An Approach of Community Search with Minimum Spanning Tree Based on Node Embedding

Algorithm 1

Node embedding model NEBRW.

	Input: a network ; walks per node ; walk length ;
	windows size ; dimension
	Output: vector representations of nodes in
(1)	begin
(2)	initialize
(3)
(4)	whiledo
(5)	foreachdo
//rw is a random walk function
(6)
(7)	append into
(8)	end
(9)
(10)	end
(11)	construct a corpus consisting of sentences which are stored in
(12)	use the Skip-gram to learn the mapping by treating as a corpus
(13)	return
(14)	end