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

Wei Li, Jianbin Huang and Shuzhen Wang

International Journal of Information Technology and Management, 2015, vol. 14, issue 2/3, 172-184

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. (search for similar items in EconPapers)
Date: 2015
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Persistent link: https://EconPapers.repec.org/RePEc:ids:ijitma:v:14:y:2015:i:2/3:p:172-184

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