Abstract
Collective classification is the area in machine learning, in which unknown nodes in the network are classified based on the classes assigned to the known nodes and the network structure only. Three collective classification algorithms were described and examined in the paper: Iterative Classification (ICA), Gibbs Sampling (GS) and Loopy Belief Propagation (LBP). Experiments on various networks revealed that greater number of output classes decreases classification accuracy,GS provides better results than ICA and LBP outperforms others for dense structures while it is worse for sparse networks.
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
Preview
Unable to display preview. Download preview PDF.
References
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)
Desrosiers, C., Karypis, G.: Within-Network Classification Using Local Structure Similarity. In: Buntine, W., Grobelnik, M., Mladenić, D., Shawe-Taylor, J. (eds.) ECML PKDD 2009. LNCS, vol. 5781, pp. 260–275. Springer, Heidelberg (2009)
Gallagher, B., Eliassi-Rad, T.: Leveraging label-independent features for classification in sparsely labeled networks: An empirical study. In: Proceedings of Second ACM SIGKDD Workshop on Social Network Mining and Analysis, SNA-KDD 2008 (2008)
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)
Kazienko, P., Kajdanowicz, T.: Label-dependent node classification in the network. Neurocomputing 75(1), 199–209 (2012)
Knobbe, A.J., de Haas, M., Siebes, A.: Propositionalisation and Aggregates. In: Siebes, A., De Raedt, L. (eds.) PKDD 2001. LNCS (LNAI), vol. 2168, pp. 277–288. Springer, Heidelberg (2001)
Lu, Q., Getoor, L.: Link-based classification. In: Proceedings of 20th International Conference on Machine Learning, ICML, San Francisco, pp. 496–503 (2003)
Newman, M.E.J.: Finding community structure in networks using the eigenvectors of matrices. Physical Review E 74, 036104 (2006)
Nooy, W., Mrvar, A., Batagelj, V.: Exploratory Social Network Analysis with Pajek, ch. 11. Cambridge University Press (2004)
Pearl, J.: Probabilistic reasoning in intelligent systems. Morgan Kaufmann (1988)
Perlich, C., Provost, F.: Distribution-based aggregation for relational learning with identifier attributes. Machine Learning 62(1-2), 65–105 (2006)
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)
Taskar, B., Abbeel, P., Koller, D.: Discriminative probabilistic models for relational data. In: Proceedings of 18th Conference in Uncertainty in Artificial Intelligence. Morgan Kaufmann, San Francisco (2002)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Kajdanowicz, T., Kazienko, P., Janczak, M. (2013). Collective Classification Techniques: An Experimental Study. In: Pechenizkiy, M., Wojciechowski, M. (eds) New Trends in Databases and Information Systems. Advances in Intelligent Systems and Computing, vol 185. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32518-2_10
Download citation
DOI: https://doi.org/10.1007/978-3-642-32518-2_10
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-32517-5
Online ISBN: 978-3-642-32518-2
eBook Packages: EngineeringEngineering (R0)