# Modularity comm dectection

This algorithm works for different types of networks.

We used this command on the directed weighted network:

``````

In [3]:

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 [7]:

WORKING_FOLDER = '../../../DATA/FIN/Modularity'
os.chdir(WORKING_FOLDER)
f_in_graph = 'mention_graph_weights.dat'

f_in_Modularity_output_comm = 'modularity_partition_names.tab'

``````
``````

In [22]:

f = open('mutual_unweighted_node_names', 'r')
node_names = defaultdict(int)
for line in f:
if line[0] == '#':
continue
node_id, node_name = line.split()
node_names[int(node_id)] = int(node_name)
return node_names

``````
``````

In [81]:

# returns a dict with nodes ids per community in each value
# dict keys are just community ids in returned order
input_file = codecs.open(f_in_Modularity_output_comm, 'r', encoding='utf8')
output = defaultdict(list)
num_COMM = 0
for line in input_file:
line = line.split()
output[num_COMM] = [int(str(el)) for el in line]
num_COMM += 1

return output

``````
``````

Out[81]:

defaultdict(list,
{0: [29026, 8787],
1: [9177, 25816],
2: [9992],
3: [4580],
4: [22829, 18506],
5: [20508, 14430],
6: [17288],
7: [682],
8: [26722, 3921],
9: [9215, 7255],
10: [20247, 17586],
11: [6639],
12: [6553, 6284],
13: [21926, 9269, 2369, 24169],
14: [1965, 9711, 28637, 5131, 17994],
15: [17767, 9469, 21035],
16: [8440],
17: [18001],
18: [27892, 3406],
19: [18894, 18063],
20: [1847, 14494],
21: [4833, 20219],
22: [21081, 23708, 11675],
23: [10910, 27649],
24: [16628, 15929],
25: [11449, 7441],
26: [27494, 2504],
27: [7964, 4542],
28: [5864, 16],
29: [23793, 27834, 23117],
30: [1212],
31: [342, 7325],
32: [11279, 21376],
33: [29551, 13292],
34: [2536, 27211],
35: [16814],
36: [9682, 3505],
37: [10429, 23129],
38: [6528, 18892],
39: [16644],
40: [20716, 24270],
41: [1787, 5130],
42: [4281, 15127, 25278],
43: [18200, 20147],
44: [10086, 12099],
45: [15882, 18659],
46: [6267, 25234],
47: [15413, 6752],
48: [2189, 23867],
49: [15816, 22529],
50: [11196],
51: [21751],
52: [6931, 5239],
53: [4335],
54: [7691, 2352],
55: [22669],
56: [2578],
57: [21723, 28788],
58: [12224],
59: [601],
60: [15894, 24047],
61: [28762],
62: [13668, 1542],
63: [25621, 12833, 27610, 79],
64: [4146],
65: [8961, 3799],
66: [27523, 16473],
67: [3282, 28686],
68: [23663, 11352],
69: [7854, 9496],
70: [5220, 5112],
71: [28379],
72: [16243, 20300],
73: [22071],
74: [26698],
75: [19636, 6962],
76: [5701],
77: [3326, 13588],
78: [5206, 10354],
79: [8013, 1043],
80: [21736, 5744, 13105, 28665, 11989],
81: [23361, 17117],
82: [7779, 13861, 12808],
83: [221, 27644],
84: [28201],
85: [16873, 21281],
86: [8158, 2540, 23662],
87: [11751],
88: [8155, 24021],
89: [16946],
90: [11656, 27747],
91: [28172, 26878],
92: [16431, 10272],
93: [17699, 303],
94: [10674],
95: [18215, 15742],
96: [21388, 16445],
97: [28083],
98: [12170, 25800, 9045],
99: [4298],
100: [1969, 10702],
101: [27134],
102: [19048, 17180],
103: [15864, 25898],
104: [16822],
105: [8292, 24149],
106: [14205],
107: [5460, 16949],
108: [20885, 11330],
109: [24838, 17873, 6896],
110: [14847],
111: [1404, 17683],
112: [319, 23144],
113: [7772, 12583],
114: [7549, 2804, 3314],
115: [12406, 6035],
116: [7828, 25636],
117: [8164, 24938, 12975],
118: [8738, 10778],
119: [11075, 15266],
120: [22593, 3763],
121: [8862, 11995, 10178],
122: [19259, 21556],
123: [11714, 14745],
124: [14989, 11521],
125: [8655],
126: [21427, 13698],
127: [218, 9783],
128: [13351, 26944],
129: [26251, 15804],
130: [27130],
131: [22279, 29318],
132: [16227],
133: [12504, 16997, 17496],
134: [17014, 14854],
135: [6873, 4748],
136: [18254, 26556],
137: [22828],
138: [18369, 23187],
139: [24425, 21286, 24347],
140: [28029, 7705],
141: [25410],
142: [14686],
143: [6867],
144: [987],
145: [484, 18684, 26624],
146: [15841],
147: [27600, 17697],
148: [23258],
149: [28632],
150: [17866, 1955],
151: [17152, 3488],
152: [20185, 8297],
153: [14931, 4512],
154: [11398, 6558],
155: [386, 21407],
156: [12900, 4713, 22961, 9146],
157: [1409],
158: [5947],
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160: [5782],
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163: [26200, 14109],
164: [9424],
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20944,
19628,
5105,
25198,
26514,
9512,
15395,
28891,
28627,
7064,
5400,
3318,
12882,
14918,
17185,
22998,
6668],
977: [9315, 23522, 19524, 13910],
978: [20211],
979: [21466, 15744],
980: [25857, 21084, 8993, 18845, 25849],
981: [6734, 12501],
982: [20805, 5126, 24606],
983: [13768, 1379],
984: [25698,
18788,
19682,
12959,
5997,
15964,
10018,
26113,
24059,
12633,
9976,
29394,
17804,
13033,
21264,
24518,
27433,
8768,
17607,
26797,
11057,
1725,
27456,
13522,
442,
19581,
27842,
26787,
647,
22796,
14698,
9703,
20025,
15110,
64,
3330,
3724,
650,
5747,
10908,
13604,
19054,
7625,
11512,
19559,
17104,
2470,
7048,
7241,
11268,
16896,
24509,
1038,
11703,
2230,
5229,
25317,
4861,
21397,
24196,
20760,
1461,
14190,
8264,
16266,
16748,
19647,
25982,
19618,
26355,
7775,
28494,
18292,
8387,
23319,
6452,
19655,
5731,
9429,
29072,
7263],
985: [24875, 14941],
986: [476, 26344],
987: [20030],
988: [4613,
15023,
15381,
14669,
6607,
10924,
17583,
15261,
1,
14807,
15634,
2909,
7706,
10244,
26748,
849,
23635,
15320],
989: [15878],
990: [14808,
23454,
2027,
24403,
28401,
7947,
6101,
18265,
27471,
27496,
22825,
16476,
22104,
7298,
28681,
13556,
23451,
29321,
17548,
20043,
28828,
8177,
10530,
9934,
17043,
13630,
27526,
22372,
6671,
8251,
26910,
16032,
767,
19419,
7648,
6743,
3259,
25431,
4354,
649,
5910],
991: [17157, 15243],
992: [1019, 2685],
993: [9079],
994: [7813, 17430],
995: [28952, 28468],
996: [24137, 2490],
997: [7300, 12709],
998: [23728, 26023, 19330],
999: [27731, 28756, 5688],
...})

``````
``````

In [9]:

ordered_communities = OrderedDict(sorted(read_modularity_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)

``````
``````

In [17]:

# returns a set of communities in which each node belongs to
def find_nodes_COMM():
nodes_COMM = defaultdict(list)
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 [18]:

# a helper function to show us info when print info is true
# otherwise returns only the list of community sizes
def num_and_sizes_modularity_COMM(print_info=False):
num_COMM = 0
COMM_sizes = []
ALL_users = defaultdict(int)
num_COMM = len(COMM.keys())
for comm in COMM:
COMM_sizes.append(len(COMM[comm]))
for el in COMM[comm]:
ALL_users[el] = 1
if print_info:
print 'Modularity 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
return COMM_sizes

``````
``````

In [19]:

num_and_sizes_modularity_COMM(print_info=True)

``````
``````

Modularity has output: 2141 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, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 8, 8, 8, 8, 8, 8, 8, 9, 9, 9, 9, 9, 9, 10, 10, 10, 11, 11, 11, 11, 11, 12, 12, 12, 12, 12, 12, 12, 13, 13, 13, 14, 14, 15, 15, 15, 15, 16, 16, 17, 17, 18, 18, 19, 19, 19, 19, 20, 20, 21, 21, 21, 22, 23, 25, 26, 26, 27, 27, 28, 30, 31, 31, 32, 33, 34, 39, 39, 41, 43, 43, 47, 48, 49, 51, 52, 52, 53, 54, 56, 57, 57, 57, 58, 58, 58, 58, 59, 62, 62, 64, 67, 67, 67, 74, 78, 78, 79, 81, 83, 86, 87, 87, 90, 92, 94, 94, 94, 97, 97, 99, 106, 107, 108, 109, 113, 116, 117, 119, 120, 121, 126, 126, 126, 129, 131, 132, 132, 133, 137, 142, 143, 143, 147, 149, 153, 161, 167, 171, 172, 185, 190, 197, 212, 225, 227, 244, 250, 257, 294, 309, 321, 378, 380, 400, 409, 429, 486, 501, 519, 543, 638, 864, 936, 2694, 2869]
Total number of users in COMM:
26717
Total size of COMM:
26717

Out[19]:

[2,
2,
1,
1,
2,
2,
1,
1,
2,
2,
2,
1,
2,
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1,
2869,
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14,
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1,
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2,
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380,
519,
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116,
3,
1,
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3,
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3,
4,
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2,
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3,
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1,
1,
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2,
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1,
4,
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2,
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2,
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1,
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227,
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1,
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20,
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3,
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108,
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212,
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27,
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2,
2,
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41,
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2,
2,
3,
3,
...]

``````
``````

In [23]:

# here this function is just a test if all is ok
# returns the node membership for each node
# i.e., in how many communities it participates
def find_nodes_in_more_COMM(print_info=False):
nodes_num_COMM = defaultdict(int)
for comm in COMM:
for node in COMM[comm]:
nodes_num_COMM[int(node)] += 1
cnt_singletons = 0

for node in all_nodes:
if node not in nodes_num_COMM:
cnt_singletons += 1
if print_info:
print len(all_nodes), len(nodes_num_COMM.keys())
print 'I found nodes out of communities ', cnt_singletons
#nodes_num_COMM2 = {node: nodes_num_COMM[node] if nodes_num_COMM[node] < 10 else 10 for node in nodes_num_COMM}
sorted_nodes_num_COMM = OrderedDict(sorted(nodes_num_COMM.items(), key=lambda t:t[1], reverse=True))
global N_overlap_comm
N_overlap_comm = sorted_nodes_num_COMM.items()[0][1]
#print sorted_nodes_num_COMM
return sorted_nodes_num_COMM

find_nodes_in_more_COMM(print_info=True)
print N_overlap_comm
N_overlap_comm = 20

``````
``````

26717 26717
I found nodes out of communities  0
1

``````

# Let us visualize the community structure

``````

In [28]:

# plots complementary CDF for the comm sizes
# in log-log scale
def plot_ccdf_comm_sizes():
COMM_sizes = num_and_sizes_modularity_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.show()

``````
``````

In [29]:

plot_ccdf_comm_sizes()

``````
``````

``````

# Now we add SR values and graph into the story

We want to see does community size correlate with average SR

``````

In [31]:

# reads in the graph using igraph and Ncol format, i.e., edge list
# with weights being the SR on the edges
#G = read("test", format="ncol", directed=False, weights=True)
G = Graph.Read_Ncol('directed_mention_graph_with_SR_NCOL_edgelist', directed=True, weights=True, names=True)
#print G.summary()
G.to_undirected(mode="collapse", combine_edges=min)
#print G.summary()
#G.simplify(combine_edges=None)
#print G.summary()
return G

``````
``````

Out[31]:

<igraph.Graph at 0x7f6e54e6ced8>

``````

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 [33]:

# 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 = []
for el in nodes:
n = G.vs.select(name = str(el))[0]
n = n.index
node_indices.append(n)
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 [38]:

def avg_SR_per_COMM_size():
size_vs_SR = defaultdict(int)
for comm_nodes in BigClam_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 [51]:

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 [52]:

scatterplot_avg_SR_per_COMM_size()

``````
``````

Corrcoef (-0.2096340780473733, 0.026528881689800013)

``````

Semantic capital

``````

In [54]:

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 [55]:

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 [60]:

def avg_SEM_CAP_per_COMM_size():
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 [65]:

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 [66]:

scatterplot_avg_SEM_CAP_per_COMM_size()

``````
``````

Corrcoef (-0.060480909011702666, 0.52642710266251047)

``````

# Entities, concepts, taxonomies etc.

``````

In [67]:

##################################################
##################################################

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 [68]:

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.show()

``````
``````

In [69]:

##################################################
# 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)
"""

# 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:
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"]
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 [74]:

def main(sizeN='ALL'):

if sizeN == "ALL":
sys.stdout = open('../ALL/top_20_stats', 'w')
find_taxonomy_of_user_list()
else:
sys.stdout = open('Modularity_taxonomy_stats_' + f_in_Modularity_output_comm + '.txt', 'w')
print len(top_communities), "top communities found of size", str(sizeN)

for community in top_communities:

``````
``````

In [82]:

main(sizeN=500)

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/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

<matplotlib.figure.Figure at 0x7f6e4c3e5c90>

<matplotlib.figure.Figure at 0x7f6e4c462790>

<matplotlib.figure.Figure at 0x7f6e4c4b3c50>

<matplotlib.figure.Figure at 0x7f6e4ca628d0>

<matplotlib.figure.Figure at 0x7f6e4e2a0d90>

<matplotlib.figure.Figure at 0x7f6e4c89d850>

<matplotlib.figure.Figure at 0x7f6e4dc3c150>

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