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Memetic Graph Clustering

Authors Sonja Biedermann, Monika Henzinger, Christian Schulz, Bernhard Schuster

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Author Details

Sonja Biedermann
  • University of Vienna, Vienna, Austria
Monika Henzinger
  • University of Vienna, Vienna, Austria
Christian Schulz
  • University of Vienna, Vienna, Austria
Bernhard Schuster
  • University of Vienna, Vienna, Austria

Funding

The research leading to these results has received funding from the European Research Council under the European Community’s Seventh Framework Programme (FP7/2007-2013) /ERC grant agreement No. 340506.

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Sonja Biedermann, Monika Henzinger, Christian Schulz, and Bernhard Schuster. Memetic Graph Clustering. In 17th International Symposium on Experimental Algorithms (SEA 2018). Leibniz International Proceedings in Informatics (LIPIcs), Volume 103, pp. 3:1-3:15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018) https://doi.org/10.4230/LIPIcs.SEA.2018.3

Abstract

It is common knowledge that there is no single best strategy for graph clustering, which justifies a plethora of existing approaches. In this paper, we present a general memetic algorithm, VieClus, to tackle the graph clustering problem. This algorithm can be adapted to optimize different objective functions. A key component of our contribution are natural recombine operators that employ ensemble clusterings as well as multi-level techniques. Lastly, we combine these techniques with a scalable communication protocol, producing a system that is able to compute high-quality solutions in a short amount of time. We instantiate our scheme with local search for modularity and show that our algorithm successfully improves or reproduces all entries of the 10th DIMACS implementation challenge under consideration using a small amount of time.

Subject Classification

ACM Subject Classification
  • Information systems → Clustering
  • Theory of computation → Evolutionary algorithms
Keywords
  • Graph Clustering
  • Evolutionary Algorithms

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References

  1. L. Akoglu, H. Tong, and D. Koutra. Graph Based Anomaly Detection and Description: A Survey. Data Min. Knowl. Discov., 29(3):626-688, may 2015. URL: http://dx.doi.org/10.1007/s10618-014-0365-y.
  2. D. Aloise, G. Caporossi, S. Perron, P. Hansen, L. Liberti, and M. Ruiz. Modularity Maximization in Networks by Variable Neighborhood Search. In 10th DIMACS Impl. Challenge Workshop. Georgia Inst. of Technology, Atlanta, GA, 2012. Google Scholar
  3. V. Arnau, S. Mars, and I. Marín. Iterative Cluster Analysis of Protein Interaction Data. Bioinformatics, 21(3):364-378, 2004. Google Scholar
  4. G. Ausiello, P. Crescenzi, G. Gambosi, V. Kann, A. Marchetti-Spaccamela, and M. Protasi. Complexity and Approximation: Combinatorial Optimization Problems and their Approximability Properties. Springer Science &Business Media, 2012. Google Scholar
  5. T. Bäck. Evolutionary Algorithms in Theory and Practice: Evolution Strategies, Evolutionary Programming, Genetic Algorithms. PhD thesis, Informatik Centrum Dortmund, Germany, 1996. Google Scholar
  6. D. Bader, A. Kappes, H. Meyerhenke, P. Sanders, C. Schulz, and D. Wagner. Benchmarking for Graph Clustering and Partitioning. In Encyclopedia of Social Network Analysis and Mining. Springer, 2014. Google Scholar
  7. D. Bader, H. Meyerhenke, P. Sanders, and D. Wagner, editors. Proc. of the 10th DIMACS Impl. Challenge, Cont. Mathematics. AMS, 2012. Google Scholar
  8. S. Biedermann. Evolutionary Graph Clustering. Bachelor’s Thesis, Universität Wien, 2017. Google Scholar
  9. S. Biedermann, M. Henzinger, C. Schulz, and B. Schuster. Memetic Graph Clustering (see ArXiv preprint arXiv:1802.07034). Technical Report. arXiv:1802.07034, 2018. Google Scholar
  10. V. D. Blondel, J.-L. Guillaume, R. Lambiotte, and E. Lefebvre. Fast Unfolding of Communities in Large Networks. Journal of Statistical Mechanics: Theory and Experiment, 2008(10):P10008, 2008. URL: http://stacks.iop.org/1742-5468/2008/i=10/a=P10008.
  11. U. Brandes. Network Analysis: Methodological Foundations, volume 3418. Springer Science &Business Media, 2005. Google Scholar
  12. U. Brandes, D. Delling, M. Gaertler, R. Gorke, M. Hoefer, Z. Nikoloski, and D. Wagner. On Modularity Clustering. IEEE Transactions on Knowledge and Data Engineering, 20(2):172-188, 2008. Google Scholar
  13. U. Brandes, M. Gaertler, and D. Wagner. Engineering Graph Clustering: Models and Experimental Evaluation. ACM Journal of Experimental Algorithmics, 12(1.1):1-26, 2007. Google Scholar
  14. Ü. V. Çatalyürek, K. Kaya, J. Langguth, and B. Uçar. A Divisive Clustering Technique for Maximizing the Modularity. In 10th DIMACS Impl. Challenge Workshop. Georgia Inst. of Technology, Atlanta, GA, 2012. Google Scholar
  15. J. Demme and S. Sethumadhavan. Approximate Graph Clustering for Program Characterization. ACM Trans. Archit. Code Optim., 8(4):21:1-21:21, 2012. URL: http://dx.doi.org/10.1145/2086696.2086700.
  16. I. Derényi, G. Palla, and T. Vicsek. Clique Percolation in Random Networks. Physical review letters, 94(16):160202, 2005. Google Scholar
  17. A. A. Diwan, S. Rane, S. Seshadri, and S. Sudarshan. Clustering Techniques for Minimizing External Path Length. In Proceedings of the 22th International Conference on Very Large Data Bases, VLDB '96, pages 342-353, San Francisco, CA, USA, 1996. Morgan Kaufmann Publishers Inc. URL: http://dl.acm.org/citation.cfm?id=645922.673636.
  18. D. Džamić, D. Aloise, and N. Mladenović. Ascent-descent Variable Neighborhood Decomposition Search for Community Detection by Modularity Maximization. Annals of Operations Research, Jun 2017. URL: http://dx.doi.org/10.1007/s10479-017-2553-9.
  19. G. W. Flake, R. E. Tarjan, and K. Tsioutsiouliklis. Graph Clustering and Minimum Cut Trees. Internet Mathematics, 1(4):385-408, 2004. Google Scholar
  20. S. Fortunato. Community Detection in Graphs. Physics reports, 486(3):75-174, 2010. Google Scholar
  21. D. E. Goldberg. Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, 1989. Google Scholar
  22. M. Hamann, B. Strasser, D. Wagner, and T. Zeitz. Simple Distributed Graph Clustering using Modularity and Map Equation. arXiv preprint arXiv:1710.09605, 2017. Google Scholar
  23. T. Hartmann, A. Kappes, and D. Wagner. Clustering Evolving Networks. In Algorithm Engineering, pages 280-329. Springer, 2016. Google Scholar
  24. R. Kannan, S. Vempala, and A. Vetta. On clusterings: Good, bad and spectral. Journal of the ACM (JACM), 51(3):497-515, 2004. Google Scholar
  25. J. Kim, I. Hwang, Y. H. Kim, and B. R. Moon. Genetic Approaches for Graph Partitioning: A Survey. In Proceedings of the 13th Annual Genetic and Evolutionary Computation Conference (GECCO'11), pages 473-480. ACM, 2011. URL: http://dx.doi.org/10.1145/2001576.2001642.
  26. D. LaSalle. Graph Partitioning, Ordering, and Clustering for Multicore Architectures, 2015. Google Scholar
  27. M. Lipczak and E. E. Milios. Agglomerative Genetic Algorithm for Clustering in Social Networks. In Franz Rothlauf, editor, GECCO, pages 1243-1250. ACM, 2009. URL: http://dblp.uni-trier.de/db/conf/gecco/gecco2009.html#LipczakM09.
  28. H. Lu, M. Halappanavar, and A. Kalyanaraman. Parallel heuristics for scalable community detection. Parallel Computing, 47:19-37, 2015. Google Scholar
  29. S. McFarling. Program Optimization for Instruction Caches. SIGARCH Comput. Archit. News, 17(2):183-191, 1989. URL: http://dx.doi.org/10.1145/68182.68200.
  30. H. Meyerhenke, P. Sanders, and C. Schulz. Partitioning Complex Networks via Size-Constrained Clustering. In SEA, volume 8504 of Lecture Notes in Computer Science, pages 351-363. Springer, 2014. Google Scholar
  31. B. L Miller and D. E Goldberg. Genetic Algorithms, Tournament Selection, and the Effects of Noise. Evolutionary Computation, 4(2):113-131, 1996. Google Scholar
  32. M. E. J. Newman. Properties of Highly Clustered Networks. Physical Review E, 68(2):026121, 2003. Google Scholar
  33. M. E. J. Newman and M. Girvan. Finding and Evaluating Community Structure in Networks. Physical review E, 69(2):026113, 2004. Google Scholar
  34. M. Ovelgönne and A. Geyer-Schulz. An Ensemble Learning Strategy for Graph Clustering. In Graph Partitioning and Graph Clustering, number 588 in Contemporary Mathematics, 2013. Google Scholar
  35. J. B. Pereira-Leal, A. J. Enright, and C. A. Ouzounis. Detection of Functional Modules from Protein Interaction Networks. Proteins: Structure, Function, and Bioinformatics, 54(1):49-57, 2004. URL: http://dx.doi.org/10.1002/prot.10505.
  36. D. C. Porumbel, J.-K. Hao, and P. Kuntz. Spacing Memetic Algorithms. In 13th Annual Genetic and Evolutionary Computation Conference, GECCO 2011, Proceedings, Dublin, Ireland, July 12-16, 2011, pages 1061-1068, 2011. Google Scholar
  37. U. N. Raghavan, R. Albert, and S. Kumara. Near Linear Time Algorithm to Detect Community Structures in Large-Scale Networks. Physical Review E, 76(3), 2007. Google Scholar
  38. M. Rosvall, D. Axelsson, and C. T. Bergstrom. The Map Equation. The European Physical Journal-Special Topics, 178(1):13-23, 2009. Google Scholar
  39. S. Ryu and D. Kim. Quick Community Detection of Big Graph Data Using Modified Louvain Algorithm. In High Performance Computing and Communications; IEEE 14th International Conference on Smart City; IEEE 2nd International Conference on Data Science and Systems (HPCC/SmartCity/DSS), 2016 IEEE 18th International Conference on, pages 1442-1445. IEEE, 2016. Google Scholar
  40. P. Sanders and C. Schulz. Distributed Evolutionary Graph Partitioning. In Proc. of the 12th Workshop on Algorithm Engineering and Experimentation (ALENEX'12), pages 16-29, 2012. Google Scholar
  41. P. Sanders and C. Schulz. Think Locally, Act Globally: Highly Balanced Graph Partitioning. In 12th International Symposium on Experimental Algorithms (SEA'13). Springer, 2013. Google Scholar
  42. S. E. Schaeffer. Survey: Graph Clustering. Comput. Sci. Rev., 1(1):27-64, 2007. URL: http://dx.doi.org/10.1016/j.cosrev.2007.05.001.
  43. C. L. Staudt and H. Meyerhenke. Engineering High-Performance Community Detection Heuristics for Massive Graphs. In Proceedings 42nd Conference on Parallel Processing (ICPP'13), 2013. Google Scholar
  44. C. L. Staudt and H. Meyerhenke. Engineering Parallel Algorithms for Community Detection in Massive Networks. IEEE Trans. on Parallel and Distributed Systems, 27(1):171-184, 2016. URL: http://dx.doi.org/10.1109/TPDS.2015.2390633.
  45. M. Tasgin and H. Bingol. Community Detection in Complex Networks using Genetic Algorithm. In ECCS '06: Proc. of the European Conference on Complex Systems, 2006. URL: http://arxiv.org/abs/cond-mat/0604419.
  46. S. M. Van Dongen. Graph Clustering by Flow Simulation. PhD thesis, Utrecht University, 2001. Google Scholar
  47. D. Wagner and F. Wagner. Between Min Cut and Graph Bisection. In Proceedings of the 18th International Symposium on Mathematical Foundations of Computer Science, pages 744-750. Springer, 1993. Google Scholar
  48. Y. Xu, V. Olman, and D. Xu. Clustering Gene Expression Data using a Graph-Theoretic Approach: an Application of Minimum Spanning Trees. Bioinformatics, 18(4):536-545, 2002. Google Scholar
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