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Evaluating Community Detection Using a Bi-objective Optimization

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Intelligent Computing Theories (ICIC 2013)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 7995))

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Abstract

Community detection consists on a partitioning networks technique into clusters (communities) with weak coupling (external connectivity) and high cohesion (internal connectivity). In order to measure the performance of the clustering, the network modularity is largely used, a metric that presents the cohesion and the coupling of communities. In this paper, a global and bi-objective function is proposed to evaluate community detection. This function combines modularity (based on structure and edges weights) and the inter-classes inertia (based on nodes weights). Then, we rely on a computational optimization technique i.e. Particle Swarm Optimization to maximize this bi-objective quality. Finally, a case study evaluates the proposed solution and illustrates practical uses.

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References

  1. Newman, M.E.J., Girvan, M.: Finding and Evaluating Community Structure in Networks. J. Physical Review E 69(I), 2 (2004)

    Google Scholar 

  2. Steinhaeuser, K., Chawla, N.V.: Community Detection in a Large Real-World Social Network. In: International Conference on Social Computing, Behavioral Modeling and Prediction, pp. 168–175 (2008)

    Google Scholar 

  3. Shi, J., Malik, J.: Normalized Cuts and Image Segmentation. IEEE Trans. Pattern Analysis and Machine Intelligence 22(8), 888–905 (2000)

    Article  Google Scholar 

  4. Xu, X., Yuruk, N., Feng, Z., Schweiger, T.A.J.: Scan: A Structural Clustering Algorithm for Networks. In: International Conference on Knowledge Discovery and Data Mining, pp. 824–833 (2007)

    Google Scholar 

  5. Tian, Y., Hankins, R.A., Patel, J.M.: Effcient Aggregation for Graph Summarization. In: International Conference Management of Data (SIGMOD 2008), pp. 567–580 (2008)

    Google Scholar 

  6. Li, D.H., Liu, J.G., Liang, J.Z., Pana, Y.: Detecting Community Structure in Complex Networks Via Node Similarity. Physica A: Statistical Mechanics and its Applications 389(14), 2849–2857 (2010)

    Article  Google Scholar 

  7. Zhou, Y., Cheng, H., Yu, J.X.: Graph Clustering Based on Structural/Attribute Similarities. In: International Conference VLDB 2009, pp. 718–729 (2009)

    Google Scholar 

  8. Dang, T.A., Viennet, E.: Community Detection Based on Structural and Attribute Similarities. In: International Conference on Digital Society (ICDS 2012), pp. 7–14 (2012)

    Google Scholar 

  9. Wakita, K., Tsurumi, T.: Finding Community Structure in Mega-Scale Social Networks. In: 16th International Conference on World Wide Web (WWW 2007), pp. 1275–1276 (2007)

    Google Scholar 

  10. Blondel, V.D., Guillaume, J.L., Lambiotte, R., Lefebvre, E.: Fast Unfolding of Communities in Large Networks. J. of Statistical Mechanics: Theory and Experiment 10, 10008–10020 (2008)

    Article  Google Scholar 

  11. Palla, G., Derenyi, I., Farkas, I., Vicsek, T.: Uncovering the Overlapping Community Structure of Complex Networks in Nature and Society. Nature 435(7043), 814–818 (2005)

    Article  Google Scholar 

  12. Ahn, Y.Y., Bagrow, J.P., Lehmann, S.: Link communities Reveal Multiscale Complexity in Networks. Natur. 466(7307), 761–764 (2010)

    Article  Google Scholar 

  13. Leskovec, J., Lang, K.J., Mahoney, M.W.: Empirical Comparison of Algorithms for Network Community Detection. In: International Conference on World Wide Web, pp. 631–640 (2010)

    Google Scholar 

  14. Pizzuti, C.: Community Detection in Social Networks with Genetic Algorithms. In: Genetic and Evolutionary Computation Conference (2008)

    Google Scholar 

  15. Mazur, P., Zmarzłowski, K., Orłowski, A.J.: Genetic Algorithms Approach to Community Detection. In: 4th Polish Symposium on Econo- and Sociophysics (2009)

    Google Scholar 

  16. Tasgin, M.: Community Detection Model Using Genetic Algorithm in Complex Networks and Its Application in Real-Life Networks, MS Thesis, Graduate Program in Computer Engineering, Bogazici University (2005)

    Google Scholar 

  17. He, D., Liu, J., Liu, D., Jin, D., Jia, Z.: Ant Colony Optimization for Community Detection in Large-Scale Complex Networks. In: Seventh International Conference on Natural Computation (ICNC), pp. 1151–1155 (2011)

    Google Scholar 

  18. Sadi, S., Etaner-Uyar, S., Gündüz-Öğüdücü, S.: Community Detection Using Ant Colony Optimization Techniques. In: 15th International Conference on Soft Computing (2009)

    Google Scholar 

  19. Liu, Y., Wang, Q., Wang, Q., Yao, Q., Liu, Y.: Email Community Detection Using Artificial Ant Colony Clustering. In: Chang, K.C.-C., Wang, W., Chen, L., Ellis, C.A., Hsu, C.-H., Tsoi, A.C., Wang, H. (eds.) APWeb/WAIM 2007 Ws. LNCS, vol. 4537, pp. 287–298. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  20. Chen, B., Chen, L., Chen, Y.: Detecting Community Structure in Networks Based on Ant Colony Optimization. In: International Conference on Information & Knowledge Engineering, pp. 247–253

    Google Scholar 

  21. Guimera, R., Amaral, L.A.: Functional cartography of complex metabolic networks. Nature 433, 895–900 (2005)

    Article  Google Scholar 

  22. El Dor, A.: Perfectionnement des algorithmes d’Optimisation par Essaim Particulaire. Applications en segmentation d’images et en électronique. PhD Thesis, Paris-Est University (2012)

    Google Scholar 

  23. Clerc, M., Siarry, P.: Une nouvelle métaheuristique pour l’optimisation difficile: la méthode des essaims particulaires. J. l’enseignement des Sciences et Technologies de l’information et des Systèmes 3(7) (2004)

    Google Scholar 

  24. Porter, M.A., Onnela, J.P., Mucha, P.J.: Communities in Networks. Notices of the AMS 56(9), 1082–1097 (2009)

    MathSciNet  MATH  Google Scholar 

  25. Pons, P.: Détection de communautés dans les grands graphes de terrain. PhD Thesis, Paris 7 University (2007)

    Google Scholar 

  26. Amaral, L.A.N., Scala, A., Barthélémy, M., Stanley, H.E.: Classes of small-world networks. Natl. Acad. Sci. 97(21) (2000)

    Google Scholar 

  27. Lebart, L., Maurineau, A., Piron, M.: Traitement des données statistiques. Dunod, Paris (1982)

    Google Scholar 

  28. Ward, J.H.: Hierarchical Grouping to Optimize an Objective Function. J. of Amer. Statist. Assoc. 58, 236–244 (1963)

    Article  Google Scholar 

  29. Ben Yahia, N., Bellamine, N., Ben Ghezala, H.: Using Community Detection to Support Decision Making Process. In: International Conference on Information Technology and e-Services, pp. 566–570 (2012)

    Google Scholar 

  30. Eberhart, R.C., Kennedy, J.: New Optimizer Using Particle Swarm Theory. In: 6th International Symposium on Micro Machine and Human Science, pp. 39–43 (1995)

    Google Scholar 

  31. Parsopoulos, K.E., Vrahatis, M.N.: Particle Swarm Optimization and Intelligence: Advances and Applications. Information Science Reference, IGI Global (2010)

    Google Scholar 

  32. Yisu, J., Knowles, J., Hongmei, L., Yizeng, L., Kell, D.B.: The Landscape Adaptive Particle Swarm Optimizer. Applied Soft Computing 8, 295–304 (2008)

    Article  Google Scholar 

  33. Clerc, M.: The Swarm and the Queen: Towards a Deterministic and Adaptive Particle Swarm Optimization. In: Congress of Evolutionary Computation, pp. 1951–1957 (1999)

    Google Scholar 

  34. Carlisle, G., Dozier, G.: An Off-The-Shelf PSO. In: Particle Swarm Optimization Workshop, pp. 1–6 (2001)

    Google Scholar 

  35. Ben Yahia, N., Bellamine, N., Ben Ghezala, H.: Vers une architecture multicouche d’ontologies dédiée à la résolution mixte de problèmes. In: Extraction et Gestion des Connaissances, pp. 263–268 (2013)

    Google Scholar 

  36. http://protege.stanford.edu

  37. Recio-García, J.A.: jCOLIBRI CBR Framework, http://www.iccbr.org/iccbr10/jCOLIBRI-Overview.pdf

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Author information

Authors and Affiliations

  1. Laboratoire RIADI, Université de la Manouba, Tunisia

    Nesrine Ben Yahia, Narjès Bellamine Ben Saoud & Henda Ben Ghezala

  2. Ecole Nationale des Sciences de l’Informatique, Université de la Manouba, Tunisia

    Nesrine Ben Yahia & Henda Ben Ghezala

  3. Institut Supérieur d’Informatique, Université Tunis El Manar, Tunisia

    Narjès Bellamine Ben Saoud

Authors
  1. Nesrine Ben Yahia
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  2. Narjès Bellamine Ben Saoud
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  3. Henda Ben Ghezala
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Editor information

Editors and Affiliations

  1. Machine Learning and Systems Biology Laboratory, Tongji University, 4800 Caoan Road, 201804, Shanghai, China

    De-Shuang Huang

  2. Electrical and Electronics Department, Polytechnic of Bari, Via Orabona 4, 70125, Bari, Italy

    Vitoantonio Bevilacqua

  3. Faculty of Engineering, District University Francisco José de Caldas, Cra. 7a No. 40-53, Fifth Floor, Bogotá, Colombia

    Juan Carlos Figueroa

  4. School of Electrical, Computer and Telecommunications Engineering, The University of Wollongong, 2522, North Wollongong, NSW, Australia

    Prashan Premaratne

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Ben Yahia, N., Bellamine Ben Saoud, N., Ben Ghezala, H. (2013). Evaluating Community Detection Using a Bi-objective Optimization. In: Huang, DS., Bevilacqua, V., Figueroa, J.C., Premaratne, P. (eds) Intelligent Computing Theories. ICIC 2013. Lecture Notes in Computer Science, vol 7995. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39479-9_8

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  • DOI: https://doi.org/10.1007/978-3-642-39479-9_8

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-39478-2

  • Online ISBN: 978-3-642-39479-9

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