Abstract
We address a problem of estimating the whole structure of an actual social network of people from only their two types of anonymous ego-centric information, personal attributes like sex and relational ones like the numbers of female and male friends, obtained as answers to questionnaires in a social survey. From these attribute values, we can obtain the degree of each node, which corresponds to the number of friends of each person, together with some macroscopic information about the network, like the ratio of links between female and male nodes to the total number of links, as the mixing matrices. However, we cannot directly know the actual connections between two nodes only from these observed mixing matrices. Thus, we propose a new method for estimating the whole structure of the hidden network by minimizing the Kullback-Leibler divergence between each pair of the observed and estimated mixing matrices, under the constraints with respect to the degree of each node. In our experiments using three types of networks, we show that the proposed method can produce much better estimation results, in comparison to a random baseline which is assigned arbitrary links under the degree constraints, especially for the cases of highly assortative, where each node has a tendency to connect to nodes with the same attribute values.
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References
Clauset, A., Moore, C., Newman, M.E.J.: Hierarchical structure and the prediction of missing links in networks. Nature 453, 98–101 (2008)
Even-Dar, E., Shapira, A.: A note on maximizing the spread of influence in social networks. In: Deng, X., Graham, F.C. (eds.) WINE 2007. LNCS, vol. 4858, pp. 281–286. Springer, Heidelberg (2007)
Kato, T., Tsuda, K., Asai, K.: Selective integration of multiple biological data for supervised network inference. Bioinformatics 21(10), 2488–2495 (2005)
Kempe, D., Kleinberg, J., Tardos, E.: Maximizing the spread of influence through a social network. In: Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2003, pp. 137–146. ACM, New York (2003)
Kimura, M., Saito, K., Nakano, R., Motoda, H.: Extracting influential nodes on a social network for information diffusion. Data Min. Knowl. Discov. 20(1), 70–97 (2010)
Leskovec, J., Adamic, L.A., Huberman, B.A.: The dynamics of viral marketing. ACM Trans. Web 1(1), 5 (2007)
Menon, A.K., Elkan, C.: Link prediction via matrix factorization. In: Gunopulos, D., Hofmann, T., Malerba, D., Vazirgiannis, M. (eds.) ECML PKDD 2011, Part II. LNCS (LNAI), vol. 6912, pp. 437–452. Springer, Heidelberg (2011)
Newman, M.E.J.: Mixing patterns in networks. Physical Review E 67(2), 026126+ (2003)
Nowell, D.L., Kleinberg, J.: The link prediction problem for social networks. In: CIKM 2003: Proceedings of the Twelfth International Conference on Information and Knowledge Management, pp. 556–559. ACM, New York (2003)
Oyama, S., Manning, C.D.: Using feature Conjunctions across examples for learning pairwise classifiers. In: Boulicaut, J.-F., Esposito, F., Giannotti, F., Pedreschi, D. (eds.) ECML 2004. LNCS (LNAI), vol. 3201, pp. 322–333. Springer, Heidelberg (2004)
Ravasz, E., Barabási, A.L.: Hierarchical organization in complex networks. Physical Review E 67(2), 026112+ (2003)
Saito, K., Kimura, M., Ohara, K., Motoda, H.: Learning continuous-time information diffusion model for social behavioral data analysis. In: Zhou, Z.-H., Washio, T. (eds.) ACML 2009. LNCS (LNAI), vol. 5828, pp. 322–337. Springer, Heidelberg (2009)
Watts, D.J., Strogatz, S.H.: Collective dynamics of ‘small-world’ networks. Nature 393(6684), 440–442 (1998)
Wu, F., Huberman, B.A.: How public opinion forms. In: Papadimitriou, C., Zhang, S. (eds.) WINE 2008. LNCS, vol. 5385, pp. 334–341. Springer, Heidelberg (2008)
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Fushimi, T., Saito, K., Kazama, K. (2014). Estimating Network Structure from Anonymous Ego-centric Information. In: Kim, Y.S., Kang, B.H., Richards, D. (eds) Knowledge Management and Acquisition for Smart Systems and Services. PKAW 2014. Lecture Notes in Computer Science(), vol 8863. Springer, Cham. https://doi.org/10.1007/978-3-319-13332-4_19
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DOI: https://doi.org/10.1007/978-3-319-13332-4_19
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