Identifying influential nodes in social networks via community structure and influence distribution

• 作 者: Zufan Zhang Xieliang Li Chenquan Gan
• 机 构: School of Communication and Information Engineering, Chongqing University of Posts and Telecommunica
• 出 处: 数字通信与网络（英文版）
• 发表日期: 2021年1期
• 关键词: Social network Community detection Influence maximization Network embedding Infl

This paper aims to effectively solve the problem of the influence maximization in social networks.For this purpose,an influence maximization method that can identify influential nodes via the community structure and the influence distribution difference is proposed.Firstly,the network embedding-based community detection approach is developed,by which the social network is divided into several high-quality communities.Secondly,the solution of influence maximization is composed of the candidate stage and the greedy stage.The candidate stage is to select candidate nodes from the interior and the boundary of each community using a heuristic algorithm,and the greedy stage is to determine seed nodes with the largest marginal influence increment from the candidate set through the sub-modular property-based Greedy algorithm.Finally,experimental results demonstrate the superiority of the proposed method compared with existing methods,from which one can further find that our work can achieve a good tradeoff between the influence spread and the running time.