730,618,691,810,724,800,705,741,819,670,800,728,824,841,738,714,724,755,785,725,756,815,733,743,749,699,758,754,737,805,740,886,780,711,798,763,692,760,731,722,811,714,695,667,712,753,805,731,815,741,791,811,722,740,777,845,728,763,768,749,805,723,724,793,729,797,812,809,794,740,814,812,732,705,682,741,644,740,682,734,769,762,687,751,684,739,750,699,742,674,758,784,756,766
LONELINESS,largest_node_weight_complete,0.80652608436,0.00547033568117,0.801339859243,0.797526165101,0.807352954555,0.803231396317,0.803272435549,0.804619215948,0.805483722851,0.815538104691,0.801811547157,0.809482524327,0.803526572126,0.806328938587,0.807947585875,0.812029440792,0.802231695334,0.81178483353,0.805595606387,0.795398842655,0.809893598088,0.808982415831,0.797135159239,0.800230758323,0.803394170978,0.802820820344,0.817725688318,0.807899218695,0.797192900432,0.804465487227,0.804318710504,0.811045673842,0.811850751458,0.807422689735,0.808158474518,0.811614178918,0.803585119598,0.802098524702,0.811812189661,0.803588766524,0.804826363535,0.807670834679,0.798662567068,0.801126287801,0.812447313843,0.81362503356,0.799301746272,0.809112072538,0.818507547347,0.812658895477,0.797673415777,0.812587225856,0.797950254144,0.801328782212,0.811269456135,0.80579187356,0.803127493935,0.805471159118,0.802515325443,0.809329686511,0.798935754597,0.804298594069,0.804267903014,0.806539726653,0.807172552908,0.805079352674,0.804117021911,0.800522550343,0.803207715661,0.795164157109,0.801831989214,0.806343835024,0.804521701776,0.798955174864,0.797082942425,0.809084493697,0.809479842366,0.805232183281,0.795644617458,0.814300199389,0.797880330673,0.806671970488,0.81022896677,0.818635005318,0.809406125854,0.801875595832,0.80108762997,0.796865498931,0.813310780225,0.803844383714,0.802653440724,0.7995416305,0.815989051527,0.805990939213,0.810215906821,0.812810952975,0.812315178117,0.816620654647,0.809838742871,0.80994360782,0.801789929603,0.806714905652,0.813707070111,0.806344584324,0.803501339218,0.818725332974,0.801202057863,0.8118103822,0.804103480492,0.808414986782,0.80843721845,0.817277958655,0.800890013934,0.80691981871,0.806362027705,0.814516390243,0.806755330456,0.807817018446,0.798910144196,0.807201927097,0.808939950829,0.810568641079,0.808969820185,0.814679253966,0.811685255254,0.799382728026,0.808714864422,0.816589936249,0.804882531796,0.816015821169,0.807311169585,0.807147029536,0.81420510194,0.814271408459,0.812616311056,0.813647286176,0.803840310013,0.811934520926,0.793690956076,0.816333511492,0.80705148619,0.808476987102,0.81123990594,0.814033021249,0.814379334539,0.801107536809,0.803727170711,0.813028509012,0.805156655925,0.798423630716,0.804117684192,0.796871095986,0.811749901292,0.813193655179,0.808659212833,0.802545709528,0.805269748296,0.811009696301,0.79658217582,0.808431309122,0.804613862948,0.815950310492,0.801941646437,0.80867847999,0.81156031915,0.800489586352,0.798251840892,0.796815782365,0.804967442167,0.802993628126,0.795956185348,0.80910162521,0.805771765038,0.80740184766,0.80323812951,0.808767252831,0.806241372688,0.807073586563,0.805217804809,0.798350167449,0.808731037702,0.802710637927,0.802219870967,0.80724414008,0.802750578322,0.807796871758,0.811349881333,0.800142622648,0.815730418181,0.809148726523,0.810351803606,0.804364319276,0.80893805298,0.800948995183,0.803262910394,0.81422931596,0.815551456425,0.812893847741,0.809852013049,0.801536975692,0.806188832389,0.81029134179,0.804067386343,0.813268409387,0.810586153836,0.804949254571,0.812259550649,0.798189181403,0.809972398993,0.817835218849,0.802163910482,0.811321655762,0.814546637741,0.797298302526,0.812706673999,0.801192314207,0.818012796528,0.809532333733,0.804436811609,0.799544367689,0.800343233247,0.810667593298,0.798912513902,0.794020496128,0.807324488178,0.808690870497,0.811500752034,0.805061236465,0.806247144374,0.808854841451,0.805848477172,0.79586276386,0.801155676211,0.807053625544,0.812732587565,0.815465187486,0.824277964377,0.800476366639,0.803117356816,0.816768308979,0.811017969648,0.814044843145,0.814010686727,0.807301755249,0.808079114698,0.807772255388,0.810234410051,0.807274946469,0.818767620936,0.804948253544,0.808717385344,0.807426486794,0.807089065382,0.806828586998,0.804565522585,0.792948555893,0.808827632308,0.804814543875,0.813370670879,0.811940220235,0.803581622158,0.808500793877,0.80464803835,0.810713619336,0.802748048648,0.816059165014,0.815855710344,0.81566512786,0.799942362518,0.815897746677,0.807540311258,0.811501550749,0.797369990565,0.805715825265,0.817496668452,0.808704696641,0.80044180033,0.80508283435,0.806136298149,0.807919583361,0.810071028763,0.804914907533,0.797939746815,0.806826056484,0.79497331171,0.796188943191,0.808349702704,0.807223438007,0.802362002504,0.804298215768,0.807031771764,0.801939896077,0.809973567361,0.806107655251,0.804401232539,0.812082424839,0.807861028858,0.812374071755,0.810823252395,0.80151095658,0.806965899254,0.805273498452,0.805809599175,0.803670631221,0.803930816661,0.806166572958,0.80584160077,0.813165134064,0.799475486492,0.803673192797,0.807919118285,0.801982083798,0.806427008763,0.811409901008,0.807622794055,0.810071052741,0.813741729579,0.803287977279,0.800124658212,0.799311246586,0.809472592935,0.81280855122,0.798916239917,0.800064690496,0.810393637394,0.81082642142,0.805244394831,0.799526468465,0.806153663892,0.803893481803,0.801441054933,0.80399369967,0.802916377037,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