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• W296472135 abstract "Bipartite networks have gained an increasing amount of attention over thepast few years. Network measures in particular, have been the focus of thisresearch as many of them cannot be directly applied to bipartite networks. Theclustering coefficient is one measure that has been redefined recently to suitthe analysis of bipartite networks. Building up on this definition, we proposea clustering coefficient that distinguishes between differently structuredbipartite clusters. We use this measure to identify influential nodes in agiven bipartite network. By comparing the global and local clusteringcoefficients, we assign a score to each node that indicates the extent to whichit drives the clustering behaviour of the whole network. We demonstrate thatour clustering coefficient is not only able to identify influential nodes, butgives new insights into a network's structure." @default.
• W296472135 created "2016-06-24" @default.
• W296472135 creator A5003002518 @default.
• W296472135 creator A5071957313 @default.
• W296472135 date "2014-06-23" @default.
• W296472135 modified "2023-09-26" @default.
• W296472135 title "A Clustering Coefficient to Identify Important Nodes in Bipartite Networks." @default.
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• W296472135 hasPublicationYear "2014" @default.
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