Algorithms for estimating relative importance in networks
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Parallel Algorithms for Evaluating Centrality Indices in Real-world Networks
ICPP '06 Proceedings of the 2006 International Conference on Parallel Processing
Betweenness centrality as an indicator of the interdisciplinarity of scientific journals
Journal of the American Society for Information Science and Technology
IPDPS '09 Proceedings of the 2009 IEEE International Symposium on Parallel&Distributed Processing
Approximating betweenness centrality
WAW'07 Proceedings of the 5th international conference on Algorithms and models for the web-graph
Betweenness centrality on GPUs and heterogeneous architectures
Proceedings of the 6th Workshop on General Purpose Processor Using Graphics Processing Units
Discovering influential authors in heterogeneous academic networks by a co-ranking method
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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Social networks have demonstrated in the last few years to be a powerful and flexible concept useful to represent and analyze data emerging form social interactions and social activities. The study of these networks can thus provide a deeper understanding of many emergent global phenomena. The amount of data available in the form of social networks data is growing by the day, and this poses many computational challenging problems for their analysis. In fact many analysis tools suitable to analyze small to medium sized networks are inefficient for large social networks. The computation of the betweenness centrality index is a well established method for network data analysis and it is also important as subroutine in more advanced algorithms, such as the Girvan-Newman method for graph partitioning. In this paper we present a new approach for the computation of the betweenness centrality, which speeds up considerably Brandes' algorithm (the current state of the art) in the context of social networks. Our approach exploits the natural sparsity of the data to algebraically (and efficiently) determine the betweenness of those nodes forming trees (tree-nodes) in the social network. Moreover, for the residual network, which is often of much smaller size, we modify directly the Brandes' algorithm so that we can remove the nodes already processed and perform the computation of the shortest paths only for the residual nodes. Tests conducted on a sample of publicly available large networks from the Stanford repository show that improvements of a factor ranging between 2 and 5 are possible on several such graphs, when the sparsity, measured by the ratio of tree-nodes to the total number of nodes, is in a medium range (30% to 50%). For some large networks from the Stanford repository and for a sample of social networks provided by Sistemi Territoriali with high sparsity (80% and above) tests show that our algorithm consistently runs between one and two orders of magnitude faster than the current state of the art exact algorithm.