In [321]:
import os
from graph_tool.all import *
import codecs
from collections import defaultdict, OrderedDict
import matplotlib.pyplot as plt
import numpy as np
%matplotlib inline
from igraph import *
from scipy.stats.stats import pearsonr
from scipy import stats
import seaborn as sns
import pandas as pd
import math
import json
plt.style.use('seaborn-white')
sns.set(font_scale=2)
sns.set_style("white")
In [170]:
WORKING_FOLDER = '../../../DATA/FIN/Infomap'
os.chdir(WORKING_FOLDER)
f_in_graph = 'mention_graph_weights.dat'
# this is a .tree file
f_in_Infomap_output_comm = 'dir_weighted_node_names.tree'
In [171]:
# returns a dict with node ids per community in each value
# dict keys are just community ids in returned order
def read_Infomap_output(print_info=False):
input_file = codecs.open(f_in_Infomap_output_comm, 'r', encoding='utf8')
output = defaultdict(list)
num_COMM = 0
max_tree_level = 0
for line in input_file:
if line[0] == '#':
continue
line = line.split()
tree_levels = line[0].split(':')[:-1]
if len(tree_levels) > max_tree_level:
max_tree_level = len(tree_levels)
node_name = line[-2].replace('"', '')
multiplier = 1
for comm in tree_levels:
output[int(comm)*multiplier].append(int(node_name))
multiplier *= 10
if print_info:
print 'number of communities by Infomap ', len(output.keys())
print 'MAX hierarchical tree levels ', max_tree_level + 1
for comm in output:
output[comm] = list(set(output[int(comm)]))
return output
read_Infomap_output(print_info=True)
number of communities by Infomap 4692
MAX hierarchical tree levels 7
Out[171]:
defaultdict(list,
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1999,
2000,
2020,
2022,
2032,
2033,
2036,
2039,
2043,
2046,
2048,
2054,
2060,
2065,
2067,
2068,
2073,
2075,
2077,
2082,
2084,
2085,
2088,
2089,
2093,
2097,
2099,
2100,
2118,
2126,
2128,
2130,
2132,
2133,
2143,
2149,
2150,
2154,
2158,
2159,
2163,
2168,
2175,
2181,
2184,
2188,
2200,
2202,
2213,
2218,
2221,
2223,
2242,
2244,
2253,
2260,
2271,
2272,
2276,
2281,
2285,
2288,
2290,
2291,
2298,
2308,
2310,
2311,
2316,
2319,
2321,
2324,
2331,
2333,
2335,
2339,
2345,
2351,
2356,
2357,
2360,
2361,
2366,
2370,
2372,
2373,
2375,
2386,
2388,
2389,
2394,
2395,
2396,
2397,
2408,
2413,
2414,
2415,
2418,
2419,
2434,
2436,
2440,
2446,
2447,
2448,
2451,
2453,
2455,
2456,
2463,
2474,
2478,
2483,
2489,
2492,
2507,
2513,
2514,
2521,
2522,
2525,
2526,
2528,
2530,
2534,
2537,
2553,
2557,
2560,
2570,
2571,
2573,
2574,
2576,
2579,
2602,
2604,
2607,
2612,
2620,
2625,
2626,
2627,
2630,
2631,
2639,
2643,
2645,
2647,
2656,
2664,
2671,
2672,
2674,
2679,
2683,
2698,
2699,
2701,
2705,
2708,
2710,
2714,
2723,
2729,
2734,
2739,
2740,
2748,
2751,
2762,
2764,
2767,
2773,
2780,
2781,
2786,
2787,
2794,
2795,
2798,
2806,
2830,
2840,
2850,
2854,
2867,
2868,
2869,
2872,
2878,
2886,
2890,
2893,
2902,
2904,
2917,
2919,
2932,
2942,
2943,
2945,
2951,
2952,
2965,
2966,
2969,
2974,
2977,
2985,
2986,
2990,
2991,
2994,
2995,
3001,
3010,
3011,
3014,
3021,
3022,
3023,
3024,
3033,
3046,
3055,
3060,
3065,
3070,
3080,
3083,
3085,
3086,
3092,
3095,
3097,
3100,
3117,
3119,
3125,
3129,
3134,
3135,
3144,
3150,
3155,
3159,
3162,
3177,
3184,
3189,
3192,
3203,
3206,
3211,
3213,
3215,
3219,
3221,
3226,
3230,
3254,
3258,
3269,
3272,
3280,
3281,
3290,
3300,
3303,
3305,
3306,
3315,
3320,
3324,
3325,
3362,
3384,
3386,
3388,
3391,
3404,
3405,
3407,
3415,
3422,
3424,
3428,
3430,
3433,
3434,
3439,
3451,
3453,
3455,
3459,
3462,
3478,
3481,
3487,
3490,
3496,
3498,
3499,
3502,
3503,
3512,
3524,
3526,
3527,
3528,
3534,
3548,
3557,
3560,
3565,
3567,
3570,
3571,
3572,
3573,
3586,
3601,
3602,
3614,
3616,
3617,
3618,
3629,
3633,
3636,
3640,
3646,
3654,
3659,
3665,
3666,
3668,
3672,
3674,
3684,
3687,
3692,
3693,
3698,
3699,
3702,
3714,
3720,
3722,
3732,
3733,
3736,
3743,
3744,
3748,
3755,
3765,
3774,
3775,
3777,
3790,
3795,
3800,
3807,
3808,
3809,
3813,
3818,
3824,
3825,
3840,
3843,
3850,
3852,
3855,
3858,
3862,
3875,
3880,
3881,
3884,
3888,
3889,
3893,
3900,
3901,
3903,
3907,
3909,
3916,
3917,
3922,
3938,
3939,
3940,
3958,
3960,
3968,
3971,
3972,
3973,
3976,
3980,
3985,
3986,
3996,
4001,
4003,
4004,
4005,
4007,
4009,
4013,
4026,
4046,
4049,
4052,
4053,
4056,
4058,
4068,
4073,
4079,
4087,
4094,
4098,
4118,
4119,
4121,
4122,
4125,
4126,
4127,
4129,
4154,
4155,
4163,
4168,
4169,
4170,
4172,
4175,
4190,
4201,
4203,
4211,
4232,
4235,
4249,
4251,
4259,
4263,
4265,
4268,
4269,
4272,
4285,
4293,
4307,
4312,
4313,
4323,
4326,
4331,
4336,
4337,
4342,
4343,
4350,
4351,
4368,
4372,
4381,
4389,
4391,
4397,
4399,
4402,
4406,
4407,
4409,
4412,
4419,
4420,
4427,
4432,
4435,
4442,
4443,
4457,
4463,
4465,
4470,
4471,
4473,
4481,
4500,
4502,
4505,
4506,
4509,
4516,
4519,
4524,
4533,
4537,
4538,
4549,
4551,
4554,
4556,
4577,
4582,
4585,
4590,
4598,
4608,
4611,
4617,
4621,
4632,
4644,
4648,
4671,
4676,
4681,
4694,
4696,
4697,
4699,
4702,
4705,
4713,
4717,
4718,
4721,
4740,
4747,
4749,
4752,
4755,
4758,
4760,
4766,
4768,
4775,
4778,
4779,
4782,
4784,
4789,
4805,
4806,
4809,
4811,
4814,
4822,
4841,
4845,
4855,
4868,
4875,
4882,
4885,
4887,
4895,
4899,
4903,
4904,
4906,
4911,
4913,
4916,
4923,
4928,
4933,
4939,
4945,
4951,
4952,
4958,
4960,
4965,
4987,
4991,
4996,
5001,
5002,
5013,
5018,
5019,
5025,
5028,
5029,
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11: [10833, 23379, 8790],
12: [29312,
15291,
25613,
6991,
23989,
1303,
24859,
22460,
17021,
25854],
13: [7972, 23948, 8952, 16275, 8536, 16095],
14: [15141, 11019, 14029, 46, 18545, 6802, 23356, 13565, 255],
15: [3244, 4541],
16: [8152, 7856, 20852, 29079],
17: [11874, 12539, 28031],
18: [8837, 10882, 10173, 1706],
19: [23764, 13941, 27502, 13743],
20: [16386,
24580,
5,
16390,
23212,
27824,
16403,
24597,
22,
26,
8219,
8220,
24609,
24613,
2737,
8232,
44,
24621,
46,
16437,
65,
24644,
5473,
8265,
24654,
16467,
8280,
24666,
24667,
92,
16478,
6843,
101,
8294,
24681,
16499,
8311,
8315,
8319,
140,
8334,
24725,
16535,
24733,
24734,
16544,
162,
16547,
17777,
24745,
171,
16556,
24753,
6007,
8374,
24760,
16569,
8379,
24766,
24772,
16584,
8394,
8395,
10957,
24793,
8410,
16606,
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24801,
16617,
24814,
24815,
16625,
245,
16631,
16632,
16633,
16635,
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16640,
16643,
19158,
8458,
8465,
8466,
24855,
4143,
24860,
294,
8487,
24872,
24874,
8494,
8498,
309,
16695,
24629,
24898,
8519,
331,
333,
16719,
8528,
17742,
16726,
346,
24924,
24925,
352,
8545,
355,
356,
24933,
360,
8555,
24943,
9014,
24951,
24952,
24953,
389,
24967,
10989,
24981,
8598,
16793,
8606,
28741,
417,
8611,
16805,
8614,
16808,
17820,
8624,
25022,
25025,
16837,
458,
8651,
25039,
19192,
16850,
1443,
25045,
471,
25048,
473,
8671,
483,
27388,
25068,
25070,
8691,
502,
25081,
25084,
1450,
516,
16903,
8719,
528,
4184,
531,
8724,
536,
90,
16928,
17842,
16947,
8286,
19209,
25149,
8766,
9653,
16960,
16965,
582,
25159,
16971,
16972,
25166,
8783,
25172,
606,
610,
26043,
15121,
25195,
8813,
8815,
25200,
17016,
25213,
25217,
644,
11030,
646,
8839,
27415,
8845,
17039,
19224,
17042,
8852,
8856,
665,
25246,
25252,
10661,
17069,
25262,
25266,
17080,
25275,
1482,
4213,
706,
8902,
17096,
12407,
17102,
17869,
8912,
25307,
8924,
25310,
741,
25335,
17146,
17147,
768,
17154,
8965,
25350,
25361,
8979,
25366,
25370,
25372,
25377,
17189,
17194,
25387,
817,
25395,
17204,
24032,
25398,
4233,
23349,
25412,
9697,
25416,
17225,
9034,
9046,
9047,
25434,
17246,
863,
23355,
870,
17257,
874,
25457,
883,
17272,
9083,
8341,
25472,
17281,
898,
17283,
9092,
17286,
17289,
17293,
25496,
25497,
923,
9117,
25502,
25505,
25507,
27582,
25514,
940,
941,
17330,
954,
955,
957,
17342,
968,
9164,
973,
9167,
25552,
978,
9178,
17375,
9184,
1531,
998,
6993,
20647,
9197,
17390,
9200,
1009,
1013,
25590,
8361,
1020,
1022,
25600,
25601,
1027,
9222,
25610,
25613,
1040,
1049,
9242,
25631,
9254,
17453,
19403,
17928,
1074,
1078,
25661,
21313,
9279,
25665,
182,
1094,
17485,
17486,
17488,
184,
17492,
17493,
9302,
25697,
25701,
5649,
25704,
9321,
19303,
13404,
20670,
19951,
25720,
9338,
9339,
17536,
20672,
25731,
17542,
1167,
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1177,
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1198,
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17597,
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17621,
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1364,
17750,
17751,
9561,
17754,
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25949,
9568,
1381,
17769,
9585,
1395,
26174,
11156,
9594,
17790,
9599,
17793,
2966,
17798,
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25994,
17803,
1421,
9617,
9619,
9622,
1431,
9624,
1434,
8431,
26012,
9630,
26016,
26019,
26022,
1448,
26026,
1458,
1459,
17844,
26037,
26039,
1465,
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17870,
26065,
1494,
1498,
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1506,
26090,
17902,
9712,
26097,
9717,
17911,
17915,
26108,
26110,
26112,
1540,
9733,
9734,
1544,
9738,
26123,
9747,
26132,
17945,
1562,
26139,
26140,
26141,
1570,
17956,
1575,
1577,
17964,
11186,
1584,
9779,
26166,
1594,
9790,
1599,
26182,
12556,
17995,
1618,
26197,
26199,
9820,
1629,
1631,
18019,
9833,
26218,
9835,
11904,
9845,
1641,
9852,
18046,
18047,
18050,
1679,
9872,
1683,
18074,
1692,
11207,
26285,
18095,
18098,
1716,
18109,
26305,
1731,
22134,
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1760,
1762,
9964,
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1775,
1781,
9974,
9977,
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9982,
26378,
1804,
18189,
9998,
26385,
18197,
10010,
10011,
26399,
26412,
1839,
10032,
18225,
1842,
1844,
18229,
1846,
18231,
26424,
1849,
18234,
10050,
26435,
28982,
18246,
1870,
26447,
18256,
26449,
10066,
18259,
1879,
1880,
16975,
26255,
10083,
1892,
10088,
26477,
26479,
11240,
26484,
18300,
1920,
10121,
18317,
1938,
26521,
10138,
26523,
10140,
9882,
18335,
26528,
18337,
1954,
26533,
11249,
26268,
18346,
24903,
10159,
18358,
24616,
11252,
26554,
1992,
27890,
1995,
26572,
10191,
26580,
2005,
10203,
26588,
2013,
2014,
18399,
18400,
336,
19451,
26596,
18407,
2031,
18426,
2049,
18435,
26628,
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2071,
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10333,
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10385,
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4501,
2432,
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2438,
2439,
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18840,
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2474,
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10746,
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18963,
2580,
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2589,
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2593,
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27176,
10793,
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2622,
2625,
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19015,
19017,
2634,
23650,
19022,
27222,
27223,
27229,
19040,
19041,
27235,
10855,
27241,
2667,
2670,
2675,
27252,
10869,
19069,
19083,
2702,
19087,
2707,
7278,
19103,
10915,
2724,
10924,
27310,
19119,
10929,
2738,
19571,
2754,
459,
13731,
27350,
10969,
10973,
2782,
27359,
20945,
25042,
27379,
2804,
19190,
2808,
11003,
11004,
2816,
27776,
11010,
2820,
27398,
19207,
27401,
19210,
2829,
19216,
27409,
19219,
19222,
25049,
27416,
13188,
19227,
19228,
4570,
27426,
27429,
27430,
11048,
19243,
2860,
19247,
2864,
27442,
2868,
11065,
11068,
11069,
2881,
11077,
19271,
2888,
27470,
27473,
2901,
11099,
19296,
27792,
19298,
19300,
19302,
23697,
19312,
27509,
11126,
25065,
19320,
19322,
19327,
2947,
492,
2955,
2957,
19348,
16878,
19350,
27544,
27548,
2978,
19363,
19365,
11176,
19371,
2994,
19379,
2998,
19385,
3006,
19396,
3015,
19401,
27595,
3020,
19405,
27599,
27603,
27606,
11224,
27611,
27616,
11235,
7334,
27624,
27630,
3057,
10067,
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19446,
11256,
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19462,
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3082,
19468,
11278,
20995,
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19487,
27681,
3109,
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3115,
19500,
3118,
3131,
19519,
3143,
19745,
27728,
19537,
27730,
19540,
3163,
3164,
11363,
19556,
11365,
3175,
3176,
11371,
3181,
4627,
11380,
3200,
11395,
27784,
19593,
11403,
11404,
19598,
11408,
3217,
19652,
19610,
27803,
11425,
3237,
11435,
3248,
17021,
11459,
11460,
19657,
27851,
19661,
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21: [19552, 9113, 4558, 16527],
22: [23728, 19330, 26023],
23: [6087,
11272,
20329,
6487,
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...})
In [314]:
def read_in_top_communities(sizeN):
ordered_communities = OrderedDict(sorted(read_Infomap_output().items(), key=lambda t:len(t[1]), reverse=True))
top_communities = OrderedDict(x for x in ordered_communities.items() if len(x[1])>sizeN)
return top_communities
In [172]:
# returns a set of communities in which each node belongs to
def find_nodes_COMM():
nodes_COMM = defaultdict(list)
output = read_Infomap_output()
for comm in output.keys():
for node in output[comm]:
nodes_COMM[node].append(comm)
for node in nodes_COMM:
nodes_COMM[node] = list(set(nodes_COMM[node]))
return nodes_COMM
In [315]:
# a helper function to show us info when print info is true
# otherwise returns only the list of community sizes
def num_and_sizes_Infomap_COMM(print_info=False):
COMM_sizes = []
ALL_users = defaultdict(int)
infomap_COMM = read_Infomap_output()
num_COMM = len(infomap_COMM.keys())
for comm in infomap_COMM:
COMM_sizes.append(len(infomap_COMM[comm]))
for el in infomap_COMM[comm]:
ALL_users[el] = 1
if print_info:
print 'Infomap has output: %d COMM ' % (num_COMM)
print 'Their sizes in increasing order:'
print sorted(COMM_sizes)
print 'Total number of users in COMM:'
print len(ALL_users.keys())
print 'Total size of COMM:'
print sum(COMM_sizes)
return COMM_sizes
num_and_sizes_Infomap_COMM(print_info=True)
Infomap has output: 4692 COMM
Their sizes in increasing order:
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 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Total number of users in COMM:
26717
Total size of COMM:
90219
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2,
92,
2,
21,
2,
2,
2,
2,
2,
2,
2,
2,
2,
13,
2,
2,
2,
2,
2,
2,
2,
2,
2,
17,
2,
2,
2,
2,
2,
2,
2,
3,
2,
19,
2,
2,
2,
2,
2,
2,
2,
16,
2,
17,
3,
2,
2,
2,
2,
2,
2,
2,
2,
703,
2,
2,
2,
2,
2,
2,
2,
10,
2,
13,
3,
9,
2,
2,
2,
2,
2,
3,
2,
13,
2,
3,
2,
5,
2,
2,
2,
2,
2,
19,
2,
2,
2,
2,
2,
2,
2,
2,
2,
12,
2,
2,
2,
3,
2,
2,
3,
2,
2,
16,
2,
2,
3,
2,
3,
2,
2,
2,
2,
12,
2,
2,
2,
2,
2,
2,
2,
2,
2,
10,
3,
2,
3,
2,
2,
2,
2,
2,
3,
8,
2,
2,
2,
2,
2,
2,
3,
2,
2,
7,
3,
2,
2,
2,
2,
2,
2,
2,
2,
3043,
...]
In [174]:
# returns the node membership for each node
# i.e., in how many communities it participates
def find_nodes_in_more_COMM():
nodes_num_COMM = defaultdict(int)
infomap_COMM = read_Infomap_output()
for comm in infomap_COMM:
for node in infomap_COMM[comm]:
nodes_num_COMM[int(node)] += 1
sorted_nodes_num_COMM = OrderedDict(sorted(nodes_num_COMM.items(), key=lambda t:t[1], reverse=True))
print max(sorted_nodes_num_COMM.values())
return sorted_nodes_num_COMM
In [175]:
find_nodes_in_more_COMM()
40
Out[175]:
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(8540, 7),
...])
In [327]:
# plots complementary CDF for the membership per node
# in log-log scale
def plot_ccdf_node_comm_membership():
nodes_num_COMM = find_nodes_in_more_COMM()
data = np.array(nodes_num_COMM.values())
sorted_data = np.sort(data)
yvals=1-np.arange(len(sorted_data))/float(len(sorted_data)-1)
plt.plot(sorted_data,yvals,color='r')
plt.yscale('log')
plt.ylabel('complementary CDF')
plt.xscale('log')
plt.xlabel('comm memberships')
plt.savefig('ccdf_memberships.jpg')
plt.show()
In [328]:
plot_ccdf_node_comm_membership()
In [329]:
# plots complementary CDF for the comm sizes
# in log-log scale
def plot_ccdf_comm_sizes():
COMM_sizes = num_and_sizes_Infomap_COMM()
data = np.array(COMM_sizes)
sorted_data = np.sort(data)
yvals=1-np.arange(len(sorted_data))/float(len(sorted_data)-1)
plt.plot(sorted_data,yvals,color='r')
plt.yscale('log')
plt.ylabel('complementary CDF')
plt.xscale('log')
plt.xlabel('community size')
plt.savefig('ccdf_comm_sizes.jpg')
plt.show()
In [330]:
plot_ccdf_comm_sizes()
In [331]:
# reads in the graph using igraph and Ncol format, i.e., edge list
# with weights being the SR on the edges
def read_in_SR_graph():
#G = Graph.Read_Ncol('undirected_mention_graph_with_SR.csv', directed=False, weights=True)
#G = read("test", format="ncol", directed=False, weights=True)
G = Graph.Read_Ncol('directed_mention_graph_with_SR_NCOL_edgelist', weights=True, names=True)
print G.summary()
#for edge in G.es():
# print edge.tuple[0], edge.tuple[1], edge["weight"]
return G
Next function uses igraph to first find a subgraph induced by the community and to then calculate average SR on its edges. It is relatively fast given that other implementation I took (iterating through nodes) were really slow.
In [332]:
# find average SR between the connected nodes
# in the subgraph of G induced by the nodes
def find_avg_SR(G, nodes):
node_SR_list = []
node_indices = []
cnt = 0
for el in nodes:
try:
n = G.vs.select(name = str(el))[0]
except IndexError:
cnt += 1
#print el
continue
n = n.index
node_indices.append(n)
#print 'I didnt find SR for %d nodes ', cnt
edges = G.es.select(_within = node_indices)
for e in edges:
w = e['weight']
node_SR_list.append(w)
avg_SR = np.mean(np.array(node_SR_list))
std_SR = np.std(np.array(node_SR_list))
return (avg_SR, std_SR)
We now combine the BigClam output and previous function to find avergage SR per community
In [333]:
def avg_SR_per_COMM_size():
output = read_Infomap_output()
G = read_in_SR_graph()
size_vs_SR = defaultdict(int)
for comm_nodes in output.values():
size_vs_SR[len(comm_nodes)] = find_avg_SR(G, comm_nodes)
sorted_size_vs_SR = OrderedDict(sorted(size_vs_SR.items(), key=lambda t:t[0], reverse=True))
return size_vs_SR
We now plot the distribution. There is no correlation.
In [334]:
def plot_avg_SR_per_COMM_size():
size_vs_SR = avg_SR_per_COMM_size()
x = np.array(size_vs_SR.keys())
y = np.array([s[0] for s in size_vs_SR.values()])
e = np.array([s[1] for s in size_vs_SR.values()])
print 'Corrcoef', pearsonr(x, y)
plt.errorbar(x,y,e,linestyle="-",marker='*',color='maroon',label='mean SR per comm')
#plt.yscale('log', nonposy='clip')
plt.xlabel('comm size')
plt.ylabel('mean SR')
plt.legend(loc='best',frameon=False)
plt.show()
In [335]:
#plot_avg_SR_per_COMM_size()
In [336]:
def scatterplot_avg_SR_per_COMM_size():
size_vs_SR = avg_SR_per_COMM_size()
x = np.array(size_vs_SR.keys())
y = np.array([s[0] for s in size_vs_SR.values()])
e = np.array([s[1] for s in size_vs_SR.values()])
print 'Corrcoef', pearsonr(x, y)
plt.scatter(x,y, label='mean SR per comm')
plt.xlabel('comm size')
plt.ylabel('mean SR')
plt.xscale('log')
#plt.legend(loc='best',frameon=False)
plt.show()
In [231]:
scatterplot_avg_SR_per_COMM_size()
IGRAPH DNW- 26717 99910 --
+ attr: name (v), weight (e)
Corrcoef (nan, 1.0)
In [337]:
def read_sem_capital(f_name='user_entities.tab', tname='entities'):
f = open(f_name, "r")
cap = defaultdict(int)
cnt = 0
for line in f:
if tname == 'sentiment':
(vid, vn, val) = line.split('\t')
val = float(val)
else:
(vid, val) = line.split('\t')
val = float(val)
cap[vid] = val
cnt += 1
return cap
In [338]:
def find_avg_SEM_CAP(sem_cap, comm_nodes):
s = []
for node in comm_nodes:
s.append(sem_cap[str(node)])
s_mean = np.mean(np.array(s))
s_std = np.std(np.array(s))
return (s_mean, s_std)
In [339]:
def avg_SEM_CAP_per_COMM_size():
output = read_Infomap_output()
sem_cap = read_sem_capital()
size_vs_SEM_CAP = defaultdict(int)
for comm_nodes in output.values():
size_vs_SEM_CAP[len(comm_nodes)] = find_avg_SEM_CAP(sem_cap, comm_nodes)
sorted_size_vs_SEM_CAP = OrderedDict(sorted(size_vs_SEM_CAP.items(), key=lambda t:t[0], reverse=True))
return size_vs_SEM_CAP
In [340]:
def scatterplot_avg_SEM_CAP_per_COMM_size():
size_vs_SEM_CAP = avg_SEM_CAP_per_COMM_size()
x = np.array(size_vs_SEM_CAP.keys())
y = np.array([s[0] for s in size_vs_SEM_CAP.values()])
e = np.array([s[1] for s in size_vs_SEM_CAP.values()])
print 'Corrcoef', pearsonr(x, y)
plt.scatter(x,y)
plt.xlabel('comm size')
plt.ylabel('mean sem cap')
plt.xscale('log')
#plt.legend(loc='best',frameon=False)
plt.show()
In [278]:
scatterplot_avg_SEM_CAP_per_COMM_size()
Corrcoef (0.14029097707927499, 0.069711062691995507)
In [342]:
N_overlap_comm = 15
In [343]:
def how_many_nodes_with_pluralistic_level():
node_comm_membership = find_nodes_in_more_COMM()
pluralisic_classes_sizes = defaultdict(int)
for node in node_comm_membership:
pluralisic_classes_sizes[node_comm_membership[node]] += 1
return pluralisic_classes_sizes
#how_many_nodes_with_pluralistic_level()
In [344]:
# how similar is the node to his neighbors
def find_avg_neighborhood_SR_per_node_COMM_membership():
node_comm_membership = find_nodes_in_more_COMM()
G = read_in_SR_graph()
res = defaultdict(list)
for node in node_comm_membership:
n = G.vs.select(name = str(node))
nCOMM = node_comm_membership[node] if node_comm_membership[node] <= N_overlap_comm else N_overlap_comm
try:
total_SR = G.strength(n[0].index, weights='weight')
except IndexError:
continue
total_neighbors = G.degree(n[0].index)
meanSR = total_SR / float(total_neighbors)
res[nCOMM].append(meanSR)
res_mean = defaultdict(float)
res_stdev = defaultdict(float)
for COMM in res:
res_mean[COMM] = np.mean(np.array(res[COMM]))
res_stdev[COMM] = np.std(np.array(res[COMM]))
return res_mean, res_stdev
In [349]:
def plot_avg_neighborhood_SR_vs_COMM_membership():
fig, ax1 = plt.subplots()
ax1.set_xlabel('user comm membership')
d, std = find_avg_neighborhood_SR_per_node_COMM_membership()
x = d.keys()
y = d.values()
e = std.values()
ax1.set_ylabel('mean node SR with neighbors', color='red')
ax1.tick_params('y', colors='red')
ax1.errorbar(x,y,e,linestyle="-",marker='*',color='red',fmt='o',elinewidth=2.0)
ax1.set_xlim([0,N_overlap_comm+1])
ax2 = ax1.twinx()
pluralistic_classes = how_many_nodes_with_pluralistic_level()
z = np.array([pluralistic_classes[s] for s in x])
ax2.scatter(x,z,marker='o',color='blue')
ax2.plot(x,z,'-', color='blue')
ax2.set_ylabel('total users', color='blue')
ax2.tick_params('y', colors='blue')
ax2.set_yscale('log')
ax1.set_xlim([0,N_overlap_comm+1])
print 'Corrcoef SR and comm membership ', pearsonr(np.array(x), np.array(y))
#plt.title(r'Histogram for mention network pairwise SR: $\mu=' + "{:.3f}".format(mu) + '$, $\sigma= ' + "{:.3f}".format(sigma) + '$')
#plt.grid(True)
plt.savefig('Infomap_node_comm_membership_vs_mean_SR.jpg', bbox_inches='tight')
plt.show()
In [351]:
plot_avg_neighborhood_SR_vs_COMM_membership()
In [ ]:
In [357]:
def find_avg_SEM_cap_per_node_COMM_membership():
node_comm_membership = find_nodes_in_more_COMM()
sem_cap = read_sem_capital()
res = defaultdict(list)
for node in node_comm_membership:
n_sem = sem_cap[str(node)]
nCOMM = node_comm_membership[node] if node_comm_membership[node] < N_overlap_comm else N_overlap_comm
res[nCOMM].append(n_sem)
res_mean = defaultdict(float)
res_stdev = defaultdict(float)
for COMM in res:
res_mean[COMM] = np.mean(np.array(res[COMM]))
res_stdev[COMM] = np.std(np.array(res[COMM]))
return res_mean, res_stdev
In [360]:
def plot_SEM_CAP_vs_COMM_membership():
fig, ax1 = plt.subplots()
ax1.set_xlabel('user comm membership')
d, std = find_avg_SEM_cap_per_node_COMM_membership()
x = d.keys()
y = d.values()
e = std.values()
ax1.set_ylabel('mean user semantic capital', color='darkred')
ax1.tick_params('y', colors='darkred')
ax1.errorbar(x,y,e,linestyle="-",marker='*',color='darkred',fmt='o',elinewidth=2.0)
ax1.set_xlim([0,N_overlap_comm+1])
ax2 = ax1.twinx()
pluralistic_classes = how_many_nodes_with_pluralistic_level()
z = np.array([pluralistic_classes[s] for s in x])
ax2.scatter(x,z,marker='o',color='blue')
ax2.plot(x,z,'-', color='blue')
ax2.set_ylabel('total users', color='blue')
ax2.tick_params('y', colors='blue')
ax2.set_yscale('log')
ax1.set_xlim([0,N_overlap_comm+1])
print 'Corrcoef SEM CAP and comm membership ', pearsonr(np.array(x), np.array(y))
#plt.tight_layout()
plt.savefig('Infomap_node_comm_membership_vs_avg_SEM_CAP.jpg', bbox_inches='tight')
plt.show()
In [361]:
plot_SEM_CAP_vs_COMM_membership()
In [355]:
N_shared = 15
In [362]:
def read_in_mention_graph():
d = defaultdict(int)
fn = 'mention_graph_weights.dat'
f = open(fn, 'r')
for line in f:
(u1, u2, w) = line.split()
d[int(u1), int(u2)] = int(w)
return d
In [363]:
def find_node_interaction_comm_propensity():
d = read_in_mention_graph()
cnt_edges = 0
node_comm_membership = find_nodes_COMM()
comm_mem = defaultdict(list)
comm_mem_total_pairs = defaultdict(list)
max_shared = 0
for (u1, u2) in d:
comms_u1 = node_comm_membership[int(u1)]
comms_u2 = node_comm_membership[int(u2)]
shared = len(set(comms_u1).intersection(set(comms_u2)))
if shared > max_shared:
max_shared = shared
if shared > N_shared:
shared = N_shared
comm_mem[shared].append(d[(u1, u2)])
if shared not in comm_mem_total_pairs:
comm_mem_total_pairs[shared] = defaultdict(int)
if u2 > u1:
pom = u2
u2 = u1
u1 = pom
comm_mem_total_pairs[shared][((u1, u2))] = 1
cnt_edges += 1
N = cnt_edges
print N
comm_mem_mean = OrderedDict()
comm_mem_std = OrderedDict()
for comm in comm_mem:
comm_mem_mean[comm] = np.mean(np.array(comm_mem[comm]))
comm_mem_std[comm] = np.std(np.array(comm_mem[comm]))
comm_mem_total_pairs[comm] = sum(comm_mem_total_pairs[comm].values())
print 'Max shared COMM ', max_shared
return comm_mem_mean, comm_mem_std, comm_mem_total_pairs
In [364]:
def plot_interaction_comm_propensity():
comm_mem_mean, comm_mem_std, comm_mem_total_pairs = find_node_interaction_comm_propensity()
fig, ax1 = plt.subplots()
ax1.set_xlabel('shared comm between nodes')
x = comm_mem_mean.keys()
y = comm_mem_mean.values()
e = comm_mem_std.values()
z = comm_mem_total_pairs.values()
ax1.set_ylabel('communication intensity', color='r')
ax1.tick_params('y', colors='r')
ax1.errorbar(x,y,e,linestyle="-",marker='*',color='r',fmt='o',elinewidth=2.0)
ax1.set_xlim([-1,N_shared+1])
ax2 = ax1.twinx()
ax2.scatter(x,z,marker='o',color='darkblue')
ax2.plot(x,z,'-', color='darkblue')
ax2.set_ylabel('total user pairs', color='darkblue')
ax2.tick_params('y', colors='darkblue')
ax2.set_yscale('log')
ax2.set_xlim([-1,N_shared+1])
plt.savefig('Infomap_node_shared_comm_vs_CI.jpg', bbox_inches='tight')
print pearsonr(np.array(x), np.array(y))
In [365]:
plot_interaction_comm_propensity()
In [366]:
def read_in_SR_graph_simple():
d = defaultdict(int)
fn = 'mention_graph_IDs_with_SR.dat'
f = open(fn, 'r')
for line in f:
(u1, u2, w) = line.split()
d[int(u1), int(u2)] = float(w)
return d
In [370]:
def find_node_SR_vs_shared_comm():
d = read_in_SR_graph_simple()
cnt_edges = 0
node_comm_membership = find_nodes_COMM()
comm_mem = defaultdict(list)
comm_mem_total_pairs = defaultdict(list)
max_shared = 0
for (u1, u2) in d:
comms_u1 = node_comm_membership[int(u1)]
comms_u2 = node_comm_membership[int(u2)]
shared = len(set(comms_u1).intersection(set(comms_u2)))
if shared > max_shared:
max_shared = shared
if shared > N_shared:
shared = N_shared
comm_mem[shared].append(d[(u1, u2)])
if shared not in comm_mem_total_pairs:
comm_mem_total_pairs[shared] = defaultdict(int)
if u2 > u1:
pom = u2
u2 = u1
u1 = pom
comm_mem_total_pairs[shared][((u1, u2))] = 1
cnt_edges += 1
N = cnt_edges
print N
comm_mem_mean = OrderedDict()
comm_mem_std = OrderedDict()
for comm in comm_mem:
comm_mem_mean[comm] = np.mean(np.array(comm_mem[comm]))
comm_mem_std[comm] = np.std(np.array(comm_mem[comm]))
comm_mem_total_pairs[comm] = sum(comm_mem_total_pairs[comm].values())
print 'Max shared COMM ', max_shared
return comm_mem_mean, comm_mem_std, comm_mem_total_pairs
In [371]:
def plot_SR_vs_shared_comm():
comm_mem_mean, comm_mem_std, comm_mem_total_pairs = find_node_SR_vs_shared_comm()
fig, ax1 = plt.subplots()
ax1.set_xlabel('shared comm between nodes')
x = comm_mem_mean.keys()
y = comm_mem_mean.values()
e = comm_mem_std.values()
z = comm_mem_total_pairs.values()
ax1.set_ylabel('SR', color='r')
ax1.tick_params('y', colors='r')
ax1.errorbar(x,y,e,linestyle="-",marker='*',color='r',fmt='o',elinewidth=2.0)
ax1.set_xlim([-1,N_shared+1])
ax2 = ax1.twinx()
ax2.scatter(x,z,marker='o',color='darkblue')
ax2.plot(x,z,'-', color='darkblue')
ax2.set_ylabel('total user pairs', color='darkblue')
ax2.tick_params('y', colors='darkblue')
ax2.set_yscale('log')
ax2.set_xlim([-1,N_shared+1])
plt.savefig('Infomap_node_shared_comm_vs_SR.jpg', bbox_inches='tight')
print pearsonr(np.array(x), np.array(y))
In [372]:
plot_SR_vs_shared_comm()
In [373]:
def find_node_sentiment_vs_shared_comm():
d = read_in_SR_graph_simple()
sent = read_sem_capital(f_name='user_sentiment.tab', tname='sentiment')
cnt_edges = 0
node_comm_membership = find_nodes_COMM()
comm_mem = defaultdict(list)
comm_mem_total_pairs = defaultdict(list)
max_shared = 0
for (u1, u2) in d:
comms_u1 = node_comm_membership[int(u1)]
comms_u2 = node_comm_membership[int(u2)]
shared = len(set(comms_u1).intersection(set(comms_u2)))
if shared > max_shared:
max_shared = shared
if shared > N_shared:
shared = N_shared
comm_mem[shared].append(abs(sent[str(u1)] - sent[str(u2)]))
if shared not in comm_mem_total_pairs:
comm_mem_total_pairs[shared] = defaultdict(int)
if u2 > u1:
pom = u2
u2 = u1
u1 = pom
comm_mem_total_pairs[shared][((u1, u2))] = 1
cnt_edges += 1
N = cnt_edges
print N
comm_mem_mean = OrderedDict()
comm_mem_std = OrderedDict()
for comm in comm_mem:
comm_mem_mean[comm] = np.mean(np.array(comm_mem[comm]))
comm_mem_std[comm] = np.std(np.array(comm_mem[comm]))
comm_mem_total_pairs[comm] = sum(comm_mem_total_pairs[comm].values())
print 'Max shared COMM ', max_shared
return comm_mem_mean, comm_mem_std, comm_mem_total_pairs
In [374]:
def plot_sent_diff_vs_shared_comm():
comm_mem_mean, comm_mem_std, comm_mem_total_pairs = find_node_sentiment_vs_shared_comm()
fig, ax1 = plt.subplots()
ax1.set_xlabel('shared comm between nodes')
x = comm_mem_mean.keys()
y = comm_mem_mean.values()
e = comm_mem_std.values()
z = comm_mem_total_pairs.values()
ax1.set_ylabel('sentiment difference', color='r')
ax1.tick_params('y', colors='r')
ax1.errorbar(x,y,e,linestyle="-",marker='*',color='r',fmt='o',elinewidth=2.0)
ax1.set_xlim([-1,N_shared+1])
ax2 = ax1.twinx()
ax2.scatter(x,z,marker='o',color='darkblue')
ax2.plot(x,z,'-', color='darkblue')
ax2.set_ylabel('total user pairs', color='darkblue')
ax2.tick_params('y', colors='darkblue')
ax2.set_yscale('log')
ax2.set_xlim([-1,N_shared+1])
plt.savefig('Infomap_node_shared_comm_vs_sentiment.jpg', bbox_inches='tight')
print pearsonr(np.array(x), np.array(y))
In [375]:
plot_sent_diff_vs_shared_comm()
In [376]:
def find_overlapping_COMM():
nodes_num_COMM = find_nodes_in_more_COMM()
COMM_density = defaultdict(list)
output = read_Infomap_output()
for comm in output:
for node in output[comm]:
COMM_density[comm].append(nodes_num_COMM[int(node)])
COMM_density2 = defaultdict(tuple)
for COMM in COMM_density:
COMM_density2[COMM] = (np.mean(np.array(COMM_density[COMM])), len(COMM_density[COMM]))
COMM_density3 = OrderedDict(sorted(COMM_density2.items(), key=lambda t:t[1][0]))
return COMM_density3
In [377]:
def plot_COMM_size_vs_density():
import seaborn as sns
sns.set(color_codes=True, font_scale=2)
x = []
y = []
data = find_overlapping_COMM()
for COMM in data:
x.append(data[COMM][0])
y.append(data[COMM][1])
x = np.array(x)
y = np.array(y)
print 'Corrcoef COMM size and density ', pearsonr(np.array(x), np.array(y))
(r, p) = pearsonr(np.log(np.array(x)), np.array(y))
lab = r'$r=' + "{:.2f}".format(r) + '$, $p= ' + "{:.2f}".format(p) + '$'
xlabel = 'comm density'
ylabel = 'comm size'
sns.set_style("white")
plt.scatter(x, y, edgecolors='none', c='c', label=lab)
#g = sns.jointplot(x=x, y=y, kind='reg',annot_kws=dict(stat="r"), \
# joint_kws={'line_kws':{'color':'gray', 'alpha':0.3, 'markeredgewidth':0}}).set_axis_labels(xlabel, ylabel)
#regline = g.ax_joint.get_lines()[0]
#regline.set_color('c')
#regline.set_zorder('5')
#ax = g.ax_joint
#ax.set_xscale('log')
#g.ax_marg_x.set_xscale('log')
#labelsx = ['0','','1','', '2','', '3','','4']
#g.ax_joint.set_xticklabels(labelsx)
plt.yscale('log')
#plt.legend(frameon=0, loc=2)
#plt.show()
#plt.tight_layout()
plt.savefig('node_comm_size_density777.pdf',bbox_inches='tight' , dpi=550)
plt.show()
In [248]:
plot_COMM_size_vs_density()
40
Corrcoef COMM size and density (0.15592320067360205, 6.367142982371597e-27)
In [378]:
def find_avg_ST_INC_per_node_COMM_membership():
node_comm_membership = find_nodes_in_more_COMM()
sem_cap = read_sem_capital(f_name='status_inconsistency', tname='status_inconsistency')
res = defaultdict(list)
for node in node_comm_membership:
n_sem = sem_cap[str(node)]
nCOMM = node_comm_membership[node] if node_comm_membership[node] < N_overlap_comm else N_overlap_comm
res[nCOMM].append(n_sem)
res_mean = defaultdict(float)
res_stdev = defaultdict(float)
for COMM in res:
res_mean[COMM] = np.mean(np.array(res[COMM]))
res_stdev[COMM] = np.std(np.array(res[COMM]))
return res_mean, res_stdev
In [380]:
def plot_ST_INC_vs_COMM_membership():
fig, ax1 = plt.subplots()
ax1.set_xlabel('user comm membership')
d, std = find_avg_ST_INC_per_node_COMM_membership()
x = d.keys()
y = d.values()
e = std.values()
ax1.set_ylabel('mean user $st_{inc}$', color='darkcyan')
ax1.tick_params('y', colors='darkcyan')
ax1.errorbar(x,y,e,linestyle="-",marker='*',color='darkcyan',fmt='o',elinewidth=2.0)
ax1.set_xlim([0,N_overlap_comm+1])
ax2 = ax1.twinx()
pluralistic_classes = how_many_nodes_with_pluralistic_level()
z = np.array([pluralistic_classes[s] for s in x])
ax2.scatter(x,z,marker='o',color='darkblue')
ax2.plot(x,z,'-', color='darkblue')
ax2.set_ylabel('total users', color='darkblue')
ax2.tick_params('y', colors='darkblue')
ax2.set_yscale('log')
ax1.set_xlim([0,N_overlap_comm+1])
print 'Corrcoef ST INC and comm membership ', pearsonr(np.array(x), np.array(y))
#plt.tight_layout()
plt.savefig('Infomap_node_comm_membership_vs_mean_ST_INC.eps', bbox_inches='tight')
plt.show()
In [381]:
plot_ST_INC_vs_COMM_membership()
In [385]:
def find_avg_sentiment_per_node_COMM_membership():
node_comm_membership = find_nodes_in_more_COMM()
sem_cap = read_sem_capital(f_name='user_sentiment.tab', tname='sentiment')
res = defaultdict(list)
for node in node_comm_membership:
n_sem = sem_cap[str(node)]
nCOMM = node_comm_membership[node] if node_comm_membership[node] < N_overlap_comm else N_overlap_comm
res[nCOMM].append(n_sem)
res_mean = defaultdict(float)
res_stdev = defaultdict(float)
for COMM in res:
res_mean[COMM] = np.mean(np.array(res[COMM]))
res_stdev[COMM] = np.std(np.array(res[COMM]))
return res_mean, res_stdev
In [386]:
def plot_sentiment_vs_COMM_membership():
d, std = find_avg_sentiment_per_node_COMM_membership()
x = d.keys()
y = d.values()
e = std.values()
print 'Corrcoef sentiment and comm membership ', pearsonr(np.array(x), np.array(y))
plt.errorbar(x,y,e,linestyle="-",marker='*',color='darkcyan',label='mean sentiment per comm membership ')
#plt.yscale('log', nonposy='clip')
plt.xlabel('node comm membership')
plt.ylabel('mean node sentiment')
#plt.legend(loc=2,frameon=False)
plt.xlim(0,N_overlap_comm+1)
#plt.ylim(-0.1,0.1)
#plt.title(r'Histogram for mention network pairwise SR: $\mu=' + "{:.3f}".format(mu) + '$, $\sigma= ' + "{:.3f}".format(sigma) + '$')
#plt.grid(True)
plt.savefig('node_comm_membership_vs_avg_sentiment.jpg')
plt.show()
In [387]:
plot_sentiment_vs_COMM_membership()
In [388]:
##################################################
# read in a map for the twitter username --> id
##################################################
def read_user_IDs():
user_ids = defaultdict(str)
with codecs.open('../user_IDs.dat','r', encoding='utf8') as f:
for line in f:
line = line.split()
user_id = line[0]
user = line[1]
user_ids[user] = int(user_id)
return user_ids
In [389]:
def plot_taxonomy(tokens, weights, max_tokens, tokens_name, COM='ALL'):
df = pd.DataFrame({'token': tokens, 'weight': weights})
df.sort('weight', inplace=True, ascending=False)
if df.shape[0] > max_tokens:
tail = df.ix[max_tokens:, 'weight'].sum()
df2 = df.iloc[0:max_tokens].copy()
nrow = pd.Series({'token': '--[other ' + tokens_name + ']--', 'weight': tail})
df2.ix[df2.shape[0]] = nrow
df2.index = np.arange(df2.shape[0])
else:
df2 = df
# Let's plot something better than a word cloud!
plt.figure(figsize=(10,5))
plt.title('community ' + COM)
colors = []
for c in range(df2.shape[0]-1):
colors.append('#383838')
colors.append('#ebe728')
plt.barh(df2.index * -1, np.log(df2['weight']), color=colors)
plt.gca().yaxis.grid(False)
plt.yticks(df2.index * -1 + 1.0, df2['token'])
plt.ylim(-1 * df2.shape[0] + 1.4, 1)
plt.xlabel(tokens_name + ' weight')
plt.savefig(COM+tokens_name+'.jpg', bbox_inches='tight')
if tokens_name == 'entity':
plt.show()
In [390]:
##################################################
# the core function for the user lists, comm etc.
##################################################
"""
here, the options are to visualize the taxonomy for the whole dataset (COM="ALL")
and to visualize for different communities (COM="COM") that are read in through read_in_communities()
in the case of communities, this functions is invoked once per each community
-- user_list holds the ids of the users in one community
-- TOP_N holds the number of top concepts, keywords and entities that we want to visualize and record
-- user_com holds a map for user_id --> com_id
-- N_COM holds the total number of communities found (changes depending on the community detection algorithm)
"""
def find_taxonomy_of_user_list(user_ids=read_user_IDs(), COM='ALL', user_list=None, TOP_N=20):
# resulting dictionaries in which the counts and tfidf relevance are collected
keywords_sum = defaultdict(int)
entities_sum = defaultdict(int)
concepts_sum = defaultdict(int)
taxonomies_sum = defaultdict(int)
docSentiment_sum = defaultdict(int)
print user_list
cnt = 0
with codecs.open('../tweets_taxonomy_clean.JSON','r', encoding='utf8') as input_file:
for line7s in input_file:
try:
line = json.loads(line7s)
user_name = line["_id"]
user_name = str(user_name)
user_id = user_ids[user_name]
if user_list is not None:
if not user_id in user_list:
print user_id
continue
# if dealing with ALL, take all the users
taxonomy_all = line["taxonomy"]
keywords = taxonomy_all["keywords"]
entities = taxonomy_all["entities"]
concepts = taxonomy_all["concepts"]
taxonomy = taxonomy_all["taxonomy"]
#
docSentiment = taxonomy_all["docSentiment"]
# this counts how many user we have analyzed
cnt += 1
except KeyError:
#print line7s
# we don't print since it is tested, there some 10% users for whom
# the taxonomy was not successfuly downloaded and they would be listed here
continue
for el in keywords:
category = el["text"]
# if we first time encounter this keyword, add a dict for it in the result
if not category in keywords_sum:
keywords_sum[category] = defaultdict(int)
# we use this not so well coded part because tuples do not allow assignment
old_relev = keywords_sum[category][0]
old_cnt = keywords_sum[category][1]
new_relev = old_relev + float(el["relevance"])
new_cnt = old_cnt + 1
keywords_sum[category] = (new_relev, new_cnt)
for el in entities:
entity = el["text"]
if entity in ['#', '#MentionTo', 'twitter', 'Twitter']:
continue
# if we first time encounter this entity, add a dict for it in the result
if not entity in entities_sum:
entities_sum[entity] = defaultdict(int)
# we use this not so well coded part because tuples do not allow assignment
old_relev = entities_sum[entity][0]
old_cnt = entities_sum[entity][1]
new_relev = old_relev + float(el["relevance"])
new_cnt = old_cnt + 1
entities_sum[entity] = (new_relev, new_cnt, el["type"])
for el in concepts:
concept = el["text"]
if concept in ['Trigraph', 'Gh', 'trigraph']:
continue
# if we first time encounter this concept, add a dict for it in the result
if not concept in concepts_sum:
concepts_sum[concept] = defaultdict(int)
# we use this not so well coded part because tuples do not allow assignment
old_relev = concepts_sum[concept][0]
old_cnt = concepts_sum[concept][1]
new_relev = old_relev + float(el["relevance"])
new_cnt = old_cnt + 1
concepts_sum[concept] = (new_relev, new_cnt)
# a bit different procedure for extracting the sentiment
sentiment = docSentiment["type"]
if sentiment == "neutral":
docSentiment_sum[sentiment] += 1
else:
if not sentiment in docSentiment_sum:
docSentiment_sum[sentiment] = defaultdict(int)
old_score = docSentiment_sum[sentiment][0]
old_cnt = docSentiment_sum[sentiment][1]
old_mixed_cnt = docSentiment_sum[sentiment][2]
try:
new_score = old_score + float(docSentiment["score"])
except KeyError:
continue
new_cnt = old_cnt + 1
try:
new_mixed_cnt = old_mixed_cnt + int(docSentiment["mixed"])
except KeyError:
continue
docSentiment_sum[sentiment] = (new_score, new_cnt, new_mixed_cnt)
for el in taxonomy:
taxonomy_tree = el["label"]
taxon = taxonomy_tree
if not taxon in taxonomies_sum:
taxonomies_sum[taxon] = defaultdict(int)
old_score = taxonomies_sum[taxon][0]
old_cnt = taxonomies_sum[taxon][1]
new_score = old_score + float(el["score"])
new_cnt = old_cnt + 1
taxonomies_sum[taxon] = (new_score, new_cnt)
com_size = cnt
# THIS IS A CONSTANT, because we know how many users there are in total after we did one ALL run
N = 27665
print "*** The user list %s ***" % COM
print "Analyzed %d users out of total %d users " % (com_size, N)
try:
pos_users = docSentiment_sum["positive"][1]
pos_score = docSentiment_sum["positive"][0]
except TypeError:
pos_users = 0
pos_score = 0
try:
neg_users = docSentiment_sum["negative"][1]
neg_score = docSentiment_sum["negative"][0]
except TypeError:
neg_users = 0
neg_score = 0
try:
neu_users = docSentiment_sum["neutral"]
except TypeError:
neu_users = 0
print "___________________"
print "Sentiment stats: positive %d users; negative %d users; and neutral %d " % (pos_users, neg_users, neu_users)
print "Sentiment score: positive %f ; negative %f; and the sum sentiment %f " % (pos_score, neg_score, pos_score + neg_score)
print "Overall positive sentiment pct is %f " % (float(pos_users)/com_size)
print "___________________"
print "Total keywords found ", len(keywords_sum)
print "Total entities found ", len(entities_sum)
print "Total concepts found ", len(concepts_sum)
print "Total taxonomies on different levels found ", len(taxonomies_sum)
print "___________________"
#####################
## STARTS plotting ##
#####################
#####################
## KEYWORDS ##
#####################
for kw in keywords_sum:
tot_relev = keywords_sum[kw][0]
tot_cnt = keywords_sum[kw][1]
inv_kw_fq = 0 if tot_cnt == 0 else N/float(tot_cnt)
tfidf = float(tot_relev * math.log(1.0 + inv_kw_fq))
keywords_sum[kw] = (tot_relev, tot_cnt, tfidf)
print
print "Keywords (ordered by TF-IDF): [relevance, count, TF-IDF]"
ord_keywords_sum2 = OrderedDict(sorted(keywords_sum.items(), key=lambda x: x[1][0], reverse = True))
NN = len(ord_keywords_sum2.items())
labels = np.empty([NN], dtype="<U26")
sizes = np.empty([NN], dtype=float)
sizes_tot = np.empty([NN], dtype=float)
i = 0
for el in ord_keywords_sum2:
print el.encode('utf-8'), ord_keywords_sum2[el]
labels[i] = el
sizes[i] = float(ord_keywords_sum2[el][1])
sizes_tot[i] = float(ord_keywords_sum2[el][0])
i += 1
#if i == TOP_N:
# break
plot_taxonomy(tokens=labels, weights=sizes_tot, max_tokens=TOP_N, tokens_name='keyword', COM=COM)
#plot_taxonomy(labels, sizes_tot, "kw_com_" + str(COM) + ".png")
plt.clf()
print
#####################
## ENTITIES ##
#####################
for en in entities_sum:
tot_relev = entities_sum[en][0]
tot_cnt = entities_sum[en][1]
inv_ent_fq = 0 if tot_cnt == 0 else N/float(tot_cnt)
tfidf = tot_relev * math.log(1.0 + inv_ent_fq)
entities_sum[en] = (tot_relev, tot_cnt, tfidf)
print "Entities (sorted by TF-IDF): [relevance, count, TF-IDF]"
ord_entities_sum2 = OrderedDict(sorted(entities_sum.items(), key=lambda x: x[1][0], reverse = True))
NN = len(ord_keywords_sum2.items())
labels = np.empty([NN], dtype="<U26")
sizes = np.empty([NN], dtype=float)
sizes_tot = np.empty([NN], dtype=float)
i = 0
for el in ord_entities_sum2:
print el.encode('utf-8'), ord_entities_sum2[el]
labels[i] = el
#print labels
sizes_tot[i] = float(ord_entities_sum2[el][0])
sizes[i] = float(ord_entities_sum2[el][2])
i += 1
#if i == TOP_N:
# break
plot_taxonomy(tokens=labels, weights=sizes_tot, max_tokens=TOP_N, tokens_name='entity', COM=COM)
plt.clf()
print
#####################
## CONCEPTS ##
#####################
for conc in concepts_sum:
tot_relev = concepts_sum[conc][0]
tot_cnt = concepts_sum[conc][1]
inv_fq = 0 if tot_cnt == 0 else N/float(tot_cnt)
tfidf = float(tot_relev * math.log(1.0 + inv_fq))
concepts_sum[conc] = (tot_relev, tot_cnt, tfidf)
print "Concepts (sorted by TF-IDF): [relevance, count, TF-IDF]"
ord_concepts_sum = OrderedDict(sorted(concepts_sum.items(), key=lambda x: x[1][0], reverse = True))
NN = len(ord_keywords_sum2.items())
labels = np.empty([NN], dtype="<U26")
sizes = np.empty([NN], dtype=float)
sizes_tot = np.empty([NN], dtype=float)
i = 0
for el in ord_concepts_sum:
print el.encode('utf-8'), ord_concepts_sum[el]
labels[i] = el
sizes[i] = float(ord_concepts_sum[el][2])
sizes_tot[i] = float(ord_concepts_sum[el][0])
i += 1
#if i == TOP_N:
# break
plot_taxonomy(tokens=labels, weights=sizes_tot, max_tokens=TOP_N, tokens_name='concept', COM=COM)
#plot_pie(labels, sizes_tot, "concept_" + str(COM) + ".png")
plt.clf()
print
#####################
## TAXONOMIES ##
#####################
for taxon in taxonomies_sum:
tot_score = taxonomies_sum[taxon][0]
tot_cnt = taxonomies_sum[taxon][1]
inv_fq = 0 if tot_cnt == 0 else N/float(tot_cnt)
tfidf = float(tot_score * math.log(1.0 + inv_fq))
taxonomies_sum[taxon] = (tot_score, tot_cnt, tfidf)
print "Taxonomies (sorted by TF-IDF): [relevance, count, TF-IDF]"
ord_taxonomies_sum = OrderedDict(sorted(taxonomies_sum.items(), key=lambda x: x[1][0], reverse = True))
NN = len(ord_keywords_sum2.items())
labels = np.empty([NN], dtype="<U26")
sizes = np.empty([NN], dtype=float)
sizes_tot = np.empty([NN], dtype=float)
i = 0
for el in ord_taxonomies_sum:
print el.encode('utf-8'), ord_taxonomies_sum[el]
labels[i] = el
sizes[i] = float(ord_taxonomies_sum[el][2])
sizes_tot[i] = float(ord_taxonomies_sum[el][0])
i += 1
#if i == TOP_N:
# break
plot_taxonomy(tokens=labels, weights=sizes_tot, max_tokens=TOP_N, tokens_name='taxonomy', COM=COM)
#plot_pie(labels, sizes_tot, "taxon_" + str(COM) + ".png")
plt.clf()
print
In [391]:
def main(sizeN='ALL'):
if sizeN == "ALL":
#sys.stdout = open('../ALL/top_20_stats', 'w')
find_taxonomy_of_user_list()
else:
#sys.stdout = open('InfoMap_taxonomy_stats_' + f_in_Infomap_output_comm + '.txt', 'w')
top_communities = read_in_top_communities(sizeN)
print len(top_communities), "top communities found of size", str(sizeN)
for community in top_communities:
# user_ids=read_user_IDs(), COM='ALL', user_list=None, TOP_N=20
find_taxonomy_of_user_list(user_ids=read_user_IDs(), COM=str(community), user_list=top_communities[community], TOP_N=20)
In [393]:
main(sizeN=2360)
/usr/local/lib/python2.7/dist-packages/ipykernel_launcher.py:3: FutureWarning: sort(columns=....) is deprecated, use sort_values(by=.....)
This is separate from the ipykernel package so we can avoid doing imports until
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In [ ]:
Content source: sanja7s/SR_Twitter
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