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The delta-betweenness centrality
2013 IEEE 24th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC), 2013In this paper we consider the extension of the betweenness centrality measure which is used in social and computer communication networks to estimate the potential monitoring and control capabilities a node may have on data flowing in the network.
Alexander Plutov, Michael Segal 0001
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Distributed estimation of betweenness centrality
2015 53rd Annual Allerton Conference on Communication, Control, and Computing (Allerton), 2015Betweenness centrality is a fundamental centrality measure that quantifies how important a node or an edge is, within a network, based on how often it lies on the shortest paths between all pairs of nodes. In this paper, we develop a scalable distributed algorithm, which enables every node in a network to estimate its own betweenness and the ...
Wei Wang 0069, Choon Yik Tang
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Approximating Betweenness Centrality
2007Betweenness is a centrality measure based on shortest paths, widely used in complex network analysis. It is computationally-expensive to exactly determine betweenness; currently the fastest-known algorithm by Brandes requires O(nm) time for unweighted graphs and O(nm + n2 log n) time for weighted graphs, where n is the number of vertices and m is the ...
David A. Bader +3 more
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Between centrality and anxiety
Asian Journal of Comparative Politics, 2016China’s centrality to Australia’s economy, migration, tourism, and student population is obvious today and likely to continue. And yet there appears to be anxiety among some in Australia about China’s ‘rise’, especially its growing military power, seemingly aggressive behaviour in disputed maritime space, global economic influence, and apparent quest ...
Purnendra Jain, Gregory McCarthy
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Fully Dynamic Betweenness Centrality
2015We present fully dynamic algorithms for maintaining betweenness centrality (BC) of vertices in a directed graph \(G=(V,E)\) with positive edge weights. BC is a widely used parameter in the analysis of large complex networks. We achieve an amortized \(O({\nu ^*}^2 \cdot \log ^3 n)\) time per update with our basic algorithm, and \(O({\nu ^*}^2 \cdot \log
Matteo Pontecorvi, Vijaya Ramachandran
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A Parallel Algorithm for Computing Betweenness Centrality
2009 International Conference on Parallel Processing, 2009In this paper we present a multi-grained parallel algorithm for computing betweenness centrality, which is extensively used in large-scale network analysis. Our method is based on a novel algorithmic handling of access conflicts for a CREW PRAM algorithm. We propose a proper data-processor mapping, a novel edge-numbering strategy and a new triple array
Guangming Tan, Dengbiao Tu, Ninghui Sun
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A stable betweenness centrality measure in networks
2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2014This paper presents a formal definition of stability for node centrality measures in networks and shows that the well-known betweenness centrality is not stable with respect to that metric. An alternative definition that preserves the same centrality notion while satisfying this stability criterion is then introduced.
Santiago Segarra, Alejandro Ribeiro
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Betweenness centrality on Multi-GPU systems
Proceedings of the 5th Workshop on Irregular Applications: Architectures and Algorithms, 2015Betweenness Centrality (BC) is steadily growing in popularity as a metrics of the influence of a vertex in a graph. The exact BC computation for a large scale graph is an extraordinary challenging and requires high performance computing techniques to provide results in a reasonable amount of time.
BERNASCHI, Massimo +2 more
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Out-degree betweenness centrality based on betweenness centrality
International Conference on Cryptography, Network Security, and Communication Technology (CNSCT 2023), 2023Zhe Yang +3 more
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Computing Betweenness Centrality in B-hypergraphs
Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, 2017The directed hypergraph (especially B-hypergraph) has hyperedges that represent relations of a set of source nodes to a single target node. Author-cited networks and cellular signaling pathways can be modeled as a B-hypergraph. In this paper every source node of a hyperedge in the shortest path p in a B-hypergraph is considered a participant of p.
Kwang Hee Lee, Myoung-Ho Kim
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