Abstract
Link prediction has attracted wide attention in the related fields of social networks which has been widely used in many domains, such as, identifying spurious interactions, extracting missing information, evaluating evolving mechanism of complex networks. But all of the previous works do not considering the influence of the neighbors and just applying in small networks. In this paper, a new similarity algorithm is proposed, which is motivated by the herd phenomenon taking place on network. Moreover, it is found that many links are assigned low scores while it has a longer path. Therefore, if such links the longer path has not been taken into account, which can improve the efficiency of time further, especially in large-scale networks. Extensive experiments were conducted on five real-world social networks, compared with the representative node similarity-based methods, our proposed model can provide more accurate predictions.
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 subscriptionsReferences
Lü, L.Y., Zhou, T.: Link prediction in complex networks: a survey. Phys. A: Stat. Mech. Appl. 390, 1150–1170 (2011)
Clauset, A., Moore, C., Newman, M.E.J.: Hierarchical structure and the prediction of missing links in networks. Nature 453, 98–101 (2008)
O’Madadhain, J., Hutchins, J., Smyth, P.: Prediction and ranking algorithms for event-based network data. ACM SIGKDD 7(2), 23–30 (2005)
Han, X., Wang, L.Y., Chen C., Farahbakhsh, R.: Link prediction for new users in social networks. In: ICC, pp. 1250–1255 (2015)
Lü, L.Y., Pan, L.M., Zhou, T., Zhang, Y.C., Stanley, H.E.: Toward link predictability of complex networks. PANS 112, 2325–2330 (2015)
Zhang, W.Y., Wu, B.: Accurate and fast link prediction in complex networks. In: Natural Computation (ICNC), pp. 653–657 (2014)
Yin, G., Yin W.S., Dong, Y.X.: A new link prediction algorithm: node link strength algorithm. In: SCAC, pp. 5–9 (2014)
Abir, D., Nilloy, G., Soumen, C.: Discriminative link prediction using local links, node features and community structure. In: Data Mining (ICDM), pp. 1009–1018 (2013)
Valverde-Rebaza, J., Lopes, A.: Exploiting behaviors of communities of twitter users for link prediction. Soc. Netw. Anal. Min. 3, 1063–1074 (2013)
Li, X., Chen, H.: Recommendation as link prediction in bipartite graphs: a graph kernel-based machine learning approach. Decis. Support Syst. 54, 880–890 (2013)
Scellato, S., Noulas, A., Mascolo, C.: Exploiting place features in link prediction on location-based social networks. In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Diego, pp. 1046–1054 (2011)
Chen, Z., Zhang, W.: A marginalized denoising method for link prediction in relational data. In: Proceedings of the 2014 SIAM International Conference on Data Mining, Philadelphia, pp. 298–306 (2014)
Menon, K., Elkan, C.: Link prediction via matrix factorization. In: Proceedings of the 2011 European Conference on Machine Learning and Knowledge Discovery in Databases, Athens, pp. 437–452 (2011)
Ozcan, A., Sule, O.G.: Multivariate temporal link prediction in evolving social networks. In: 2015 IEEE/ACIS 14th International Conference on Computer and Information Science (ICIS), pp. 185–190 (2015)
Zhu, M., Zhou, Y.: Density-based link clustering algorithm for overlapping community detection. J. Comput. Res. Dev. 2520–2530 (2013)
Newman, M.E.J.: Clustering and preferential attachment in growing networks. Phys. Rev. E 64, 025102 (2001)
Adamic, L.A., Adar, E.: Friends and neighbors on the web. Soc. Netw. 25, 211–230 (2003)
Zhou, T., Lü, L., Zhang, Y.C.: Predicting missing links via local information. Eur. Phys. J. B 71, 623–630 (2009)
Hanley, J.A., McNeil, B.J.: A method of comparing the areas under receiver operating characteristic curves derived from the same cases. Radiology 148, 839–843 (1983)
Zhu, M., Meng, F.R., Zhou, Y.: Density-based link clustering algorithm for overlapping community detection. 2520–2530 (2013)
Herlocker, J.L., Konstan, J.A., Terveen, L.G., Riedl, J.T.: Evaluating collaborative filtering recommender systems. ACM TOIS 22, 5–53 (2004)
McAuley, J., Leskovec, J.: Learning to discover social circles in ego networks. Adv. Neural Inf. Process. Syst. 25, 548–556 (2012)
Zhou, S., Mondragon, R.J.: The rich-club phenomenon in the internet topology. IEEE Common. Lett. 8, 180–182 (2004)
Acknowledgments
This work is partly funded by the National Nature Science Foundation of China (61075019) and the Natural Science Foundation of Chongqing (CSTC2014jcyjA40047) and the Municipal Education Commission research project of Chongqing (KJ1400403).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Kuang, R., Liu, Q., Yu, H. (2016). Community-Based Link Prediction in Social Networks. In: Tan, Y., Shi, Y., Li, L. (eds) Advances in Swarm Intelligence. ICSI 2016. Lecture Notes in Computer Science(), vol 9713. Springer, Cham. https://doi.org/10.1007/978-3-319-41009-8_37
Download citation
DOI: https://doi.org/10.1007/978-3-319-41009-8_37
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-41008-1
Online ISBN: 978-3-319-41009-8
eBook Packages: Computer ScienceComputer Science (R0)