Mining top-k influential nodes in social networks via community detection
by Wei Li; Jianbin Huang; Shuzhen Wang
International Journal of Information Technology and Management (IJITM), Vol. 14, No. 2/3, 2015

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.

Online publication date: Sat, 04-Apr-2015

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