C-index: A weighted network node centrality measure for collaboration competence

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.

► We propose a new node centrality measurement index in weighted network.
► c-Index observe the power law distribution in weighted scale-free network.
► The c-index and its derivative indexes producing more accurately utilized information.
► The indexes composing a new unique centrality measure for collaborative competency.