Combined Centrality Measures for an Improved Characterization of Influence Spread in Social Networks
Mehmet Simsek, Henning Meyerhenke
发表信息：
Mehmet Simsek, Henning Meyerhenke, Combined centrality measures for an improved characterization of influence spread in social networks, Journal of Complex Networks, Volume 8, Issue 1, February 2020,
Influence Maximization (IM) aims at finding the most influential users in a social network, i. e., users who maximize the spread of an opinion within a certain propagation model. Previous work investigated the correlation between influence spread and nodal centrality measures to bypass more expensive IM simulations. The results were promising but incomplete, since these studies investigated the performance (i. e., the ability to identify influential users) of centrality measures only in restricted settings, e. g., in undirected/unweighted networks and/or within a propagation model less common for IM. In this paper, we first show that good results within the Susceptible- Infected-Removed (SIR) propagation model for unweighted and undirected networks do not necessarily transfer to directed or weighted networks under the popular Independent Cascade (IC) propagation model. Then, we identify a set of centrality measures with good performance for weighted and directed networks within the IC model. Our main contribution is a new way to combine the centrality measures in a closed formula to yield even better results. Additionally, we also extend gravitational centrality (GC) with the proposed combined centrality measures. Our experiments on 50 real-world data sets show that our proposed centrality measures outperform well-known centrality measures and the state-of-the art GC measure significantly. social networks, influence maximization, centrality measures, IC propagation model, influential spreaders
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Distance Backbones Optimize Spreading Dynamics and Centrality Ranks in the Sparsification of Complex Networks
Politecnico di Torino（都灵理工大学）；CENTAI Institute（CENTAI研究所）；Universidade Católica Portuguesa, Católica Medical School, Católica Biomedical Research Centre（葡萄牙天主教大学，天主教医学院，天主教生物医学研究中心）；Binghamton University (State University of New York)（宾厄姆顿大学（纽约州立大学））
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