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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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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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Betweenness Centrality in Graphs
2014The first book devoted exclusively to quantitative graph theory, Quantitative Graph Theory: Mathematical Foundations and Applications presents and demonstrates existing and novel methods for analyzing graphs quantitatively. Incorporating interdisciplinary knowledge from graph theory, information theory, measurement theory, and statistical techniques ...
Gago Álvarez, Silvia +2 more
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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 Approaches for Image Retrieval
2015 IEEE International Symposium on Multimedia (ISM), 2015To quantify social tags' relatedness in an image collection, we examine the betweenness centrality measure. We depict the image collection as a multi-graph representation, where nodes are the social tags and edges bind an image's social tags. We present our weighted betweenness centrality algorithm and compare it to the unweighted version on sparse and
Brandeis Marshall +3 more
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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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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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Betweenness centrality on GPUs and heterogeneous architectures
Proceedings of the 6th Workshop on General Purpose Processor Using Graphics Processing Units, 2013The betweenness centrality metric has always been intriguing for graph analyses and used in various applications. Yet, it is one of the most computationally expensive kernels in graph mining. In this work, we investigate a set of techniques to make the betweenness centrality computations faster on GPUs as well as on heterogeneous CPU/GPU architectures.
Ahmet Erdem Sariyüce +3 more
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An approximation of betweenness centrality for Social Networks
Proceedings of the 2015 IEEE 9th International Conference on Semantic Computing (IEEE ICSC 2015), 2015A challenge in the research of Social Networks is the large scale analysis of graphs. One of the most valuable metrics in the evaluation of graphs is betweenness-centrality. In this paper, we define an approximation of betweenness-centrality for the purpose of building a predictive model of Social Networks. The methodology presented describes a bounded
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