LAUSR

Identifying a small subset of influential individuals on social networks can bring great benefits for many practical applications like viral marketing. This issue is typically formulated as the influence maximization problem. As a fundamental research topic in social network analysis, influence maximization has attracted much attention in recent years. In general, traditional influence maximization algorithms can be classified into two categories: 1) greedy algorithms, which possess high-performance guarantee but are time-consuming and 2) heuristic algorithms, which are time-efficient but lack performance guarantee. In this paper, we first propose a community detection approach based on network embedding to detect the community structures of social networks. With the aid of these community structures, we then propose two novel and robust community-based approximation algorithms, basic community-based robust influence maximization (BCRIM) and improved community-based robust influence maximization (ICRIM), to combat the problem of influence maximization. Both BCRIM and ICRIM have high-performance guarantee as well as high efficiency, while ICRIM runs even faster than BCRIM. Specifically, BCRIM and ICRIM identify influential individuals within communities rather than the entire network. The influence scope of each individual in BCRIM and ICRIM is restricted to its community and its neighbors’ communities; thus, they are able to simultaneously identify influential individuals within communities and important hub or bridge individuals that connect different communities together. Furthermore, we analyze the performance guarantee of BCRIM and ICRIM in detail. Finally, we conduct extensive experiments on five benchmark networks to evaluate the performance of the proposed algorithms.