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Quick Start

The following two examples use data in the both cited papers mentioned in the index page. This is also available in jupyter notebook format

import networkx as nx
nx.__version__

'2.0'
from modularity_maximization import partition
from modularity_maximization.utils import get_modularity

Undirected Network

Karate
karate = nx.Graph(nx.read_pajek("data/karate.net"))
print(nx.info(karate))

Name: 
Type: Graph
Number of nodes: 34
Number of edges: 78
Average degree:   4.5882

comm_dict = partition(karate)
for comm in set(comm_dict.values()):
    print("Community %d"%comm)
    print(', '.join([node for node in comm_dict if comm_dict[node] == comm]))

Community 3
5, 7, 6, 11, 17
Community 4
20, 22, 1, 3, 2, 4, 8, 10, 13, 12, 14, 18
Community 5
27, 21, 23, 9, 15, 16, 19, 31, 30, 34, 33
Community 6
24, 25, 26, 28, 29, 32

from modularity_maximization.utils import get_modularity
print('Modularity of such partition for karate is %.3f' % get_modularity(karate, comm_dict))

Modularity of such partition for karate is 0.419
Jazz Network
jazz = nx.Graph(nx.read_pajek("data/jazz.net"))
print(nx.info(jazz))

Name:
Type: Graph
Number of nodes: 198
Number of edges: 2742
Average degree:  27.6970

comm_dict = partition(jazz)
for comm in set(comm_dict.values()):
    print("Community %d"%comm)
    print(', '.join([node for node in comm_dict if comm_dict[node] == comm]))

Community 3
    130, 127, 129, 69, 55, 54, 57, 56, 53, 52, 195, 194, 197, 67, 192, 114, 89, 113, 112, 83, 7, 103, 31, 30, 36, 34, 61, 178, 174, 186, 196, 185, 164, 165, 161, 11, 10, 12, 14, 19, 151, 150, 158, 49, 87, 142, 141, 74, 72, 71, 70, 2
    Community 4
    133, 136, 138, 25, 26, 27, 21, 28, 29, 4, 96, 124, 126, 128, 51, 90, 198, 191, 115, 88, 85, 3, 92, 102, 39, 37, 176, 173, 172, 180, 181, 184, 6, 189, 97, 169, 167, 160, 163, 13, 18, 153, 152, 155, 157, 156, 86, 45, 5, 147, 144, 145, 140, 148, 149, 77, 76, 75, 73, 79, 41, 47
    Community 5
    132, 131, 135, 134, 139, 24, 20, 22, 23, 95, 8, 120, 122, 123, 91, 58, 50, 110, 80, 81, 119, 108, 109, 100, 101, 106, 107, 104, 105, 38, 33, 32, 35, 60, 62, 63, 64, 65, 66, 68, 171, 170, 182, 187, 188, 179, 99, 98, 168, 166, 162, 15, 17, 16, 154, 159, 48, 46, 44, 42, 43, 40, 1, 78
    Community 6
    137, 93, 94, 121, 125, 59, 190, 193, 111, 82, 118, 84, 177, 175, 183, 117, 116, 9, 146, 143

print('Modularity of such partition for jazz is %.3f' % get_modularity(jazz, comm_dict))


    Calculating modularity for undirected graph
    Modularity of such partition for karate is 0.442

Directed Network: Big 10 Football Season 2005

big_10_football = nx.read_gml("data/big_10_football_directed.gml")
print(nx.info(big_10_football))

Name: 
Type: DiGraph
Number of nodes: 11
Number of edges: 44
Average in degree:   4.0000
Average out degree:   4.0000

comm_dict = partition(big_10_football)
for comm in set(comm_dict.values()):
    print("Community %d"%comm)
    print(', '.join([node for node in comm_dict if comm_dict[node] == comm]))

Community 1
OhioState, Northwestern, Wisconsin, Iowa, PennState, Michigan, Minnesota
Community 2
Purdue, Illinois, MichiganState, Indiana

print('Modularity of such partition for karate is %.3f' %\
      get_modularity(big_10_football, comm_dict))

Calculating modularity for directed graph
Modularity of such partition for karate is 0.112