An influence maximisation algorithm based on community detection. (4th May 2020)

• Record Type: Journal Article
• Authors: Yuan, Yan; Chen, Bolun; Yu, Yongtao; Jin, Ying
• Is Part Of: International journal of computational science and engineering. Volume 22:Number 1(2020)
• Page Start: 1
• Page End: 14
• Publication Date: 2020-05-04
• Subjects: community detection -- modularity -- influence maximisation -- social network -- k-means algorithm; Computer science -- Mathematics -- Periodicals; Computer simulation -- Mathematical aspects -- Periodicals; Computational intelligence -- Periodicals; 004.015105
• Languages: English
• ISSNs: 1742-7185

Abstract

Influence maximisation is an important research direction in social networks. The main goal of this approach is to select seed nodes in the network to maximise the propagated influence. Because the influence maximisation is an NP-hard problem, existing studies have provided approximate solutions, and the research focuses on the framework of greed, but the time complexity of the greedy algorithm is high. In this study, an influence maximisation algorithm based on community detection is proposed. This algorithm uses the K-means algorithm to divide the community. According to the modularity, the optimal community segmentation result is selected. By calculating the edge betweenness of each community, some nodes are selected as important nodes. The important nodes of each community constitute the set of seed nodes used in the influence maximisation algorithm. Experiments show that the algorithm not only has an improved influence, but also the time complexity is effectively reduced.