THE PROPOSED RESULTS OF ALGORITHM TO DETECT THE COMMUNITY ON SOCIAL NETWORK

Abstract

Nowadays, community detecting on social network has been an orientation which draws attention of many researchers. Numerous algorithms have been proposed, but one of the problems that need to be solved for social networks is the fact that the number of vertices and edges of the graph is extremely large. As a result, the volume of calculations in the algorithms could be significantly large, and thus it is difficult to meet with practical requirements. In this article, we introduce a new approach based on certain attributes of special vertex on network and then propose the algorithm of merging vertices of the same centrality to reduce input vertices/ edges of network, and develop in conjunction with label propagation techniques, build a heuristic function to speed up community detection algorithms. Experimental results on the standard data sets showed that, compared to the original label propagation method LPA, the average processing time was reduced down to only 85.5%, while the quality of the community is increased by an average of 1.145 times, thereby confirming the effectiveness of the proposed algorithm.