摘要： In recent years, due to the surge in popularity of social-networking web sites, considerable interest has arisen regarding influence maximization in social networks. Given a social network structure, the problem of influence maximization is to determine a minimum set of nodes that could maximize the spread of influences. With a large-scale social network, the efficiency and practicability of such algorithms are critical. Although many recent studies have focused on the problem of influence maximization, these works in general are time-consuming when a social network is large-scale. In this paper, we propose two novel algorithms, CDH-Kcut and Community and Degree Heuristic on Kcut/SHRINK, to solve the influence maximization problem based on a realistic model. The algorithms utilize the community structure, which significantly decreases the number of candidates of influential nodes, to avoid information overlap. The experimental results on both synthetic and real datasets indicate that our algorithms not only significantly outperform the state-of-the-art algorithms in efficiency but also possess graceful scalability. 其他題名： Knowl Inf Syst 出版者： London: Springer-Verlag 出版日期： 2012-12-01 出處： Knowledge and information systems, 2012-12, Vol.33 (3), p.577-601 資源來源： ABI/INFORM Collection (via ProQuest) 版權： Springer-Verlag London Limited 2012 版權： 2015 INIST-CNRS 版權： Springer-Verlag London 2012 識別號： ISSN: 0219-1377 識別號： EISSN: 0219-3116 識別號： DOI: 10.1007/s10115-012-0540-7 識別號： CODEN: KISNCR
