Clustering Attributed Multi-graphs with Information Ranking

  • Andreas PapadopoulosEmail author
  • Dimitrios Rafailidis
  • George Pallis
  • Marios D. Dikaiakos
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9261)


Attributed multi-graphs are data structures to model real-world networks of objects which have rich properties/attributes and they are connected by multiple types of edges. Clustering attributed multi-graphs has several real-world applications, such as recommendation systems and targeted advertisement. In this paper, we propose an efficient method for Clustering Attributed Multi-graphs with Information Ranking, namely CAMIR. We introduce an iterative algorithm that ranks the different vertex attributes and edge-types according to how well they can separate vertices into clusters. The key idea is to consider the ‘agreement’ among the attribute- and edge-types, assuming that two vertex properties ‘agree’ if they produced the same clustering result when used individually. Furthermore, according to the calculated ranks we construct a unified similarity measure, by down-weighting noisy vertex attributes or edge-types that may reduce the clustering accuracy. Finally, to generate the final clusters, we follow a spectral clustering approach, suitable for graph partitioning and detecting arbitrary shaped clusters. In our experiments with synthetic and real-world datasets, we show the superiority of CAMIR over several state-of-the-art clustering methods.


Attributed multi-graphs Spectral clustering Information ranking 



This work was partially supported by the EU Commission in terms of the PaaSport 605193 FP7 project (FP7-SME-2013).


  1. 1.
    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)CrossRefzbMATHGoogle Scholar
  2. 2.
    Papadopoulos, A., Pallis, G., Dikaiakos, M.D.: Identifying clusters with attribute homogeneity and similar connectivity in information networks. In: Proceedings of the 2013 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT), WI-IAT 2013, vol. 01, pp. 343–350. IEEE Computer Society, Washington, DC (2013)Google Scholar
  3. 3.
    Akoglu, L., Tong, H., Meeder, B., Faloutsos, C.: Pics: parameter-free identification of cohesive subgroups in large attributed graphs. In: Proceedings of the 12th SIAM International Conference on Data Mining, SDM 2012, pp. 439–450. SIAM/Omnipress (2012)Google Scholar
  4. 4.
    Perozzi, B., Akoglu, L., Iglesias Sánchez, P., Müller, E.: Focused clustering and outlier detection in large attributed graphs. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2014, pp. 1346–1355. ACM, New York (2014)Google Scholar
  5. 5.
    Xu, Z., Ke, Y., Wang, Y., Cheng, H., Cheng, J.: GBAGC: a general bayesian framework for attributed graph clustering. ACM Trans. Knowl. Discov. Data 9(1), 5:1–5:43 (2014)CrossRefGoogle Scholar
  6. 6.
    Zhou, Y., Cheng, H., Yu, J.X.: Graph clustering based on structural/attribute similarities. Proc. VLDB Endow. 2(1), 718–729 (2009)CrossRefGoogle Scholar
  7. 7.
    Kumar, A., Rai, P., Daume, H.: Co-regularized multi-view spectral clustering. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K. (eds.) Advances in Neural Information Processing Systems 24, pp. 1413–1421. Curran Associates, Inc., NY (2011)Google Scholar
  8. 8.
    Karypis, G., Kumar, V.: Multilevel algorithms for multi-constraint graph partitioning. In: Proceedings of the 1998 ACM/IEEE Conference on Supercomputing, SC 1998, pp. 1–13. IEEE Computer Society, Washington, DC (1998)Google Scholar
  9. 9.
    Papalexakis, E., Akoglu, L., Ience, D.: Do more views of a graph help? community detection and clustering in multi-graphs. In: 2013 16th International Conference on Information Fusion (FUSION), pp. 899–905 (2013)Google Scholar
  10. 10.
    Xu, X., Yuruk, N., Feng, Z., Schweiger, T.A.J.: SCAN: a structural clustering algorithm for networks. In: Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2007, pp. 824–833. ACM, New York (2007)Google Scholar
  11. 11.
    Yang, J., McAuley, J.J., Leskovec, J.: Community detection in networks with node attributes. [24], pp. 1151–1156Google Scholar
  12. 12.
    Xu, Z., Ke, Y., Wang, Y., Cheng, H., Cheng, J.: A model-based approach to attributed graph clustering. In: Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data, SIGMOD 2012, pp. 505–516. ACM, New York (2012)Google Scholar
  13. 13.
    Günnemann, S., Färber, I., Raubach, S., Seidl, T.: Spectral subspace clustering for graphs with feature vectors. [24], pp. 231–240Google Scholar
  14. 14.
    Günnemann, S., Boden, B., Färber, I., Seidl, T.: Efficient mining of combined subspace and subgraph clusters in graphs with feature vectors. In: Pei, J., Tseng, V.S., Cao, L., Motoda, H., Xu, G. (eds.) PAKDD 2013, Part I. LNCS, vol. 7818, pp. 261–275. Springer, Heidelberg (2013) CrossRefGoogle Scholar
  15. 15.
    Zhou, Y., Cheng, H., Yu, J.X.: Clustering large attributed graphs: an efficient incremental approach. In: Webb, G.I., Liu, B., Zhang, C., Gunopulos, D., Wu, X. (eds.) ICDM 2010, pp. 689–698. IEEE Computer Society, Washington, DC (2010) Google Scholar
  16. 16.
    Luxburg, U.: A tutorial on spectral clustering. Stat. Comput. 17(4), 395–416 (2007)MathSciNetCrossRefGoogle Scholar
  17. 17.
    Chan, P.K., Schlag, M.D.F., Zien, J.Y.: Spectral K-way ratio-cut partitioning and clustering. IEEE Trans. CAD Integr. Circuits Syst. 13(9), 1088–1096 (1994)CrossRefGoogle Scholar
  18. 18.
    Shi, J., Malik, J.: Normalized cuts and image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 22(8), 888–905 (2000)CrossRefGoogle Scholar
  19. 19.
    Chen, W.Y., Song, Y., Bai, H., Lin, C.J., Chang, E.: Parallel spectral clustering in distributed systems. IEEE Trans. Pattern Anal. Mach. Intell. 33(3), 568–586 (2011)CrossRefGoogle Scholar
  20. 20.
    Kang, U., Meeder, B., Papalexakis, E.E., Faloutsos, C.: Heigen: spectral analysis for billion-scale graphs. IEEE Trans. Knowl. Data Eng. 26(2), 350–362 (2014)CrossRefzbMATHGoogle Scholar
  21. 21.
    Hofmann, T., Schölkopf, B., Smola, A.J.: Kernel methods in machine learning. Ann. Stat. 36(3), 1171–1220 (2008)CrossRefzbMATHGoogle Scholar
  22. 22.
    Ning, H., Xu, W., Chi, Y., Gong, Y., Huang, T.S.: Incremental spectral clustering by efficiently updating the eigen-system. Pattern Recogn. 43(1), 113–127 (2010)CrossRefGoogle Scholar
  23. 23.
    Mall, R., Langone, R., Suykens, J.A.K.: Kernel spectral clustering for big data networks. Entropy 15(5), 1567–1586 (2013)MathSciNetCrossRefGoogle Scholar
  24. 24.
    Xiong, H., Karypis, G., Thuraisingham, B.M., Cook, D.J., Wu, X. (eds.): 2013 IEEE 13th International Conference on Data Mining. IEEE Computer Society, Washington, DC (2013)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Andreas Papadopoulos
    • 1
    Email author
  • Dimitrios Rafailidis
    • 1
  • George Pallis
    • 1
  • Marios D. Dikaiakos
    • 1
  1. 1.Department of Computer ScienceUniversity of CyprusNicosiaCyprus

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