Link prediction in multiplex online social networks

Royal Society Open Science - Tập 4 Số 2 - Trang 160863 - 2017
Mahdi Jalili1, Yasin Orouskhani2, Milad Asgari Mehrabadi3, Nazanin Alipourfard4, Matjaž Perc5,6
1School of Engineering, RMIT University, Melbourne, Victoria, Australia
2Department of Computer Engineering, Sharif University of Technology, Tehran, Iran
3Department of Computer Science, University of California, Riverside, CA, USA
4Department of Computer Science, University of Southern California, Los Angeles, CA, USA
5Center for Applied Mathematics and Theoretical Physics, University of Maribor, Maribor, Slovenia
6Faculty of Natural Sciences and Mathematics, University of Maribor, Maribor, Slovenia

Tóm tắt

Online social networks play a major role in modern societies, and they have shaped the way social relationships evolve. Link prediction in social networks has many potential applications such as recommending new items to users, friendship suggestion and discovering spurious connections. Many real social networks evolve the connections in multiple layers (e.g. multiple social networking platforms). In this article, we study the link prediction problem in multiplex networks. As an example, we consider a multiplex network of Twitter (as a microblogging service) and Foursquare (as a location-based social network). We consider social networks of the same users in these two platforms and develop a meta-path-based algorithm for predicting the links. The connectivity information of the two layers is used to predict the links in Foursquare network. Three classical classifiers (naive Bayes, support vector machines (SVM) and K-nearest neighbour) are used for the classification task. Although the networks are not highly correlated in the layers, our experiments show that including the cross-layer information significantly improves the prediction performance. The SVM classifier results in the best performance with an average accuracy of 89%.

Từ khóa


Tài liệu tham khảo

10.1098/rsta.2012.0375

10.1016/j.physrep.2016.10.006

10.1016/j.physa.2010.11.027

Liben-Nowelly D Kleinberg J. 2003 The link prediction problem for social networks. In Twelfth Annual ACM Int. Conf. on Information and Knowledge Management pp. 556–559.

10.1073/pnas.0908366106

10.1209/0295-5075/106/18008

10.1073/pnas.1424644112

10.1038/nrg2918

10.1038/nbt.2601

10.1038/nmeth.3773

10.1145/963770.963776

10.1007/s10115-014-0779-2

10.1109/TKDE.2005.99

Basu C Hirsh H Cohen W. 1998 Recommendation as classification: using social and content-based information in recommendation. In National Conference on Artificial Intelligence Wisconsin 26–30 July .

10.1145/245108.245121

Sarwar B Karypis G Konstan J Riedl J. 2001 Item-based collaborative filtering recommendation algorithms. In 10th Int. Conf. on World Wide Web (ACM) Hong Kong 1–5 May .

Aslanian E Radmanesh M Jalili M. 2016 Hybrid recommender systems based on content feature relationship. IEEE Trans. Industrial Informatics . (doi:10.1109/TII.2016.2631138)

10.1126/science.1116869

10.1103/PhysRevLett.107.034101

10.1016/j.physrep.2014.07.001

10.1093/comnet/cnu016

10.1103/PhysRevE.89.052813

10.1016/j.physa.2016.09.030

10.1371/journal.pone.0166787

10.1109/TNSE.2015.2425961

10.1140/epjb/e2015-60270-7

10.1371/journal.pone.0078293

10.1007/978-1-4419-8462-3_9

10.1002/asi.20591

Hasan MA Chaoji V Salem S Zaki M. 2006 Link prediction using supervised learning. In SDM Workshop of Link Analysis Counterterrorism and Security Maryland 22 April .

Song HH Cho TW Dave V Zhang Y. 2009 Scalable proximity estimation and link prediction in online social networks. In Internet Measurement Conf. Chicago IL 4–6 November.

Sun Y Barber R Gupta M Aggarwal CC Han J. 2011 Co-author relationship prediction in heterogeneous bibliographic networks. In Int. Conf. on Advances in Social Networks Analysis and Mining Taiwan 25–27 July pp. 121–128.

Sun Y, 2012, Mining heterogeneous information networks: principles and methodologies, 10.1007/978-3-031-01902-9

Javari A Norouzitallab M Jalili M. Submitted. Who will accept my request? predicting response of link initiation in two-way relation networks.

Hristova D Novals A Brown C Musolesi M Mascolo C. 2015 A multilayer approach to multiplexity and link prediction in online geo-social networks. (http://arxiv.org/abs/1508.07876)

Sun Y Barber R Gupta M Aggarwal CC Han J. 2011 Co-author relationship prediction in heterogeneous bibliographic networks. In Adv. Social Networks Analysis and Mining (ASONAM) 2011 Int. Conf. Taiwan 25–27 July pp. 121–128. IEEE.

10.1109/TST.2013.6574671

10.1145/2481244.2481248

Leskovec J Huttenlocher D Kleinberg J. 2010 Signed networks in social media. In SIGCHI Conf. on Human Factors in Computing Systems Atlanta GA 10–15 April pp. 1361–1370.

Guha RV Kumar R Raghavan P Tomkins A. 2004 Propagation of trust and distrust. In Proc. World Wide Web New York 17–20 May pp. 403–412.

Leskovec J Huttenlocher D Kleinberg J. 2010 Predicting positive and negative links in online social networks . In Int. Conf. on World Wide Web pp. 641–650.

Shahriari M, 2014, Ranking nodes in signed social networks, Soc. Netw. Anal. Mining, 4, 1

Javari A, 2014, Cluster-based collaborative filtering for sign prediction in social networks with positive and negative links, ACM Trans. Intelligent Syst. Technol., 5, 24

10.1103/PhysRevE.90.042817

10.1038/srep14339

Edler D Rosvall M. 2015 The infomap software package . See http://www.mapequation.org/.

10.1073/pnas.0706851105

10.1007/BF00994018

Lewis DD. 1998 Naive (Bayes) at forty: the independence assumption in information retrieval. In Machine learning: ECML-98 pp. 4–15. Berlin Germany: Springer.

Altman NS, 1992, An introduction to kernel and nearest-neighbor nonparametric regression, Am. Stat., 46, 175, 10.1080/00031305.1992.10475879

Thông tin
Thông tin xuất bản

Royal Society Open Science

Tập 4 Số 2

160863

Thông tin tác giả