Abstract
Social circles detection is a special case of community detection in social network that is currently attracting a growing interest in the research community. We propose in this paper an empirical evaluation of the multi-assignment clustering method using different feature representation models. We define different vectorial representations from both structural egonet information and user profile features. We study and compare the performance on the available labelled Facebook data from the Kaggle competition on learning social circles in networks. We compare our results with several different baselines.
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References
Buhmann, J., Kühnel, H.: Vector quantization with complexity costs. IEEE Trans. Inf. Theor. 39(4), 1133–1145 (1993)
Dey, K., Bandyopadhyay, S.: An empirical investigation of like-mindedness of topically related social communities on microblogging platforms. In: International Conference on Natural Languages (2013)
Fortunato, S.: Community detection in graphs. Phys. Rep. 486(3), 75–174 (2010)
Frank, M., Streich, A.P., Basin, D., Buhmann, J.M.: Multi-assignment clustering for boolean data. J. Mach. Learn. Res. 13(1), 459–489 (2012)
Kaggle: Learning social circles in networks. http://www.kaggle.com/c/learning-social-circles
McAuley, J., Leskovec, J.: Learning to discover social circles in ego networks. Adv. Neural Inf. Process. Syst. 25, 539–547 (2012)
McAuley, J., Leskovec, J.: Discovering social circles in ego networks. ACM Trans. Knowl. Discov. Data (TKDD) 8(1), 4 (2014)
Palla, G., Dernyi, I., Farkas, I., Vicsek, T.: Uncovering the overlapping community structure of complex networks in nature and society. Nature 435(7043), 814–818 (2005)
Pathak, N., DeLong, C., Banerjee, A., Erickson, K.: Social topic models for community extraction. In: The 2nd SNA-KDD Workshop (2008)
Porter, M.A., Onnela, J.P., Mucha, P.J.: Communities in networks. Not. Amer. Math. Soc. 56(9), 1082–1097 (2009)
Rose, K., Gurewitz, E., Fox, G.C.: Vector quantization by deterministic annealing. IEEE Transactions on Information Theory 38(4), 1249–1257 (1992)
Sachan, M., Contractor, D., Faruquie, T.A., Subramaniam, L.V.: Using content and interactions for discovering communities in social networks. In: Proceedings of the 21st International Conference on World Wide Web, pp. 331–340 (2012)
Streich, A.P., Frank, M., Basin, D., Buhmann, J.M.: Multi-assignment clustering for Boolean data. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 969–976 (2009)
Vaidya, J., Atluri, V., Guo, Q.: The role mining problem: finding a minimal descriptive set of roles. In: Proceedings of the 12th ACM Symposium on Access Control Models and Technologies, pp. 175–184 (2007)
Zhou, D., Councill, I., Zha, H., Giles, C.L.: Discovering temporal communities from social network documents. In: Seventh IEEE International Conference on Data Mining, PP. 745–750 (2007)
Acknowledgement
This work was developed in the framework of the W911NF-14-1-0254 research project Social Copying Community Detection (SOCOCODE), funded by the US Army Research Office (ARO).
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Alonso, J., Paredes, R., Rosso, P. (2015). Empirical Evaluation of Different Feature Representations for Social Circles Detection. In: Paredes, R., Cardoso, J., Pardo, X. (eds) Pattern Recognition and Image Analysis. IbPRIA 2015. Lecture Notes in Computer Science(), vol 9117. Springer, Cham. https://doi.org/10.1007/978-3-319-19390-8_4
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DOI: https://doi.org/10.1007/978-3-319-19390-8_4
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