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Towards a Framework for Aiding the Collaborative Management of Informal Projects Read chapter Read first chapter

2018 | OriginalPaper | Chapter

Using a Time-Based Weighting Criterion to Enhance Link Prediction in Social Networks

Authors : Carlos Pedro Muniz, Ronaldo Goldschmidt, Ricardo Choren

Published in: Enterprise Information Systems

Publisher: Springer International Publishing

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Abstract

Recently, the link prediction (LP) problem has attracted much attention from both scientific and industrial communities. This problem tries to predict whether two not linked nodes in a network will connect in the future. Several studies have been proposed to solve it. Some of them compute a compatibility degree (link strength) between connected nodes and apply similarity metrics between non-connected nodes in order to identify potential links. However, despite the acknowledged importance of temporal data for the LP problem, few initiatives investigated the use of this kind of information to represent link strength. In this paper, we propose a weighting criterion that combines the frequency of interactions and temporal information about them in order to define the link strength between pairs of connected nodes. The results of our experiment with weighted and non-weighted similarity metrics in ten co-authorship networks present statistical evidences that confirm our hypothesis that weighting links based on temporal information may, in fact, improve link prediction.

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Footnotes
Literature
1.
Wang, P., Xu, B., Wu, Y., Zhou, X.: Link prediction in social networks: the state-of-the-art. Sci. Chin. Inf. Sci. 58(1), 1–38 (2015)
2.
3.
Borgatti, S.P., Mehra, A., Brass, D.J., Labianca, G.: Network analysis in the social sciences. Science 323(5916), 892–895 (2009)CrossRef
4.
Wang, H., Hu, W., Qiu, Z., Du, B.: Nodes’ evolution diversity and link prediction in social networks. IEEE Trans. Knowl. Data Eng. 29(10), 2263–2274 (2017)CrossRef
5.
Liben-Nowell, D., Kleinberg, J.: The link-prediction problem for social networks. J. Am. Soc. Inf. Sci. Technol. 58(7), 1019–1031 (2007)CrossRef
6.
Aiello, L.M., Barrat, A., Schifanella, R., Cattuto, C., Markines, B., Menczer, F.: Friendship prediction and homophily in social media. ACM Trans. Web 6(2), 1–33 (2012)CrossRef
7.
Adamic, L.A., Adar, E.: Friends and neighbors on the Web. Soc. Netw. 25(3), 211–230 (2003)CrossRef
8.
Murata, T., Moriyasu, S.: Link prediction of social networks based on weighted proximity measures. In: Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence, pp. 85–88 (2007)
9.
Lü, L., Zhou, T.: Link prediction in weighted networks: the role of weak ties. Europhys. Lett. 89(1), 18001 (2010)CrossRef
10.
Munasinghe, L., Ichise, R.: Time score: a new feature for link prediction in social networks. IEICE Trans. Inf. Syst. E95.D(3), 821–828 (2012)CrossRef
11.
Choudhary, P., Mishra, N., Sharma, S., Patel, R.: Link score: a novel method for time aware link prediction in social network. In: Prasad, D.N.H., Nalini, D.N. (eds.) Emerging Research in Computing, Information, Communication and Applications. Springer, New Delhi (2013)
12.
Valverde-Rebaza, J., Valejo, A., Berton, L., de Paulo Faleiros, T., de Andrade Lopes, A.: A naïve bayes model based on overlapping groups for link prediction in online social networks. In: Proceedings of the 30th Annual ACM Symposium on Applied Computing, pp. 1136–1141 (2015)
13.
Zhu, B., Xia, Y.: Link prediction in weighted networks: a weighted mutual information model. PLoS ONE 11(2), e0148265 (2016)CrossRef
14.
Hasan, M.A., Chaoji, V., Salem, S., Zaki, M.: Link prediction using supervised learning. In: Proceedings of SDM 2006 Workshop on Link Analysis, Counterterrorism and Security, pp. 1–10 (2006)
15.
Granovetter, M.S.: The strength of weak ties. Am. J. Sociol. 78, 1360–1380 (1973)CrossRef
16.
Katz, L.: A new status index derived from sociometric analysis. Psychometrika 18(1), 39–43 (1953)CrossRef
17.
Salton, G., McGill, M.J.: Introduction to Modern Information Retrieval. McGraw-Hill Inc, New York (1986)MATH
18.
Jeh, G., Widom, J.: SimRank: a measure of structural-context similarity. In: Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 538–543 (2002)
19.
20.
Gündoǧan, E., Kaya, B.: A recommendation method based on link prediction in drug-disease bipartite network. In: Proceedings of 2nd International Conference on Advanced Information and Communication Technologies, pp. 125–128 (2017)
21.
Newman, M.E.J.: Clustering and preferential attachment in growing networks. Phys. Rev. E 64, 13 (2001)
22.
Barabási, A.-L., Bonabeau, E.: Scale-free networks. Sci. Am. 288(5), 50–59 (2003)CrossRef
23.
Tan, P.-N., Steinbach, M., Kumar, V.: Introduction to Data Mining. Pearson, New York (2005)
24.
Zhao, J., Miao, L., Yang, J., Fang, H., Zhang, Q.-M., Nie, M., Holme, P., Zhou, T.: Prediction of links and weights in networks by reliable routes. Sci. Rep. 5, 11770 (2015)CrossRef
25.
Tylenda, T, Angelova, R., Bedathur, S.: Towards time-aware link prediction in evolving social networks. In: Proceedings of the 3rd Workshop on Social Network Mining and Analysis, pp. 1–10 (2009)
26.
Tang, J., Musolesi, M., Mascolo, C., Latora, V.: Temporal distance metrics for social network analysis. In: Proceedings of the 2nd ACM Workshop on Online Social Networks, pp. 31–36 (2009)
27.
Demšar, J.: Statistical comparisons of classifiers over multiple data sets. J. Mach. Learn. Res. 7, 1–30 (2006)MathSciNetMATH
28.
Muniz, C., Choren, Goldschmidt, R.: Using a time based relationship weighting criterion to improve link prediction in social networks. In: Proceedings of the 19th International Conference on Enterprise Information Systems - Volume 1, ICEIS, pp. 73–79 (2017)
Metadata
Title
Using a Time-Based Weighting Criterion to Enhance Link Prediction in Social Networks
Authors
Carlos Pedro Muniz
Ronaldo Goldschmidt
Ricardo Choren
Copyright Year
2018
Book
Enterprise Information Systems

Print ISBN: 978-3-319-93374-0

Electronic ISBN: 978-3-319-93375-7

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-319-93375-7_2

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