Graph Manipulations for Fast Centrality Computation

The betweenness and closeness metrics are widely used metrics in many network analysis applications. Yet, they are expensive to compute. For that reason, making the betweenness and closeness centrality computations faster is an important and well-studied problem. In this work, we propose the framework BADIOS that manipulates the graph by compressing it and splitting into pieces so that the centrality computation can be handled independently for each piece. 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 betweenness centrality computation time of a 4.6 million edges graph from more than 5 days to less than 16 hours. For the same graph, the closeness computation time is decreased from more than 3 days to 6 hours (12.7x speedup).

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

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

[3]  Ümit V. Çatalyürek,et al.  Shattering and Compressing Networks for Betweenness Centrality , 2013, SDM.

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

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

[6]  Ümit V. Çatalyürek,et al.  Regularizing graph centrality computations , 2015, J. Parallel Distributed Comput..

[7]  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.

[8]  Ümit V. Çatalyürek,et al.  Hardware/Software Vectorization for Closeness Centrality on Multi-/Many-Core Architectures , 2014, 2014 IEEE International Parallel & Distributed Processing Symposium Workshops.

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

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

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

[12]  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.

[13]  Eli Upfal,et al.  ABRA: Approximating Betweenness Centrality in Static and Dynamic Graphs with Rademacher Averages , 2016, KDD.

[14]  Ümit V. Çatalyürek,et al.  Betweenness centrality on GPUs and heterogeneous architectures , 2013, GPGPU@ASPLOS.

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

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

[17]  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.

[18]  Jiawei Han,et al.  ACM Transactions on Knowledge Discovery from Data: Introduction , 2007 .

[19]  G. Loukidis,et al.  SIAM International Conference on Data Mining (SDM) , 2015 .

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

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

[22]  KayaKamer,et al.  Graph Manipulations for Fast Centrality Computation , 2017 .

[23]  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).

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

[25]  Ulrik Brandes,et al.  Topology manipulations for speeding betweenness centrality computation , 2015, J. Complex Networks.

[26]  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 .

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