Abstract
Detecting communities using modularity maximization is an important operation in network analysis. As the size of the networks increase to petascales, it is important to design parallel algorithms to handle the large-scale data. In this chapter, a shared memory (OpenMP-based) implementation of the Louvain method, one of the most popular algorithms for maximizing modularity, is introduced. This chapter also discusses the challenges in parallelizing this algorithm as well as metrics for evaluating the correctness of the results. The results demonstrate that the implementation is highly scalable. Moreover, it also focuses on how this template can be extended to time-varying networks.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
S. Bansal, S. Bhowmick, P. Paymal, Fast community detection for dynamic complex networks, communications in computer and information science, vol. 116, in Proceedings of the Second Workshop on Complex Networks, 2010
V.D. Blondel, J.-L. Guillaume, R. Lambiotte, E. Lefebvre, Fast unfolding of communities in large networks. J. Stat. Mech. Theory Exp. 2008(10), P10008 (25 Jul 2008). doi:10.1088/1742-5468/2008/10/p10008
U. Brandes, D. Delling, M. Gaertler, R. Gorke, M. Hoefer, Z. Nikoloski, D. Wagner, On modularity clustering. IEEE Trans. Knowl. Data Eng. 20(2), 172–188 (2008)
A. Clauset, M.E.J. Newman, C. Moore, Finding community structure in very large networks. Phys. Rev. E 70(6), 66111 (2004)
B.H. Good, Y.-A. de Montjoye, A. Clauset, The performance of modularity maximization in practical contexts. Phys. Rev. E 81, 046106 (2010)
D. Ediger, J. Riedy, H. Meyerhenke, D.A. Bader, Tracking structure of streaming social networks, in 5th Workshop on Multithreaded Architectures and Applications (MTAAP) (2011)
A. Lancichinetti, S. Fortunato, Benchmarks for testing community detection algorithms on directed and weighted graphs with overlapping communities. Phys. Rev. E 80, 016118 (2009)
M.E.J. Newman, M. Girvan, Finding and evaluating community structure in networks. Phys. Rev. E 69(2), 026113 (2004)
M.A. Porter, J.-P. Onnela, P.J. Mucha, Communities in networks. Not. Am. Math. Soc. 56(9), (22 Feb 2009)
U.N. Raghavan, R. Albert, S. Kumara, Near linear time algorithm to detect community structures in large-scale networks. Phys. Rev. E 76, 036106 (2007)
W. Rand, Objective criteria for the evaluation of clustering methods. J. Am. Stat. Assoc. 66(336), 846–850 (1971)
E.J. Riedy, H. Meyerhenke, D. Ediger, D.A. Bader, Parallel community detection for massive graphs, in 10th DIMACS Implementation Challenge - Graph Partitioning and Graph Clustering, 2012
J. Soman, A. Narang, Fast community detection algorithm with GPUs and multicore architectures, in Proceedings of the 2011 IEEE International Parallel & Distributed Processing Symposium, 2011
V.A. Traag, P. Van Dooren, Y. Nesterov, Narrow scope for resolution-limit-free community detection. Phys. Rev. E 84, 016114 (2011)
Acknowledgement
This work was completed with the support of the College of Information Science and Technology, University of Nebraska at Omaha (UNO), and the FIRE grant from the UNO Office of Research and Creative Activity.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer Science+Business Media New York
About this chapter
Cite this chapter
Bhowmick, S., Srinivasan, S. (2013). A Template for Parallelizing the Louvain Method for Modularity Maximization. In: Mukherjee, A., Choudhury, M., Peruani, F., Ganguly, N., Mitra, B. (eds) Dynamics On and Of Complex Networks, Volume 2. Modeling and Simulation in Science, Engineering and Technology. Birkhäuser, New York, NY. https://doi.org/10.1007/978-1-4614-6729-8_6
Download citation
DOI: https://doi.org/10.1007/978-1-4614-6729-8_6
Published:
Publisher Name: Birkhäuser, New York, NY
Print ISBN: 978-1-4614-6728-1
Online ISBN: 978-1-4614-6729-8
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)