Community Detection by Node Betweenness and Similarity in Complex Network

Algorithm 1

The proposed algorithm.

	Input: Graph G, Node set V, Link set E.
	: the set of big scall community, : the set of small scall community.
	: the node that their betweenness is over .
	Output: the detected communities C.
	Step 1: identifying influent node
(1)	Ranking the node by their node betweenness decreasing. , where .
(2)	Find the node that their betweenness is over . (k < n). And remaining nodes are  = .
	Step 2: expanding the community
(1)	Calculate the degree of similarity between the and , and attribute the remaining nodes to the community where the highest similarity node is located.
(2)	The initial community is formed.
	Step 3: integrating the community
(a)	Suppose the is the average number of nodes in the community.
	For i in k:
	If > , then the community is a big scall community;
	= (m <= k)
	If  < S; then the community is a small scall community;
	= (n <= k)
(b)	for in :
	for in :
	calculate the ;
	if is the max;
	then
	until is None
	Return C