An Efficient Memetic Algorithm for Influence Maximization in Social Networks

作者：Gong, Maoguo^*; Song, Chao; Duan, Chao; Ma, Lijia; Shen, Bo

来源：IEEE Computational Intelligence Magazine, 2016, 11(3): 23-34.

DOI：10.1109/MCI.2016.2572538

• COMMUNITY
• COMPUTATION

Influence maximization is to extract a small set of nodes from a social network which influences the propagation maximally under a cascade model. In this paper, we propose a memetic algorithm for community-based influence maximization in social networks. The proposed memetic algorithm optimizes the 2-hop influence spread to find the most influential nodes. Problem-specific population initialization and similarity-based local search are designed to accelerate the convergence of the algorithm. Experiments on three real-world datasets demonstrate that our algorithm has competitive performances to the comparing algorithms in terms of effectiveness and efficiency. For example, on a real-world network of 15233 nodes and 58891 edges, the influence spread of the proposed algorithm is 12.5%, 13.2% and 173.5% higher than the three comparing algorithms Degree, PageRank and Random, respectively.

• 出版日期2016-8
• 单位西安电子科技大学