Parallel Community Detection Algorithm Using a Data Partitioning Strategy with Pairwise Subdomain Duplication

  • Diana Palsetia
  • William Hendrix
  • Sunwoo Lee
  • Ankit Agrawal
  • Wei-keng Liao
  • Alok Choudhary
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9697)


Community detection is an important data clustering technique for studying graph structures. Many serial algorithms have been developed and well studied in the literature. As the problem size grows, the research attention has recently been turning to parallelizing the technique. However, the conventional parallelization strategies that divide the problem domain into non-overlapping subdomains do not scale with problem size and the number of processes. The main obstacle lies in the fact that the graph algorithms often exhibit a high degree of data dependency, which makes developing scalable parallel algorithms a great challenge.

We present PMEP, a distributed-memory based parallel community detection algorithm that adopts an unconventional data partitioning strategy. PMEP divides a graph into subgraphs and assigns each pair of subgraphs to one process. This method duplicates a portion of computational workload among processes in exchange for a significantly reduced communication cost required in the later stages. After data partitioning, each process runs MEP on the assigned subgraph pair. MEP is a community detection algorithm based on the idea of maximizing equilibrium and purity. Our data partitioning method effectively simplifies the communication required for combining the local results into a global one and hence allows us to achieve better scalability over existing parallel algorithms without sacrificing the result quality. Our experimental results show a speedup of 126.95 on 190 MPI processes for using synthetic data sets and a speedup of 204.22 on 1225 processes for using a real-world data set.



This work is supported in part by the following grants: NSF awards CCF-1029166, IIS-1343639, CCF-1409601; DOE awards DE-SC0007456, DE-SC0014330; AFOSR award FA9550-12-1-0458; NIST award 70NANB14H012; DARPA award N66001-15-C-4036.


  1. 1.
    Bansal, S., Bhowmick, S., Paymal, P.: Fast community detection for dynamic complex networks. In: Mangioni, G. (ed.) CompleNet 2010. CCIS, vol. 116, pp. 196–207. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  2. 2.
    Blondel, V.D., Guillaume, J.L., Lambiotte, R., Lefebvre, E.: Fast unfolding of communities in large networks. J. Stat. Mech: Theory Exp. 2008(10), P10008 (2008)CrossRefGoogle Scholar
  3. 3.
    Boldi, P., Codenotti, B., Santini, M., Vigna, S.: Ubicrawler: a scalable fully distributed web crawler. Softw.: Pract. Experience 34(8), 711–726 (2004)Google Scholar
  4. 4.
    Brandes, U., Delling, D., Gaertler, M., Görke, R., Hoefer, M., Nikoloski, Z., Wagner, D.: On finding graph clusterings with maximum modularity. In: Brandstädt, A., Kratsch, D., Müller, H. (eds.) WG 2007. LNCS, vol. 4769, pp. 121–132. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  5. 5.
    Clauset, A., Newman, M.E.J., Moore, C.: Finding community structure in very large networks. Phys. Rev. E 70(6), 066111 (2004)CrossRefGoogle Scholar
  6. 6.
    Cormen, T.H., Leiserson, C.E., Rivest, R.L., Stein, C.: Introduction to Algorithms, 3rd edn. The MIT Press, Cambridge (2009)zbMATHGoogle Scholar
  7. 7.
    Fortunato, S.: Community detection in graphs. Phys. Rep. 486(3–5), 75–174 (2010)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Hendrickson, B., Kolda, T.G.: Graph partitioning models for parallel computing. Parallel Comput. 26(12), 1519–1534 (2000)MathSciNetCrossRefzbMATHGoogle Scholar
  9. 9.
    Hubert, L., Arabie, P.: Comparing partitions. J. Classif. 2(1), 193–218 (1985)CrossRefzbMATHGoogle Scholar
  10. 10.
    Jain, A.K., Murty, M.N., Flynn, P.J.: Data clustering: a review (1999)Google Scholar
  11. 11.
    Karypis, G., Kumar, V.: Parallel multilevel k-way partitioning scheme for irregular graphs. In: Proceedings of the 1996 ACM/IEEE Conference on Supercomputing (1996)Google Scholar
  12. 12.
    Karypis, G., Kumar, V.: A fast and high quality multilevel scheme for partitioning irregular graphs. SIAM J. Sci. Comput. 20(1), 359–392 (1998)MathSciNetCrossRefzbMATHGoogle Scholar
  13. 13.
    Karypis, G., Kumar, V.: Multilevel k-way partitioning scheme for irregular graphs. Parallel Distrib. Comput. 48(1), 96–129 (1998)MathSciNetCrossRefzbMATHGoogle Scholar
  14. 14.
    Lancichinetti, A., Fortunato, S., Radicchi, F.: Benchmark graphs for testing community detection algorithms. Phys. Rev. E Stat. Nonlin. Soft Matter Phys. 78(4 Pt 2), 046110 (2008)CrossRefGoogle Scholar
  15. 15.
    Lu, H., Halappanavar, M., Kalyanaraman, A., Choudhury, S.: Parallel heuristics for scalable community detection. In: Proceedings of the International Workshop on Multithreaded Architectures and Applications (MTAAP), IPDPS Workshops (2014)Google Scholar
  16. 16.
    Meyerhenke, H., Gehweiler, J.: On dynamic graph partitioning and graph clustering using diffusion. In: Algorithm Engineering. Dagstuhl Seminar Proceedings, vol. 10261 (2010)Google Scholar
  17. 17.
    Riedy, E.J., Meyerhenke, H., Ediger, D., Bader, D.A.: Parallel community detection for massive graphs. In: Graph Partitioning and Graph Clustering, pp. 207–222 (2012)Google Scholar
  18. 18.
    Staudt, C., Meyerhenke, H.: Engineering high-performance community detection heuristics for massive graphs. In: ICPP, pp. 180–189 (2013)Google Scholar
  19. 19.
    Wakita, K., Tsurumi, T.: Finding community structure in mega-scale social networks:[extended abstract]. In: Proceedings of the 16th International Conference on World Wide Web, pp. 1275–1276. ACM (2007)Google Scholar
  20. 20.
    Watts, D.J., Strogatz, S.H.: Collective dynamics of’small-world’networks. Nature 393(6684), 409–10 (1998)CrossRefGoogle Scholar
  21. 21.
    Wickramaarachchi, C., Frincu, M., Small, P., Prasanna, V.: Fast parallel algorithm for unfolding of communities in large graphs. In: 2014 IEEE High Performance Extreme Computing Conference (HPEC), pp. 1–6, September 2014Google Scholar
  22. 22.
    Zafarani, R., Liu, H.: Social computing data repository at arizona state university. School Comput. Inf. Decis. Syst. Eng. (2009)Google Scholar
  23. 23.
    Zardi, H., Romdhane, L.B.: An \(o(n^2)\) algorithm for detecting communities of unbalanced sizes in large scale social networks. Know.-Based Syst. 37, 19–36 (2013)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Diana Palsetia
    • 1
  • William Hendrix
    • 2
  • Sunwoo Lee
    • 1
  • Ankit Agrawal
    • 1
  • Wei-keng Liao
    • 1
  • Alok Choudhary
    • 1
  1. 1.Northwestern UniversityEvanstonUSA
  2. 2.University of South FloridaTampaUSA

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