Premium
Influence maximization based on activity degree in mobile social networks
Author(s) -
Gao Min,
Xu Li,
Lin Limei,
Huang Yanze,
Zhang Xinxin
Publication year - 2020
Publication title -
concurrency and computation: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.309
H-Index - 67
eISSN - 1532-0634
pISSN - 1532-0626
DOI - 10.1002/cpe.5677
Subject(s) - betweenness centrality , centrality , maximization , computer science , influencer marketing , mobile social network , graph , degree (music) , ranking (information retrieval) , pagerank , set (abstract data type) , node (physics) , theoretical computer science , data mining , artificial intelligence , mathematics , mobile computing , mathematical optimization , computer network , statistics , engineering , physics , acoustics , programming language , structural engineering , marketing , relationship marketing , business , marketing management
Summary The problem of influence maximization (IM) has become an important research topic due to the rapid growth of mobile social networks. It attempts to identify a set of nodes, referred to as influencers, contributing to the spread of maximum information. In this article, we present the construction of social relation graph based on mobile communication data. And we propose a new centrality measure—activity degree to characterize the activity of nodes. By combining the local attributes of nodes and the behavioral characteristics of nodes to measure node activity degree, which can be used to evaluate the influence of users in mobile social networks, we introduce Susceptible‐Infected‐Susceptible model to simulate the dynamic spreading of information. We take advantage of the two indicators the degree centrality and the betweenness centrality to get a better ranking results. In comparison with spanning graph and initial graph, the results of comparison demonstrate that our algorithm has advantages in the scope of influence propagation.