Combined Centrality Measures for an Improved Characterization of Influence Spread in Social Networks

Abstract

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

References in corpus (8)

• Epidemic spreading in scale-free networks
• Identifying influential spreaders in complex networks based on gravity formula
• A k-shell decomposition method for weighted networks
• Adaptive Influence Maximization in Dynamic Social Networks
• Identify influential spreaders in complex networks, the role of neighborhood
• Epidemic Spreading on Weighted Complex Networks
• Effector Detection in Social Networks
• Simulating SIR processes on networks using weighted shortest paths

Cited by in corpus (3)

• Lexical Sorting Centrality to Distinguish Spreading Abilities of Nodes in Complex Networks under the Susceptible-Infectious-Recovered (SIR) Model
• Trusting code in the wild: Exploring contributor reputation measures to review dependencies in the Rust ecosystem
• Estimating the Expected Influence Capacities of Nodes in Complex Networks under the Susceptible-Infectious-Recovered (SIR) Model