Title: Mining top-k influential nodes in social networks via community detection

Authors: Wei Li; Jianbin Huang; Shuzhen Wang

Addresses: School of Software, Xidian University, Xi'an, 710071, China ' School of Software, Xidian University, Xi'an, 710071, China ' School of Computer Science and Technology, Xidian University, Xi'an, 710071, China

Abstract: Influence maximisation is a challenging problem with high computational complexity. It aims to find a small set of seed nodes in a social network that maximises the spread of influence under a certain influence model. In this paper, we propose a community-based greedy algorithm for mining top-k influential nodes in a social network. Our method consists of two separate steps: community detection and top-k nodes mining. In the first step, we use an efficient algorithm to discover the community structure in a network. Then a 'divide and conquer' process is adopted to find the top-k influential nodes from the network. Experimental results on real-world networks show that our method is effective for mining highly influential nodes in networks. Moreover, it is more efficient than the traditional algorithms using greedy policy.

Keywords: influence maximisation; social networks; community detection; top-k nodes mining; top-k influential nodes; greedy algorithm.

DOI: 10.1504/IJITM.2015.068460

International Journal of Information Technology and Management, 2015 Vol.14 No.2/3, pp.172 - 184

Received: 30 Apr 2012
Accepted: 16 Oct 2012

Published online: 18 Mar 2015 *

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