Abstract
Two well-known phenomena are observed in social networks. One is the tendency of users to connect with similar users, leading to the emergence of communities. The other is that certain users belong to multiple communities simultaneously. Understanding these phenomena is the major concern of social network analysis. In this work we focus on overlapping communities detection and personalized recommendation methods. We propose an algorithm with the property which takes closeness and influence of users into account for community detection, and utilizes semantic analysis and statistical analysis for the personalized recommendation. Our contributions include adopting the idea of greedy expansion involved with Clique Theory, extending PageRank to detect communities, and creating recommender from the view of semantics and statistics. In experiments, the algorithm is verified in terms of F1-measure, AP and MAP. The results show that our proposed algorithm can outperform the state-of-the-art methods.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Teutle, A.R.M.: Twitter: network properties analysis. In: 20th Electronics, Communications and Computer (CONIELECOMP), pp. 180–186. IEEE Press, Cholula (2010)
Haewoon, K., Changhyun, L., Hosung, B., Sue, M.: What is Twitter, a social network or a news media? In: 19th International Conference on World Wide Web, Raleigh, pp. 591–600. ACM (2010)
Akshay, J., Xiaodan, S., Tim, F., Belle, T.: Why we Twitter: understanding microblogging usage and communities. In: 9th WebKDD and 1st SNA-KDD 2007 Workshop on Web Mining and Social Network Analysis, San Jose, pp. 56–65. ACM (2007)
Jianshu, W., Ee-Peng, L., Jing, J., Qi, H.: Twitterrank: finding topic-sensitive influential twitterers. In: 3rd International Conference on Web Search and Data Mining, pp. 261–270. ACM, New York (2010)
Jianyong, D., Yamin, A.: LDA topic model for microblog recommendation. In: 8th International Conference on Asian Language Processing, Suzhou, pp. 185–188. IEEE (2015)
Deng, X., Li, G., Dong, M.: Finding overlapping communities with random walks on line graph and attraction intensity. In: Xu, K., Zhu, H. (eds.) WASA 2015. LNCS, vol. 9204, pp. 94–103. Springer, Cham (2015). doi:10.1007/978-3-319-21837-3_10
Yang, J., Leskovec, J.: Overlapping community detection at scale: a nonnegative matrix factorization approach. In: 6th International Conference on Web Search and Data Mining, Rome, pp. 587–596. ACM (2013)
William, H., Matthew, C.S., Paul, B., Nagiza, F.S.: On perturbation theory and an algorithm for maximal clique enumeration in uncertain and noisy graphs. In: 1st ACM SIGKDD Workshop on Knowledge Discovery from Uncertain Data, Paris, pp. 48–56, ACM (2009)
Hailong, Q., Ting, L., Yanjun, M.: Mining users real social circle in microblog. In: 4th International Conference on Advances in Social Networks Analysis and Mining, Istanbul, pp. 348–352. IEEE (2012)
Ba, Q., Li, X., Bai, Z.: A similarity calculating approach simulated from TF-IDF in collaborative filtering recommendation. In: 5th International Conference on Multimedia Information Networking and Security, Beijing, pp. 738–741, IEEE Press (2013)
Fortunato, S., Castellano, C.: Community structure in graphs. In: Meyers, R.A. (ed.) 12th Computational Complexity, pp. 490–512. Springer, New York (2012). doi:10.1007/978-1-4614-1800-9_33
Zhonghua, Q., Yang, L.: Interactive group suggesting for Twitter. In: 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, Portland, pp. 519–523. ACL (2011)
Gergely, P., Imre, D., Illes, F., Tamas, V.: Uncovering the overlapping community structure of complex networks in nature and society. Nature 435(7043), 814–818 (2005)
Leon, D., Albert, D., Jordi, D., Alex, A.: Comparing community structure identification. J. Stat. Mech. Theory Exp. 9, P09008 (2005)
Kim, Y., Shim, K.: TWILITE: a recommendation system for Twitter using a probabilistic model based on latent Dirichlet allocation. Inf. Syst. 42, 59–77 (2014)
Elmongui, H.G., Mansour, R., Morsy, H., Khater, S., El-Sharkasy, A., Ibrahim, R.: TRUPI: Twitter recommendation based on users’ personal interests. In: Gelbukh, A. (ed.) CICLing 2015. LNCS, vol. 9042, pp. 272–284. Springer, Cham (2015). doi:10.1007/978-3-319-18117-2_20
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Yu, R., Wang, J., Xu, T., Gao, J., Cao, K., Yu, M. (2017). Communities Mining and Recommendation for Large-Scale Mobile Social Networks. In: Ma, L., Khreishah, A., Zhang, Y., Yan, M. (eds) Wireless Algorithms, Systems, and Applications. WASA 2017. Lecture Notes in Computer Science(), vol 10251. Springer, Cham. https://doi.org/10.1007/978-3-319-60033-8_24
Download citation
DOI: https://doi.org/10.1007/978-3-319-60033-8_24
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-60032-1
Online ISBN: 978-3-319-60033-8
eBook Packages: Computer ScienceComputer Science (R0)