CBIM-RSRW: An Community-Based Method for Influence Maximization in Social Network

Abstract

Influence maximization is an important problem, which seeks a small set of key users who spread the influence widely into the network. It finds applications in viral marketing, epidemic control, and assessing cascading failures within complex systems. The current studies treat nodes in social network with equal weights, and the influence possibility mainly decide by node degree. In this paper, we study the influence maximization problem in social networks and we improve the independent cascade model to realize the goal of different weights for different users, and the differentiation of influence probability. Meanwhile, We take advantage of the community structure to speed up the algorithm. Then, we propose a method called the reverse reachable index method based on random walk (RSRW) to select potential high-impact nodes from those communities. The experimental result on four actual data set shows that these improvements can greatly reduce the calculation time while ensuring the accuracy of the results.