Abstract
Node classification in Social Network is currently receiving raising attention in the Social Network Analysis research. The main objective of node classification is to assign the correct label to the unlabeled nodes from a set of all possible class labels. This classification task is performed using features extracted from a Social Network dataset. The success of proper feature extraction significantly influences classification accuracy, providing more discriminative description of the data. This paper describes label-dependent features extraction and examines the classification accuracy based on features extracted with this approach. The experiments on real-world data have shown that usage of label-dependent features can lead to significant improvement of classification accuracy.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
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 the Second ACM SIGKDD Workshop on Social Network Mining and Analysis (SNA-KDD 2008), Las Vegas, NV (2008)
Gallagher, B., Tong, H., Eliassi-Rad, T., Faloutsos, C.: Using ghost edges for classification in sparsely labeled networks. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 256 – 264 (2008)
Jensen, D., Neville, J.: Autocorrelation and linkage cause bias in evaluation of relational learners. In: Matwin, S., Sammut, C. (eds.) ILP 2002. LNCS (LNAI), vol. 2583, pp. 101–116. Springer, Heidelberg (2003)
Jensen, D., Neville, J., Gallagher, B.: Why collective inference improves relational classification. In: The Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 593 – 598 (2004)
Kajdanowicz, T., Kazienko, P., Kraszewski, J.: Boosting Algorithm with Sequence-loss Cost Function for Structured Prediction. In: Graña Romay, M., Corchado, E., Garcia Sebastian, M.T. (eds.) HAIS 2010. LNCS (LNAI), vol. 6076, pp. 573–580. Springer, Heidelberg (2010)
Lu, Q., Getoor, L.: Link-based classification. In: Proceedings of the 20th International Conference on Machine Learning ICML 2003, pp. 496 – 503 (2003)
Macskassy, S., Provost, F.: A brief survey of machine learning methods for classification in networked data and an application to suspicion scoring. In: Airoldi, E.M., Blei, D.M., Fienberg, S.E., Goldenberg, A., Xing, E.P., Zheng, A.X. (eds.) ICML 2006. LNCS, vol. 4503, pp. 172–175. Springer, Heidelberg (2007)
McPherson, M., Smith-Lovin, L., Cook, J.: Birds of a feather: Homophily in social networks. Annual Review of Sociology 27, 415–444 (2007)
Neville, J., Jensen, D.: Collective Classification with Relational Dependency Networks. In: Proceedings of the Second International Workshop on Multi-Relational Data Mining, Washington, DC, pp. 77–91 (2003)
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 UAI 2002, Edmonton, Canada (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Kajdanowicz, T., Kazienko, P., Doskocz, P., Litwin, K. (2010). An Assessment of Node Classification Accuracy in Social Networks Using Label-Dependent Feature Extraction. In: Lytras, M.D., Ordonez De Pablos, P., Ziderman, A., Roulstone, A., Maurer, H., Imber, J.B. (eds) Knowledge Management, Information Systems, E-Learning, and Sustainability Research. WSKS 2010. Communications in Computer and Information Science, vol 111. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16318-0_14
Download citation
DOI: https://doi.org/10.1007/978-3-642-16318-0_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-16317-3
Online ISBN: 978-3-642-16318-0
eBook Packages: Computer ScienceComputer Science (R0)