A Parallel Community Structure Mining Method in Big Social Networks

Abstract

Community structure plays a key role in analyzing network features and helping people to dig out valuable hidden information. However, how to discover the hidden community structures is one of the biggest challenges in social network analysis, especially when the network size swells to a high level. Infomap is a top-class algorithm in nonoverlapping community structure detection. However, it is designed for single processor. When tackling large networks, its limited scalability makes it less effective in fully utilizing server resources. In this paper, based on infomap, we develop a scalable parallel nonoverlapping community detection method, Pinfomr (parallel Infomap with MapReduce), which utilizes the MapReduce framework to solve the two problems. Experiments on artificial networks and real datasets show that our parallel method has satisfying performance and scalability.

Hybrid Optimization Algorithm for Community and Fraud Detection in Complex Networks for High Immunity Towards Link and Node Failures

“…For an example, data propagation is subjected by the group structure in the online social community [9][10][11][12]. The migration of humans or birds in the network have the capacity to spread the diseases across the globe [12].…”

“…The networks are becoming wider and wider since it is the period of information explosion. Thus, we required many effective community detection algorithms for analyzing the networks with millions of vertices [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15].…”