Distributed Vertex-Cut Partitioning

  • Fatemeh Rahimian
  • Amir H. Payberah
  • Sarunas Girdzijauskas
  • Seif Haridi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8460)


Graph processing has become an integral part of big data analytics. With the ever increasing size of the graphs, one needs to partition them into smaller clusters, which can be managed and processed more easily on multiple machines in a distributed fashion. While there exist numerous solutions for edge-cut partitioning of graphs, very little effort has been made for vertex-cut partitioning. This is in spite of the fact that vertex-cuts are proved significantly more effective than edge-cuts for processing most real world graphs. In this paper we present Ja-be-Ja-vc, a parallel and distributed algorithm for vertex-cut partitioning of large graphs. In a nutshell, Ja-be-Ja-vc is a local search algorithm that iteratively improves upon an initial random assignment of edges to partitions. We propose several heuristics for this optimization and study their impact on the final partitioning. Moreover, we employ simulated annealing technique to escape local optima. We evaluate our solution on various graphs and with variety of settings, and compare it against two state-of-the-art solutions. We show that Ja-be-Ja-vc outperforms the existing solutions in that it not only creates partitions of any requested size, but also requires a vertex-cut that is better than its counterparts and more than 70% better than random partitioning.


Simulated Annealing Color Exchange Local Search Algorithm Graph Partitioning Direct Neighbor 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Abou-Rjeili, A., Karypis, G.: Multilevel algorithms for partitioning power-law graphs. In: Proc. of IPDPS 2006, p. 10. IEEE (2006)Google Scholar
  2. 2.
    Lang, K.: Finding good nearly balanced cuts in power law graphs (2004) (preprint)Google Scholar
  3. 3.
    Leskovec, J., Lang, K., Dasgupta, A., Mahoney, M.: Community structure in large networks: Natural cluster sizes and the absence of large well-defined clusters. Internet Mathematics 6(1), 29–123 (2009)MathSciNetCrossRefMATHGoogle Scholar
  4. 4.
    Albert, R., Jeong, H., Barabási, A.: Error and attack tolerance of complex networks. Nature 406(6794), 378–382 (2000)CrossRefGoogle Scholar
  5. 5.
    Gonzalez, J., Low, Y., Gu, H., Bickson, D., Guestrin, C.: Powergraph: Distributed graph-parallel computation on natural graphs. In: Proc. of OSDI 2012, pp. 17–30 (2012)Google Scholar
  6. 6.
    Xin, R., Gonzalez, J., Franklin, M., Stoica, I.: Graphx: A resilient distributed graph system on spark. In: Proc. of GRADES 2013, pp. 1–6. ACM (2013)Google Scholar
  7. 7.
    Rahimian, F., Payberah, A., Girdzijauskas, S., Jelasity, M., Haridi, S.: Ja-Be-Ja: A distributed algorithm for balanced graph partitioning. In: Proc. of SASO 2013. IEEE (2013)Google Scholar
  8. 8.
    Talbi, E.: Metaheuristics: From design to implementation, vol. 74. John Wiley & Sons (2009)Google Scholar
  9. 9.
    Guerrieri, A., Montresor, A.: Distributed Edge Partitioning for Graph Processing. CoRR abs/1403.6270 (2014)Google Scholar
  10. 10.
    Voulgaris, S., Gavidia, D., Van Steen, M.: Cyclon: Inexpensive membership management for unstructured p2p overlays. Journal of Network and Systems Management 13(2), 197–217 (2005)CrossRefGoogle Scholar
  11. 11.
    Jelasity, M., Montresor, A.: Epidemic-style proactive aggregation in large overlay networks. In: Proc. of ICDCS 2004, pp. 102–109. IEEE (2004)Google Scholar
  12. 12.
    Payberah, A.H., Dowling, J., Haridi, S.: Gozar: Nat-friendly peer sampling with one-hop distributed nat traversal. In: Felber, P., Rouvoy, R. (eds.) DAIS 2011. LNCS, vol. 6723, pp. 1–14. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  13. 13.
    Dowling, J., Payberah, A.: Shuffling with a croupier: Nat-aware peer-sampling. In: Proc. of ICDCS 2012, pp. 102–111. IEEE (2012)Google Scholar
  14. 14.
    Massoulié, L., Le Merrer, E., Kermarrec, A., Ganesh, A.: Peer counting and sampling in overlay networks: Random walk methods. In: Proc. of PODC 2006, pp. 123–132. ACM (2006)Google Scholar
  15. 15.
    Leskovec, J.: The graph partitioning archive (2012),
  16. 16.
    Leskovec, J.: Stanford large network dataset collection (2011),
  17. 17.
    Baños, R., Gil, C., Ortega, J., Montoya, F.G.: Multilevel heuristic algorithm for graph partitioning. In: Cagnoni, S., et al. (eds.) EvoWorkshops 2003. LNCS, vol. 2611, pp. 143–153. Springer, Heidelberg (2003)Google Scholar
  18. 18.
    Bui, T., Moon, B.: Genetic algorithm and graph partitioning. Transactions on Computers 45(7), 841–855 (1996)MathSciNetCrossRefMATHGoogle Scholar
  19. 19.
    Hendrickson, B., Leland, R.: A multi-level algorithm for partitioning graphs. SC 95, 28 (1995)MATHGoogle Scholar
  20. 20.
    Karypis, G., Kumar, V.: A fast and high quality multilevel scheme for partitioning irregular graphs. Journal on Scientific Computing 20(1), 359–392 (1998)MathSciNetCrossRefMATHGoogle Scholar
  21. 21.
    Karypis, G., Kumar, V.: Parallel multilevel series k-way partitioning scheme for irregular graphs. Siam Review 41(2), 278–300 (1999)MathSciNetCrossRefMATHGoogle Scholar
  22. 22.
    Walshaw, C., Cross, M.: Mesh partitioning: A multilevel balancing and refinement algorithm. Journal on Scientific Computing 22(1), 63–80 (2000)MathSciNetCrossRefMATHGoogle Scholar
  23. 23.
    Sanders, P., Schulz, C.: Engineering multilevel graph partitioning algorithms. In: Demetrescu, C., Halldórsson, M.M. (eds.) ESA 2011. LNCS, vol. 6942, pp. 469–480. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  24. 24.
    Soper, A., Walshaw, C., Cross, M.: A combined evolutionary search and multilevel optimisation approach to graph-partitioning. Journal of Global Optimization 29(2), 225–241 (2004)MathSciNetCrossRefMATHGoogle Scholar
  25. 25.
    Chardaire, P., Barake, M., McKeown, G.: A probe-based heuristic for graph partitioning. Transactions on Computers 56(12), 1707–1720 (2007)MathSciNetCrossRefGoogle Scholar
  26. 26.
    Benlic, U., Hao, J.: An effective multilevel tabu search approach for balanced graph partitioning. Computers & Operations Research 38(7), 1066–1075 (2011)MathSciNetCrossRefMATHGoogle Scholar
  27. 27.
    Sanders, P., Schulz, C.: Distributed evolutionary graph partitioning. arXiv preprint arXiv:1110.0477 (2011)Google Scholar
  28. 28.
    Talbi, E., Bessiere, P.: A parallel genetic algorithm for the graph partitioning problem. In: Proceedings of the 5th International Conference on Supercomputing, pp. 312–320. ACM (1991)Google Scholar
  29. 29.
    Luque, G., Alba, E.: Parallel Genetic Algorithms: Theory and Real World Applications. SCI, vol. 367. Springer (2011)Google Scholar
  30. 30.
    Gehweiler, J., Meyerhenke, H.: A distributed diffusive heuristic for clustering a virtual p2p supercomputer. In: Proc. of IPDPSW 2010, pp. 1–8. IEEE (2010)Google Scholar
  31. 31.
    Ramaswamy, L., Gedik, B., Liu, L.: A distributed approach to node clustering in decentralized peer-to-peer networks. Transactions on Parallel and Distributed Systems 16(9), 814–829 (2005)CrossRefGoogle Scholar
  32. 32.
    Kim, M., Candan, K.: SBV-Cut: Vertex-cut based graph partitioning using structural balance vertices. Data & Knowledge Engineering 72, 285–303 (2012)CrossRefGoogle Scholar
  33. 33.
    Zaharia, M., Chowdhury, M., Franklin, M., Shenker, S., Stoica, I.: Spark: Cluster computing with working sets. In: Proc. of HotCloud 2010, p. 10. USENIX (2010)Google Scholar
  34. 34.
    Zaharia, M., Chowdhury, M., Das, T., Dave, A., Ma, J., McCauley, M., Franklin, M., Shenker, S., Stoica, I.: Resilient distributed datasets: A fault-tolerant abstraction for in-memory cluster computing. In: Proc. of NSDI 2012, p. 2. USENIX (2012)Google Scholar

Copyright information

© IFIP International Federation for Information Processing 2014

Authors and Affiliations

  • Fatemeh Rahimian
    • 1
    • 2
  • Amir H. Payberah
    • 1
  • Sarunas Girdzijauskas
    • 2
  • Seif Haridi
    • 1
  1. 1.Swedish Institute of Computer ScienceStockholmSweden
  2. 2.KTH - Royal Institute of TechnologyStockholmSweden

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