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

Overview of attention for article published in PLOS ONE, October 2013
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Title
Identifying Influential Nodes in Large-Scale Directed Networks: The Role of Clustering
Published in
PLOS ONE, October 2013
DOI 10.1371/journal.pone.0077455
Pubmed ID
Authors

Duan-Bing Chen, Hui Gao, Linyuan Lü, Tao Zhou

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 [Formula: see text] nodes, more than 15 times faster than PageRank.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 161 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Mexico 1 <1%
India 1 <1%
China 1 <1%
Norway 1 <1%
Unknown 157 98%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 43 27%
Student > Master 39 24%
Researcher 21 13%
Student > Bachelor 9 6%
Student > Doctoral Student 8 5%
Other 21 13%
Unknown 20 12%
Readers by discipline Count As %
Computer Science 59 37%
Engineering 16 10%
Physics and Astronomy 11 7%
Agricultural and Biological Sciences 8 5%
Social Sciences 7 4%
Other 31 19%
Unknown 29 18%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 02 November 2013.
All research outputs
#3,065,904
of 4,507,509 outputs
Outputs from PLOS ONE
#55,992
of 80,038 outputs
Outputs of similar age
#68,458
of 102,667 outputs
Outputs of similar age from PLOS ONE
#2,948
of 3,950 outputs
Altmetric has tracked 4,507,509 research outputs across all sources so far. This one is in the 20th percentile – i.e., 20% of other outputs scored the same or lower than it.
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