ALGORITHM SCAN(G=<V, E>, ε, μ)

// all vertices in V are labeled as unclassified;
for each unclassified vertex v ∈ V do
// STEP 1. check whether v is a core;
    if COREε,μ(v) then
// STEP 2.1. if v is a core, a new cluster is expanded;
        generate new clusterID;
        insert all x ∈ Nε (v) into queue Q;
        while Q ≠ 0 do
            y = first vertex in Q;
            R = {x ∈ V | DirREACHε,μ(y, x)};
            for each x ∈ R do
                if x is unclassified or non-member then
                    assign current clusterID to x;
                if x is unclassified then
                    insert x into queue Q;
            remove y from Q;
    else
// STEP 2.2. if v is not a core, it is labeled as non-member
        label v as non-member;
end for.
// STEP 3. further classifies non-members
for each non-member vertex v do
    if (? x, y ∈ Γ(v) ( x.clusterID ≠ y.clusterID) then
        label v as hub
    else
        label v as outlier;
end for.
end SCAN.

"""Find k-clique communities in graph using the percolation method.

A k-clique community is the union of all cliques of size k that can be reached through adjacent (sharing k-1 nodes) k-cliques.
Parameters:

    k (int) – Size of smallest clique
    cliques (list or generator) – Precomputed cliques (use networkx.find_cliques(G))

Return type:

Yields sets of nodes, one for each k-clique community.

"""
import networkx as nx
import sys
import time
def find_community(graph,k):
    return list(nx.k_clique_communities(graph,k))
G = nx.Graph()
##testFile=open("F://2.txt","r") ##2-6
testFile=open("F://21.txt","r") ##5,10
for line in testFile:
    a=line.strip(‘\n‘).split(",")
    G.add_edge(a[0],a[1])

for k in range(5,10):
        print ("############# k值: %d ################" % k)
        start_time = time.clock()
        rst_com = find_community(G,k)
        end_time = time.clock()
        print ("计算耗时(秒)：%.3f" % (end_time-start_time))
        print ("生成的社区数：%d" % len(rst_com))
        print(rst_com)

a,b
c,d
a,c
a,d