C-index: A weighted network node centrality measure for collaboration competence |
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| Authors: | Xiangbin Yan Li Zhai Weiguo Fan |
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| Institution: | 1. School of Management, Harbin Institute of Technology, Harbin 150001, China;2. School of Applied Science, Harbin University of Science and Technology, Harbin 150080, China;3. Pamplin College of Business, Virginia Tech, Blacksburg, VA 24061, United States |
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| Abstract: | This paper proposes a new node centrality measurement index (c-index) and its derivative indexes (iterative c-index and cg-index) to measure the collaboration competence of a node in a weighted network. We prove that c-index observe the power law distribution in the weighted scale-free network. A case study of a very large scientific collaboration network indicates that the indexes proposed in this paper are different from other common centrality measures (degree centrality, betweenness centrality, closeness centrality, eigenvector centrality and node strength) and other h-type indexes (lobby-index, w-lobby index and h-degree). The c-index and its derivative indexes proposed in this paper comprehensively utilize the amount of nodes’ neighbors, link strengths and centrality information of neighbor nodes to measure the centrality of a node, composing a new unique centrality measure for collaborative competency. |
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