Abstract
The study of link prediction has attracted increasing attention with the booming social networks. Researchers utilized topological features of networks and the attribute features of nodes to predict new links in the future or find the missing links in the current network. Some of the works take topic into consideration, but they don’t think of the social influence that has potential impacts on link prediction. Hence, it leads us to introduce social influence into topics to find contexts. In this paper, we propose a novel model under the collaborative filter framework and improve the link prediction by exploiting context-aware social influence. We also adopt the clustering algorithm with the use of topological features, thus we incorporate the social influence, topic and topological structure to improve the quality of link prediction. We test our method on Digg data set and the results of the experiment demonstrate that our method performs better than the traditional approaches.
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References
Wang, P., Xu, B.W., Wu, Y.R., et al.: Link prediction in social networks: the state-of-the-art. Sci. China Inf. Sci. 58(1), 1–38 (2015)
Barbieri, N., Bonchi, F., Manco, G.: Who to follow and why: link prediction with explanations. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1266–1275. ACM (2014)
Backstrom, L., Huttenlocher, D., Kleinberg, J., et al.: Group formation in large social networks: membership, growth, and evolution. In: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 44–54. ACM (2006)
Lin, D.: An information-theoretic definition of similarity. In: Icml, vol. 1998, no. 98, pp. 296–304 (1998)
Leicht, E.A., Holme, P., Newman, M.E.J.: Vertex similarity in networks. Phys. Rev. E 73(2), 026120 (2006)
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, p. 9. ACM (2009)
Aslan, S., Kaya, M.: Topic recommendation for authors as a link prediction problem. Future Gener. Comput. Syst. 89, 249–264 (2018)
Bakshy, E., Karrer, B., Adamic, L.A.: Social influence and the diffusion of user-created content. In: Proceedings of the 10th ACM Conference on Electronic Commerce, pp. 325–334. ACM (2009)
Yang, Y., Jia, J., Wu, B., et al.: Social role-aware emotion contagion in image social networks. In: Thirtieth AAAI Conference on Artificial Intelligence (2016)
Li, J., Liu, C., Yu, J.X., et al.: Personalized influential topic search via social network summarization. IEEE Trans. Knowl. Data Eng. 28(7), 1820–1834 (2016)
Nguyen, J.H., Hu, B., GĂĽnnemann, S., et al.: Finding contexts of social influence in online social networks. In: Proceedings of the 7th Workshop on Social Network Mining and Analysis, p. 1. ACM (2013)
Sharma, P.K., Rathore, S., Park, J.H.: Multilevel learning based modeling for link prediction and users’ consumption preference in online social networks. Future Gener. Comput. Syst. (2017)
Wang, X., He, D., Chen, D., et al.: Clustering-based collaborative filtering for link prediction. In: Twenty-Ninth AAAI Conference on Artificial Intelligence (2015)
Wang, C., Satuluri, V., Parthasarathy, S.: Local probabilistic models for link prediction. In: Seventh IEEE International Conference on Data Mining, ICDM 2007, pp. 322–331. IEEE (2007)
Pujari, M., Kanawati, R.: Supervised rank aggregation approach for link prediction in complex networks. Proceedings of the 21st International Conference on World Wide Web, pp. 1189–1196. ACM (2012)
Ahmed, C., ElKorany, A., Bahgat, R.: A supervised learning approach to link prediction in Twitter. Soc. Netw. Anal. Min. 6(1), 24 (2016)
Anagnostopoulos, A., Kumar, R., Mahdian, M.: Influence and correlation in social networks. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 7–15. ACM (2008)
La Fond, T., Neville, J.: Randomization tests for distinguishing social influence and homophily effects. In: Proceedings of the 19th International Conference on World Wide Web, pp. 601–610. ACM (2010)
Singla, P., Richardson, M.: Yes, there is a correlation:-from social networks to personal behavior on the web. Proceedings of the 17th International Conference on World Wide Web, pp. 655–664. ACM (2008)
Kempe, D., Kleinberg, J., Tardos,É.: Maximizing the spread of influence through a social network. In: Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 137–146. ACM (2003)
Tang, J., Sun, J., Wang, C., et al.: Social influence analysis in large-scale networks. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 807–816. ACM (2009)
Goyal, A., Bonchi, F., Lakshmanan, L.V.S.: Learning influence probabilities in social networks. In: Proceedings of the Third ACM International Conference on Web Search and Data Mining, pp. 241–250. ACM (2010)
Xiang, R., Neville, J., Rogati, M.: Modeling relationship strength in online social networks. In: Proceedings of the 19th International Conference on World Wide Web, pp. 981–990. ACM (2010)
Su, X., Khoshgoftaar, T.M.: A survey of collaborative filtering techniques. Adv. Artif. Intell. 2009, 1–19 (2009)
Goldberg, D., Nichols, D., Oki, B.M., et al.: Using collaborative filtering to weave an information tapestry. Commun. ACM 35(12), 61–71 (1992)
Linden, G., Smith, B., York, J.: Amazon. com recommendations: item-to-item collaborative filtering. IEEE Internet Comput. 2003(1), 76–80 (2003)
Hofmann, T.: Latent semantic models for collaborative filtering. ACM Trans. Inf. Syst. (TOIS) 22(1), 89–115 (2004)
Su, X., Khoshgoftaar, T.M.: Collaborative filtering for multi-class data using belief nets algorithms. In: 18th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2006, vol. 2006, pp. 497–504. IEEE (2006)
Su, X., Khoshgoftaar, T.M., Zhu, X., et al.: Imputation-boosted collaborative filtering using machine learning classifiers. In: Proceedings of the 2008 ACM Symposium on Applied Computing, pp. 949–950. ACM (2008)
Melville, P., Mooney, R.J., Nagarajan, R.: Content-boosted collaborative filtering for improved recommendations. In: Aaai/iaai, vol. 23, pp. 187–192 (2002)
Pavlov, D.Y., Pennock, D.M.: A maximum entropy approach to collaborative filtering in dynamic, sparse, high-dimensional domains. In: Advances in Neural Information Processing Systems, pp. 1465–1472 (2003)
Aslan, S., Kaya, M.: Topic recommendation for authors as a link prediction problem. Future Gener. Comput. Syst. 89, 249–264 (2018)
Cha, M., Haddadi, H., Benevenuto, F., et al.: Measuring user influence in Twitter: the million follower fallacy. In: Fourth International AAAI Conference on Weblogs and Social Media (2010)
Liben-Nowell, D., Kleinberg, J.: The link-prediction problem for social networks. J. Am. Soc. Inf. Sci. Technol. 58(7), 1019–1031 (2007)
Newman, M.E.J.: Clustering preferential attachment in growing networks. Phys. Rev. E 64(2), 025102 (2001)
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Gao, H., Zhang, Y., Li, B. (2019). Improving the Link Prediction by Exploiting the Collaborative and Context-Aware Social Influence. In: Li, J., Wang, S., Qin, S., Li, X., Wang, S. (eds) Advanced Data Mining and Applications. ADMA 2019. Lecture Notes in Computer Science(), vol 11888. Springer, Cham. https://doi.org/10.1007/978-3-030-35231-8_22
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