Shattering and Compressing Networks for Betweenness Centrality

The betweenness metric has always been intriguing and used in many analyses. Yet, it is one of the most computationally expensive kernels in graph mining. For that reason, making betweenness centrality computations faster is an important and well-studied problem. In this work, we propose the framework, BADIOS, which compresses a network and shatters it into pieces so that the centrality computation can be handled independently for each piece. Although BADIOS is designed and tuned for betweenness centrality, it can easily be adapted for other centrality metrics. Experimental results show that the proposed techniques can be a great arsenal to reduce the centrality computation time for various types and sizes of networks. In particular, it reduces the computation time of a 4.6 million edges graph from more than 5 days to less than 16 hours.

[1]  John R. Gilbert,et al.  A Flexible Open-Source Toolbox for Scalable Complex Graph Analysis , 2012, SDM.

[2]  Andrew G. Barto,et al.  Skill Characterization Based on Betweenness , 2008, NIPS.

[3]  Christos Faloutsos,et al.  Beyond 'Caveman Communities': Hubs and Spokes for Graph Compression and Mining , 2011, 2011 IEEE 11th International Conference on Data Mining.

[4]  Miriam Baglioni,et al.  Fast Exact Computation of betweenness Centrality in Social Networks , 2012, 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining.

[5]  David A. Bader,et al.  National Laboratory Lawrence Berkeley National Laboratory Title A Faster Parallel Algorithm and Efficient Multithreaded Implementations for Evaluating Betweenness Centrality on Massive Datasets Permalink , 2009 .

[6]  Ulrik Brandes,et al.  Centrality Estimation in Large Networks , 2007, Int. J. Bifurc. Chaos.

[8]  Ümit V. Çatalyürek,et al.  Shattering and Compressing Networks for Centrality Analysis , 2012, ArXiv.

[9]  Bing Zhang,et al.  Fast network centrality analysis using GPUs , 2011, BMC Bioinformatics.

[10]  U. Brandes.  A faster algorithm for betweenness centrality , 2001 .

[11]  F. Schreiber,et al.  Centrality Analysis Methods for Biological Networks and Their Application to Gene Regulatory Networks , 2008, Gene regulation and systems biology.

[12]  Jared Hoberock,et al.  Edge v. Node Parallelism for Graph Centrality Metrics , 2012 .

[13]  Leonard M. Freeman,et al.  A set of measures of centrality based upon betweenness , 1977 .

[14]  Valdis E. Krebs,et al.  Mapping Networks of Terrorist Cells , 2001 .

[15]  E. Cuthill,et al.  Reducing the bandwidth of sparse symmetric matrices , 1969, ACM '69.

[16]  Peter Sanders,et al.  Better Approximation of Betweenness Centrality , 2008, ALENEX.

[17]  Robert E. Tarjan,et al.  A Note on Finding the Bridges of a Graph , 1974, Inf. Process. Lett..

[18]  Shiva Kintali,et al.  Betweenness Centrality : Algorithms and Lower Bounds , 2008, ArXiv.

[19]  Ulrik Brandes,et al.  On variants of shortest-path betweenness centrality and their generic computation , 2008, Soc. Networks.

[20]  Guojing Cong,et al.  Optimizing Large-scale Graph Analysis on Multithreaded, Multicore Platforms , 2011, 2012 IEEE 26th International Parallel and Distributed Processing Symposium.

[21]  Pak Chung Wong,et al.  A novel application of parallel betweenness centrality to power grid contingency analysis , 2010, 2010 IEEE International Symposium on Parallel & Distributed Processing (IPDPS).

[22]  Nitesh V. Chawla,et al.  DisNet: A Framework for Distributed Graph Computation , 2011, 2011 International Conference on Advances in Social Networks Analysis and Mining.

[23]  Ulrik Brandes,et al.  Heuristics for Speeding Up Betweenness Centrality Computation , 2012, 2012 International Conference on Privacy, Security, Risk and Trust and 2012 International Confernece on Social Computing.

[24]  Shou-De Lin,et al.  What Can the Temporal Social Behavior Tell Us? An Estimation of Vertex-Betweenness Using Dynamic Social Information , 2010, 2010 International Conference on Advances in Social Networks Analysis and Mining.