Identifying Influential Nodes in Large-Scale Directed Networks: The Role of Clustering

By Duanbing Chen, Hui Gao, Linyuan Lu, Tao Zhou. PLoS ONE, 2013

347 citations recorded by the source corpus.

Identifying influential nodes in very large-scale directed networks is a big challenge relevant to disparate applications, such as accelerating information propagation, controlling rumors and diseases, designing search engines, and understanding hierarchical organization of social and biological networks. Known methods range from node centralities, such as degree, closeness and betweenness, to diffusion-based processes, like PageRank and LeaderRank. Some of these methods already take into account the influences of a node’s neighbors but do not directly make use of the interactions among it’s neighbors. Local clustering is known to have negative impacts on the information spreading. We further show empirically that it also plays a negative role in generating local connections. Inspired by these facts, we propose a local ranking algorithm named ClusterRank, which takes into account not only the number of neighbors and the neighbors’ influences, but also the clustering coefficient. Subject to the susceptible-infected-recovered (SIR) spreading model with constant infectivity, experimental results on two directed networks, a social network extracted from delicious.com and a large-scale short-message communication network, demonstrate that the ClusterRank outperforms some benchmark algorithms such as PageRank and LeaderRank. Furthermore, ClusterRank can also be applied to undirected networks where the superiority of ClusterRank is significant compared with degree centrality and k-core decomposition. In addition, ClusterRank, only making use of local information, is much more efficient than global methods: It takes only 191 seconds for a network with about nodes, more than 15 times faster than PageRank.

Subfields: Communication, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/140ee985794cadac30cde355056228026ec05d8f

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In Statistical and Nonlinear Physics

Citations in the corpus

Identifying a set of influential spreaders in complex networks (2016, methodology): “It is worth mentioning that VoteRank outperforms K-shellRank on undirected network, and also outperforms other benchmark algorithms such as PageRank, ClusterRank and IndegreeRank on directed network.”
Vital nodes identification in complex networks (2016, methodology): “Although this method is very simple with the decaying factor 1 〈k〉 determined in a completely ad hoc way, it performs better than the ClusterRank [36] and coreness [37] according to simulations in the SIR model.”