Community-based greedy algorithm for mining top-K influential nodes in mobile social networks

By Yu Wang, Gao Cong, Guojie Song, Kunqing Xie. Knowledge Discovery and Data Mining, 2010

583 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/4f4e1fab4ae110423e0c1236fcf5ffd35a2845fb

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Tao Zhou
2. Linyuan Lu
3. Yi-Cheng Zhang
4. Kyomin Jung
5. Wooram Heo
6. Xiaokui Xiao
7. Youze Tang
8. Yanchen Shi

In Statistical and Nonlinear Physics

1. Youze Tang
2. Yanchen Shi
3. Xiaokui Xiao
4. Duanbing Chen
5. Tao Zhou
6. Linyuan Lu
7. Kian-Lee Tan
8. Ju Fan

Citations in the corpus

• Stop-and-Stare: Optimal Sampling Algorithms for Viral Marketing in Billion-scale Networks (2016, background): “The absolute superiority of SSA and D-SSA are confirmed through extensive experiments on real network data for IM and another topic-aware viral marketing problem, named TVM. Keywords Influence Maximization; Stop-and-Stare; Sampling”
• Influence maximization: near-optimal time complexity meets practical efficiency (2014, background): “[32] propose to divide the social network into smaller communities, and then identify influential nodes from each community individually; Goyal et al.”
• Influence Maximization in Social Networks When Negative Opinions May Emerge and Propagate (2011, methodology): “…with a standard greedy approximation algorithm while being orders of magnitude faster. keywords: influence maximization; social networks; negative opinions; independent cascade model; ∗Author affiliations and emails: W. Chen (contact author), Microsoft Research Asia, China, weic@microsoft.com.”
• Influence Blocking Maximization in Social Networks under the Competitive Linear Threshold Model (2011, background): “Many follow-up studies propose alternative heuristics and try to solve the influence maximization problem more efficiently [16, 19, 6, 5, 7, 25].”
• Vital nodes identification in complex networks (2016, methodology): “Till far, there exists a huge number of extensions of the original greedy algorithm by considering the variants of the spreading dynamics [248], by making use of the structural properties of the target networks [249], by integrating advanced optimization methods developed for related problems [250],”
• IRIE: Scalable and Robust Influence Maximization in Social Networks (2011, methodology): “To estimate influence function σ(S) efficiently, Monte-Carlo simulation and other heuristics have been used in various previous works [1], [5], [7], [6], [3], [12], [8], [7], [16].”
• Information and Influence Propagation in Social Networks (2013, methodology): “For MIA and PMIA, we set the threshold to 1=320 as suggested by Wang et al. [2012]. e influence spread results (Figures 3.7 and 3.8) show that PMIA algorithm almost matches LazyGreedyŒ20000 algorithm in influence spread, and it performs slightly better than MIA algorithm. Comparing the running tim”
• Influence Maximization on Social Graphs: A Survey (2018, methodology): “Wang et al. [58] propose an orthogonal approach called Community based Greedy algorithm (CGA) using divide-and-conquer to reduce the complexity for MC simulations.”
• Maximizing Social Influence in Nearly Optimal Time (2012, background): “These have focused largely on heuristics, such as assuming that all nodes have relatively low influence or that the input graph is clustered [12, 14, 25, 38], as well as empirically-motivated implementation improvements [27, 13].”