Identifying influential spreaders by weighted LeaderRank

2013/06/30 by Qian Li, Tao Zhou, Linyuan Lü +2 · 2 citations
Computer Science · Physics and Astronomy · #Algorithm #Artificial intelligence #Complex Network Analysis Techniques #Computer science #Data mining #Opinion Dynamics and Social Influence #Ranking (information retrieval) #Robustness (evolution) #Spam and Phishing Detection #cs.SI #physics.data-an #physics.soc-ph

paper · pdf · doi:10.1016/j.physa.2014.02.041

published as Physica A 404 (2014) 47-55 · 15 pages and 8 figures

arxiv created 2013/11/28 · openalex publication_date 2014/02/22 · arxiv updated 2015/01/16 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05

Abstract

Identifying influential spreaders is crucial for understanding and controlling spreading processes on social networks. Via assigning degree-dependent weights onto links associated with the ground node, we proposed a variant to a recent ranking algorithm named LeaderRank [L. Lv et al., PLoS ONE 6 (2011) e21202]. According to the simulations on the standard SIR model, the weighted LeaderRank performs better than LeaderRank in three aspects: (i) the ability to find out more influential spreaders, (ii) the higher tolerance to noisy data, and (iii) the higher robustness to intentional attacks.

Citations

• The Centrality Index of a Graph
• A NEW MEASURE OF RANK CORRELATION
• Immunization of complex networks
• Centrality in social networks conceptual clarification
• The anatomy of a large-scale hypertextual Web search engine
• Diffusion of scientific credits and the ranking of scientists
• Identification of influential spreaders in complex networks

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