Abstract
Graph clustering techniques are very useful for detecting densely connected groups in large graphs. Many existing graph clustering methods mainly focus on the topological structure, but ignore the vertex properties. Existing graph clustering methods have been recently extended to deal with nodes attribute. In this paper we propose a new method which uses the nodes attributes information along with the topological structure of the network in the clustering process. Experimental results demonstrate the effectiveness of the proposed method through comparisons with the state-of-the-art graph clustering methods.
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
Notes
- 1.
lipn.univ-paris13.fr/\(\sim \)falih/packages/ANCL/.
References
Blondel, V.D., Guillaume, J.l., Lefebvre, E.: Fast unfolding of communities in large networks. J. Stat. Mech. Theory Exp. 2008, P10008 (2008)
Cheng, H., Zhou, Y., Yu, J.X.: Clustering Large Attributed Graphs: A balance between structural and attribute similarities. ACM Trans. Knowl. Discov. Data 5(2), 12:1–12:33 (2011). https://doi.org/10.1145/1921632.1921638
Combe, D., Largeron, C., Egyed-Zsigmond, E.O., Géry, M.: Combining relations and text in scientific network clustering. In: 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 1280–1285. https://doi.org/10.1109/ASONAM.2012.215, http://hal.archives-ouvertes.fr/hal-00730226. (2012)
Combe, D., Largeron, C., Géry, M., Egyed-Zsigmond, E.: I-louvain: An attributed graph clustering method. Adv. Intell. Data Anal. XIV, pp. 181–192. Springer (2015)
Dang, T., Viennet, E.: Community detection based on structural and attribute similarities. In: International Conference on Digital Society (ICDS), pp. 7–12 (2012)
Duda, R., Hart, P., Stork, D.: Pattern Classification. Wiley (2001)
Good, B.H., de Montjoye, Y.A., Clauset., A.: The performance of modularity maximization in practical contexts. Phys. Rev. E(81), 046106 (2010)
Kanawati, R.: Licod : Leaders Identification for Community Detection in Complex Networks. p. 577582. IEEE (2013)
Kanawati, R.: Yasca: An ensemble-based approach for community detection in complex networks. COCOON. pp. 657–666 (2014)
Karger, D.R.: Global min-cuts in rnc, and other ramifications of a simple min-out algorithm. In: Proceedings of the Fourth Annual ACM-SIAM Symposium on Discrete Algorithms, SODA ’93, pp. 21–30. Society for Industrial and Applied Mathematics, Philadelphia, PA, USA (1993). http://dl.acm.org/citation.cfm?id=313559.313605
Khorasgani, R.R., Chen, J., Zaiane, O.R.: Top leaders community detection approach in information networks. In: 4th SNA-KDD Workshop on Social Network Mining and Analysis. Washington D.C. (2010)
Lancichinetti, A., Fortunato, S.: Limits of modularity maximization in community detection. CoRR abs/1107.1 (2011)
Largeron, C., Mougel, P.N., Rabbany, R., Zaïane, O.R.: Generating Attributed Networks with Communities. Plos One 10(4), e0122,777 (2015). https://doi.org/10.1371/journal.pone.0122777
Nawaz, W., Khan, K.U., Lee, Y.K., Lee, S.: Intra graph clustering using collaborative similarity measure. Distrib. Parallel Databases. 583–603 (2015). https://doi.org/10.1007/s10619-014-7170-x
Neville, J., Adler, M., Jensen, D.: Clustering relational data using attribute and link information. In: Proceedings of the Text Mining and Link Analysis Workshop, 18th International Joint Conference on Artificial Intelligence, pp. 9–15 (2003)
Newman, M.E.J., Girvan, M.: Finding and evaluating community structure in networks. Phys. Rev. E 69(2), 026113 (2004). https://doi.org/10.1103/PhysRevE.69.026113
Shi, J., Malik, J.: Normalized cuts and image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 22(8), 888–905 (2000)
Steinhaeuser, K., Chawla, N.V.: Community detection in a large real-world social network. In: Social Computing, Behavioral Modeling, and Prediction, pp. 168–175. Springer (2008)
Thrun, S., Saul, L.K., Schölkopf, B. (eds.): Advances in Neural Information Processing Systems 16 [Neural Information Processing Systems, NIPS 2003, December 8-13, 2003, Vancouver and Whistler, British Columbia, Canada]. MIT Press (2004)
Yakoubi, Z., Kanawati, R.: Licod: Leader-driven approaches for community detection. Vietnam J. Comput. Sci. 1(4), 241–256 (2014)
Yang, J., McAuley, J., Leskovec, J.: Community detection in networks with node attributes. In: Proceedings of IEEE International Conference on Data Mining, ICDM pp. 1151–1156 (2013). https://doi.org/10.1109/ICDM.2013.167
Zhou, Y., Cheng, H., Yu, J.X.: Clustering large attributed graphs: An efficient incremental approach. In: Proceedings of IEEE International Conference on Data Mining, ICDM pp. 689–698 (2010). https://doi.org/10.1109/ICDM.2010.41
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this paper
Cite this paper
Falih, I., Grozavu, N., Kanawati, R., Bennani, Y. (2018). ANCA : Attributed Network Clustering Algorithm. In: Cherifi, C., Cherifi, H., Karsai, M., Musolesi, M. (eds) Complex Networks & Their Applications VI. COMPLEX NETWORKS 2017. Studies in Computational Intelligence, vol 689. Springer, Cham. https://doi.org/10.1007/978-3-319-72150-7_20
Download citation
DOI: https://doi.org/10.1007/978-3-319-72150-7_20
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-72149-1
Online ISBN: 978-3-319-72150-7
eBook Packages: EngineeringEngineering (R0)