Identifying influential nodes in large-scale directed networks: the role of clustering
TLDR
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.Abstract:
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.read more
Citations
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Journal ArticleDOI
Vital nodes identification in complex networks
Linyuan Lü,Linyuan Lü,Duanbing Chen,Xiao-Long Ren,Qian-Ming Zhang,Yi-Cheng Zhang,Yi-Cheng Zhang,Tao Zhou +7 more
TL;DR: In this paper, the state-of-the-art algorithms for vital node identification in real networks are reviewed and compared, and extensive empirical analyses are provided to compare well-known methods on disparate real networks.
Journal ArticleDOI
Vital nodes identification in complex networks
Linyuan Lü,Linyuan Lü,Duanbing Chen,Xiao-Long Ren,Qian-Ming Zhang,Yi-Cheng Zhang,Yi-Cheng Zhang,Tao Zhou +7 more
TL;DR: This review clarifies the concepts and metrics, classify the problems and methods, as well as review the important progresses and describe the state of the art, and provides extensive empirical analyses to compare well-known methods on disparate real networks and highlight the future directions.
Journal ArticleDOI
The H-index of a network node and its relation to degree and coreness
TL;DR: A family of H-indices are obtained that can be used to measure a node's importance and it is proved that the convergence to coreness can be guaranteed even under an asynchronous updating process, allowing a decentralized local method of calculating a nodes' coreness in large-scale evolving networks.
Journal ArticleDOI
Identifying influential spreaders by weighted LeaderRank
TL;DR: According to the simulations on the standard SIR model, the weighted LeaderRank performs better than LeaderRank in three aspects: the ability to find out more influential spreaders; the higher tolerance to noisy data; and the higher robustness to intentional attacks.
Journal ArticleDOI
Identifying a set of influential spreaders in complex networks
TL;DR: VoteRank is presented, a simply yet effectively iterative method named VoteRank to identify a set of decentralized spreaders with the best spreading ability, and experimental results show that under Susceptible-Infected-Recovered (SIR) and Susceptibles Infected (SI) models, VoteRank outperforms the traditional benchmark methods on both spreading rate and final affected scale.
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