title: Identifying node role in social network based on multiple indicators.
abstract: It is a classic topic of social network analysis to evaluate the importance of nodes and identify the node that takes on the role of core or bridge in a network. Because a single indicator is not sufficient to analyze multiple characteristics of a node, it is a natural solution to apply multiple indicators that should be selected carefully. An intuitive idea is to select some indicators with weak correlations to efficiently assess different characteristics of a node. However, this paper shows that it is much better to select the indicators with strong correlations. Because indicator correlation is based on the statistical analysis of a large number of nodes, the particularity of an important node will be outlined if its indicator relationship doesn't comply with the statistical correlation. Therefore, the paper selects the multiple indicators including degree, ego-betweenness centrality and eigenvector centrality to evaluate the importance and the role of a node. The importance of a node is equal to the normalized sum of its three indicators. A candidate for core or bridge is selected from the great degree nodes or the nodes with great ego-betweenness centrality respectively. Then, the role of a candidate is determined according to the difference between its indicators' relationship with the statistical correlation of the overall network. Based on 18 real networks and 3 kinds of model networks, the experimental results show that the proposed methods perform quite well in evaluating the importance of nodes and in identifying the node role.
authors.affiliation: College of Computer Science and Technology, Harbin Engineering University, Harbin, China.
authors.forename: Shaobin
authors.initials: S
authors.lastname: Huang
authors.affiliation: College of Computer Science and Technology, Harbin Engineering University, Harbin, China; College of Computer Science and Technology, Tsinghua University, Beijing, China; Audit Research Institute, National Audit Office, Beijing, China.
authors.forename: Tianyang
authors.initials: T
authors.lastname: Lv
authors.affiliation: College of Information Science and Engineering, Northeastern University, Shenyang, China.
authors.forename: Xizhe
authors.initials: X
authors.lastname: Zhang
authors.affiliation: College of Computer Science and Technology, Harbin Engineering University, Harbin, China.
authors.forename: Yange
authors.initials: Y
authors.lastname: Yang
authors.affiliation: College of Computer Science and Technology, Tsinghua University, Beijing, China.
authors.forename: Weimin
authors.initials: W
authors.lastname: Zheng
authors.affiliation: College of Information Science and Engineering, Northeastern University, Shenyang, China.
authors.forename: Chao
authors.initials: C
authors.lastname: Wen
mesh_terms: D000465:Algorithms; D000818:Animals; D006801:Humans; D012944:Social Support; D018709:Statistics, Nonparametric
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