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Journal of the ACM (JACM)
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Approximating betweenness centrality
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k-Centralities: local approximations of global measures based on shortest paths
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Social network restructuring after a node removal
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Analysing the Use of Ontologies Based on Usage Network
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Betweenness centrality on GPUs and heterogeneous architectures
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A generic topology discovery approach for huge social networks
Proceedings of the 5th ACM COMPUTE Conference: Intelligent & scalable system technologies
Incremental closeness centrality for dynamically changing social networks
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Closeness centrality is an important concept in social network analysis. In a graph representing a social network, closeness centrality measures how close a vertex is to all other vertices in the graph. In this paper, we combine existing methods on calculating exact values and approximate values of closeness centrality and present new algorithms to rank the top-kvertices with the highest closeness centrality. We show that under certain conditions, our algorithm is more efficient than the algorithm that calculates the closeness-centralities of all vertices.