├── README.md
└── code.py


/README.md:
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 1 | This program (download <a rel="nofollow" target="_blank" href="http://fromto.hig.se/~bjg/HeadTailBreaksCode/HeadTailCommunityDetectionCode.rar"> <i>here</i></a></font></font>) is developed out of the following paper:
 2 | 
 3 | <i>Jiang B. and Ding M. (2015), Defining least community as a homogeneous group in complex networks, Physica A, 428, 154-160.</i>
 4 | 
 5 | Simply put, it is for obtaining homogeneous communities in complex networks by applying head/tail breaks on edge betweenness given its heavy-tailed distribution. The program is based on NetworkX 1.8, and its input and output files follow with Pajek file format (.net). 
 6 | 
 7 | 
 8 | <b>How to do:</b>
 9 | 
10 | 1. Open .net file in txt and copy all edges under line "*edges" and then paste them into a new txt file. 
11 | 2. Replace the related lines of the code with the input file location, the division rule, and output file location as the comment indicated. 
12 | 3. Run the program and then replace the edges from the original .net with the edges from the output file (It is better to make a copy of original net file for later comparison). 
13 | 4. Load the changed .net file in Pajek, click <i> draw network </i> button, then press "ctrl + k", all the homogeneous groups will show up. In case you want to know further info of detected homo-groups, in Pajek click <i> Network > Create Partition > Components > Weak </i>, set minimum size = 1, then the number of homo-groups and their node members etc. will be obtained. 
14 | 
15 | Note that the computing capacity of this program is limited to around 30,000 edges, and it may take couple of minutes or longer for a bigger graph (say > 10,000 edges). 
16 | 
17 | 
18 | 


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/code.py:
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 1 | import networkx as nx
 2 | import csv
 3 | import timeit
 4 | 
 5 | def getMean(values):
 6 |     mean=0
 7 |     numsum=0
 8 |     for index in range(len(values)):
 9 |         numsum=numsum+values[index]
10 |     mean=float(numsum)/float(len(values))
11 |     return mean
12 | 
13 | def HeadTailCommunityDetection(G,finaledgelist):
14 |     H=nx.connected_component_subgraphs(G)
15 |     for subgraph in H:
16 |         result=nx.edge_betweenness(subgraph, False, None)
17 |         edges=result.keys()
18 |         values=result.values()
19 |         mean = getMean(values)
20 |         edgelist=[]
21 |         edgetemp=subgraph.edges();
22 |         if len(edgetemp)<=2:
23 |             for edge in edgetemp:
24 |                 finaledgelist.append(edge)
25 |         else:
26 |             for index in range(len(values)):
27 |                     if values[index] <= mean:
28 |                         edgelist.append(edges[index])
29 |             if  (float(len(edgelist))/float(len(edges)))<=0.6: #change the head/tail division rule here, here is for tail percentage, so if the rule is 40/60, the value should be assigned 0.6 as in the code.
30 |                 for edge in edgelist:
31 |                     finaledgelist.append(edge)
32 |             else:
33 |                 Gsub= nx.Graph()
34 |                 for edge in edgelist:
35 |                     Gsub.add_edge(edge[0],edge[1])
36 |                 HeadTailCommunityDetection(Gsub,finaledgelist)
37 | 
38 | def HeadTailInitiator():
39 |     G = nx.Graph()
40 |     ins = open("sampleNetFile\\Protein\\edge.txt", "r")  #input file path
41 |     for line in ins:
42 |             words = line.split(' ')
43 |             G.add_edge(int(words[0]), int(words[1]))
44 |     ins.close()
45 |     finaledgelist=[]
46 |     start = timeit.default_timer()
47 |     HeadTailCommunityDetection(G,finaledgelist)
48 |     print "done!"
49 |     stop = timeit.default_timer()
50 |     print "Processing time(in second): "
51 |     print stop - start
52 |     text_file = open("OutputEdge.txt", "w")    #output file path
53 |     for edge in finaledgelist:
54 |         text_file.write(str(edge[0])+" "+str(edge[1])+"\n")
55 |     text_file.close()
56 | 
57 | HeadTailInitiator()
58 | 
59 | 


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