¹Department of Computer Science, Yangzhou University, Yangzhou, 225127, China.
²State Key Lab of Novel Software Tech, Nanjing University, Nanjing, 210093, China.

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Abstract—Influence is an important factor in modern social network analysis. The spread of influence in social networks has become a key focus of research on data mining and social networking. The goal of influence maximization is to find a collection of nodes in a network which can spread the most influence in the network. This paper presents a node covering based method for influence maximization on linear threshold model. We define the activation set for each node in the network. The algorithm calculates the activation set for each node, and then obtain the seed set by finding out the maximum coverage set of the activate sets of all the nodes. We also present a breadth first search algorithm for calculating the activation sets. A greedy algorithm for calculating the maximum coverage set is also proposed. Our experimental results show that the algorithm can effectively select the suitable seed set to maximize the influence propagation.

Index Terms—Influence maximization, propagation model, social influence.

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Cite: Weijia Ju, Ling Chen, "A Node Covering Based Method for Influence Maximization on Linear Threshold Model," Journal of Software vol. 12, no. 11, pp. 840-848, 2017.