New Generation Computing

, Volume 33, Issue 4, pp 409–424 | Cite as

Relational Classification Using Random Walks in Graphs

  • Tomasz Kajdanowicz


A novel approach to relational classification based on a Gaussian Random Field and random walks over the graph representing labeled and unlabeled examples is proposed in the paper. Additionally, a class homogeneity measure has been introduced. It can be used for pre-assessment of method applicability for particular networks. The presented experimental results on eight datasets revealed that the framework based on random walk concept possesses the promising potential to effectively classify nodes in the network. Owing to the dependencies discovered, the usefulness of the Random Walk approach to relational classification can be assessed from careful study of proposed class homogeneity distribution in the network.


Relational Learning Collective Classification Relational Classification Complex Networks Networked-data Graph Processing Random Walk Random Walk Classification RWC Gaussian Random Field Random Field Class Homogeneity 


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  1. 1.
    Battiston, F., Nicosia, V., Latora, V., “Structural measures for multiplex networks,” Phys. Rev. E, 89, 032804, Mar 2014Google Scholar
  2. 2.
    Breiman L.: “Bagging predictors,”. Machine Learning 24(2), 123–140 (1996)zbMATHMathSciNetGoogle Scholar
  3. 3.
    Bu D., Zhao Y., Cai L., Xue H., Zhu X., Lu H., Zhang J., Sun S., Ling L., Zhang N., Li G., Chen R.: “Topological structure analysis of the protein-protein interaction network in budding yeast,”. Nucleic Acids Research 31(9), 2443–2450 (2003)CrossRefGoogle Scholar
  4. 4.
    Chapelle O., Schölkopf B., Zien A.: Semi-supervised learning. MIT press, Cambridge (2006)CrossRefGoogle Scholar
  5. 5.
    Desrosiers C., Karypis G.: “Within-network classification using local structure similarity,”. Lecture Notes in Computer Science 5781, 260–275 (2009)CrossRefGoogle Scholar
  6. 6.
    Eldardiry, H. and Neville, J., “Across-model collective ensemble classification” in Proc. of the 25th Conf. on Artificial Intelligence, AAAA, 2011Google Scholar
  7. 7.
    Eldardiry, H. and Neville, J., “An analysis of how ensembles of collective classifiers improve predictions in graphs,” in Proc. of the 21st ACM International Conference on Information and Knowledge Management, 2012Google Scholar
  8. 8.
    Fast, A. and Jensen, D., “Why stacked models perform effective collective classification,” in Proc. of the 2008 Eighth IEEE International Conference on Data Mining, IEEE, pp. 785–790 2008Google Scholar
  9. 9.
    Gallagher, B. and Eliassi-Rad, T., “Leveraging label-independent features for classification in sparsely labeled networks: An empirical study,” in SNA-KDD08, ACM, 2008Google Scholar
  10. 10.
    Geman S., Geman D.: “Stochastic relaxation, gibbs distributions and the bayesian restoration of images,”. IEEE Transactions on Pattern Analysis and Machine Intelligence 6, 721–741 (1984)zbMATHCrossRefGoogle Scholar
  11. 11.
    Kajdanowicz, T., Kazienko, P., “Collective classification,” Encyclopedia of Social Network Analysis and Mining, Springer, 2013Google Scholar
  12. 12.
    Kajdanowicz T., Kazienko P., Janczak M.: “Collective classification techniques: an experimental study,”. New Trends in Databases and Information Systems 185, 99–108 (2012)Google Scholar
  13. 13.
    Kazienko K., Kajdanowicz T.: “Label-dependent node classification in the Network,”. Neurocomputing 75(1), 199–209 (2012)CrossRefGoogle Scholar
  14. 14.
    Kazienko P., Musial K., Kajdanowicz T.: “Multidimensional social network in the social recommender system,”. IEEE Transactions on Systems, Man and Cybernetics - Part A: Systems and Humans 41(4), 746–759 (2011)CrossRefGoogle Scholar
  15. 15.
    Kimmig A., Mihalkova L., Getoor L.: “Lifted graphical models: a survey,”. Machine Learning 99(1), 1–45 (2015)MathSciNetCrossRefGoogle Scholar
  16. 16.
    Knobbe, A., de Haas, M. and Siebes, A., “Propositionalisation and aggregates,” in Proc. of Fifth European Conference on Principles of Data Mining and Knowledge Discovery, pp. 277–288, 2001Google Scholar
  17. 17.
    Kravets V. G., Schedin F., Jalil R., Britnell L., Gorbachev R.V., Ansell D., Thackray B., Novoselov K. S., Geim A. K., Kabashin A.V.: “Singular phase nano-optics in plasmonic metamaterials for label-free single-molecule detection,”. Nature materials 12(4), 304–309 (2013)CrossRefGoogle Scholar
  18. 18.
    Krawczyk B.: “One-class classifier ensemble pruning and weighting with firefly Algorithm,”. Neurocomputing 150, 490–500 (2015)CrossRefGoogle Scholar
  19. 19.
    Krawczyk, B., Wozniak, M. and Herrera, F., “On the usefulness of one-class classifier ensembles for decomposition of multi-class problems,” Pattern Recognition, Elsevier, 2015Google Scholar
  20. 20.
    Lu, Q. and Getoor, L., “Link-based classification,” in Proc. of 20th International Conference on Machine Learning ICML, pp. 496–503, 2003Google Scholar
  21. 21.
    Musial K., Kazienko P.: “Social networks on the internet,”. World Wide Web Journal 16(1), 31–72 (2013)CrossRefGoogle Scholar
  22. 22.
    Newman M. E. J.: “Finding community structure in networks using the eigenvectors of matrices,”. Physical Review E 74, 036104 (2006)MathSciNetCrossRefGoogle Scholar
  23. 23.
    Nooy, W., Mrvar, A. and Batagelj, V., Exploratory Social Network Analysis with Pajek, chapter 11, Cambridge University Press, 2004Google Scholar
  24. 24.
    Pearl, J., Probabilistic reasoning in intelligent systems, Morgan Kaufmann, 1988Google Scholar
  25. 25.
    Perozzi, B., Al-Rfou, R. and Skiena, S., “Deepwalk: Online learning of social representations,” in Proc. of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’14, pp. 701–710, New York, NY, USA, ACM, 2014Google Scholar
  26. 26.
    Sen, P., Namata, G., Bilgic, M. and Getoor, L., “Collective classification,” in Encyclopedia of Machine Learning, pp. 189–193, Springer, 2010Google Scholar
  27. 27.
    Sen P., Namata G., Bilgic M., Getoor L., Gallagher B., Eliassi-Rad T.: “Collective classification in network data,”. Artificial Intelligence Magazine 29(3), 93–106 (2008)Google Scholar
  28. 28.
    Taskar, B., Abbeel, P. and Koller, D., “Discriminative probabilistic models for relational data,” in Proc. of 18th Conference in Uncertainty in Artificial Intelligence, San Francisco, Morgan Kaufmann Publishers, 2002Google Scholar

Copyright information

© Ohmsha and Springer Japan 2015

Authors and Affiliations

  1. 1.Department of Computational IntelligenceWrocław University of TechnologyWrocławPoland

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