logo

Journals Books
Advanced Search
Sign in Register

中文

SCIENCE CHINA Information Sciences
Download PDF
SCIENCE CHINA Information Sciences, Volume 58 , Issue 1 : 011101-011101(2015) https://doi.org/10.1007/s11432-014-5237-y

Link prediction in social networks: the state-of-the-art

Peng WANG 1,2,*, BaoWen XU 1,2,3,*, YuRong WU 1, XiaoYu ZHOU 1
More info
  • ReceivedJun 1, 2020
  • AcceptedNov 4, 2014
  • PublishedDec 26, 2014
Previous Table of Contents Next
Share
Rating

Abstract

In social networks, link prediction predicts missing links in current networks and new or dissolution links in future networks, is important for mining and analyzing the evolution of social networks. In the past decade, many works have been done about the link prediction in social networks. The goal of this paper is to comprehensively review, analyze and discuss the state-of-the-art of the link prediction in social networks. A systematical category for link prediction techniques and problems is presented. Then link prediction techniques and problems are analyzed and discussed. Typical applications of link prediction are also addressed. Achievements and roadmaps of some active research groups are introduced. Finally, some future challenges of the link prediction in social networks are discussed.


References

[1] Getoor L, Diehl C P. Link mining: a survey. ACM SIGKDD Explor Newslett, 2005, 7: 3-12. Google Scholar

[2] Aiello L M, Barrat A, Schifanella R, et al. Friendship prediction and homophily in social media. ACM Trans Web, 2012, 6: 9. Google Scholar

[3] Mori J, Kajikawa Y, Kashima H, et al. Machine learning approach for finding business partners and building reciprocal relationships. Expert Syst Appl, 2012, 39: 10402-10407. Google Scholar

[4] Wu S, Sun J, Tang J. Patent partner recommendation in enterprise social networks. In: Proceedings of the 6th ACM International Conference on Web Search and Data Mining (WSDM'13), Rome, 2013. 43-52. Google Scholar

[5] Akcora C G, Carminati B, Ferrari E. Network and profile based measures for user similarities on social networks. In: Proceedings of the 12th IEEE International Conference on Information Reuse and Integration, Las Vegas, 2011. 292-298. Google Scholar

[6] Tang J, Wu S, Sun J M, et al. Cross-domain collaboration recommendation. In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'12), Beijing, 2012. 1285-1293. Google Scholar

[7] Pavlov M, Ichise R. Finding experts by link prediction in co-authorship networks. In: Proceedings of the 2nd International ISWC+ASWC Workshop on Finding Experts on the Web with Semantics (FEWS), Busan, 2007. 42-55. Google Scholar

[8] Wohlfarth T, Ichise R. Semantic and event-based approach for link prediction. In: Proceedings of the 7th International Conference on Practical Aspects of Knowledge Management (PAKM'08), Yokohama, 2008. 50-61. Google Scholar

[9] Raeder T, Lizardo O, Hachen D, et al. Predictors of short-term decay of cell phone contacts in a large scale communication network. Soc Netw, 2011, 33: 245-257. Google Scholar

[10] Marchette D J, Priebe C E. Predicting unobserved links in incompletely observed networks. Comput Stat Data Anal, 2008, 52: 1373-1386. Google Scholar

[11] Kim M, Leskovec J. The network completion problem: inferring missing nodes and edges in networks. In: Proceedings of the 11th SIAM International Conference on Data Mining (SDM'11), Mesa, 2011. 47-58. Google Scholar

[12] Barabási A L, Jeong H, Néda Z, et al. Evolution of the social network of scientific collaborations. Physica A, 2002, 311: 590-614. Google Scholar

[13] Juszczyszyn K, Musial K, Budka M. Link prediction based on subgraph evolution in dynamic social networks. In: Proceedings of the 2011 IEEE International Conference on Social Computing, Boston, 2011. 27-34. Google Scholar

[14] Bringmann B, Berlingerio M, Bonchi F, et al. Learning and predicting the evolution of social networks. IEEE Intell Syst, 2010, 25: 26-35. Google Scholar

[15] Raymond R, Kashima H. Fast and scalable algorithms for semi-supervised link prediction on static and dynamic graphs. In: Proceedings of ECML/PKDD'10, Barcelona, 2010. 131-147. Google Scholar

[16] Zhu J, Hong J, Hughes J G. Using Markov models for web site link prediction. In: Proceedings of the 13th ACM Conference on Hypertext and Hypermedia (HYPERTEXT'02), Maryland, 2002. 169-170. Google Scholar

[17] Almansoori W, Gao S, Jarada T N, et al. Link prediction and classification in social networks and its application in healthcare and systems biology. Netw Model Anal Health Inform Bioinform, 2012, 1: 27-36. Google Scholar

[18] Huang Z, Lin D K J. The time-series link prediction problem with applications in communication surveillance. INFORMS J Comput, 2009, 21: 286-303. Google Scholar

[19] Liben-Nowell D, Kleinberg J M. The link-prediction problem for social networks. J Am Soc Inf Sci Technol, 2007, 58: 1019-1031. Google Scholar

[20] Hasan M A, Zaki M. A survey of link prediction in social networks. In: Aggarwal C C, ed. Social Network Data Analytics. Springer: New York, 2011. 243-275. Google Scholar

[21] Lü L, Zhou T. Link prediction in complex networks: a survey. Physica A, 2011, 390: 1150-1170. Google Scholar

[22] Hasan M A, Chaoji V, Salem S, et al. Link prediction using supervised learning. In: Proceedings of SDM'06Workshop on Link Analysis, Counter terrorism and Security, Bethesda, 2006. Google Scholar

[23] Hanely J A, McNeil B J. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology, 1982, 143: 29-36. Google Scholar

[24] Lichtnwalter R, Chawla N V. Link prediction: fair and effective evaluation. In: Proceeding of the 2012 IEEE/ACM International Conference on Advanced in Social Networks Analysis and Mining (ASONAM'12), Istanbul, 2012. 376-383. Google Scholar

[25] Hall P A V, Dowling G R. Approximate string matching. ACM Comput Surv, 1980, 12: 381-402. Google Scholar

[26] Navarro G. A guided tour to approximate string matching. ACM Comput Surv, 2001, 33: 31-88. Google Scholar

[27] Bhattacharyya P, Garg A, Wu F S. Analysis of user keyword similarity in online social networks. Soc Netw Anal Min, 2011, 1: 143-158. Google Scholar

[28] Akcora C G, Carminati B, Ferrari E. User similarities on social networks. Soc Netw Anal Min, 2013, 3: 475-495. Google Scholar

[29] Anderson A, Huttenlocher D, Kleinberg J, et al. Effects of user similarity in social media. In: Proceedings of the 5th ACM International Conference on Web Search and Data Mining (WSDM'12), Seattle, 2012. 703-712. Google Scholar

[30] Newman M E J. Clustering and preferential attachment in growing networks. Phys Rev E, 2001, 64: 025102. Google Scholar

[31] Ravasz E, Somera A L, Mongru D A, et al. Hierarchical organization of modularity in metabolic networks. Science, 2002, 297: 1551-1555. Google Scholar

[32] Zhou T, Lü L, Zhang Y C. Predicting missing links via local information. Eur Phys J B, 2009, 71: 623-630. Google Scholar

[33] Leicht E A, Holme P, Newman M E J. Vertex similarity in networks. Phys Rev E, 2006, 73: 026120. Google Scholar

[34] Zhu Y X, Lü L, Zhang Q M, et al. Uncovering missing links with cold ends. Physica A, 2012, 391: 5769-5778. Google Scholar

[35] Adamic L A, Adar E. Friend and neighbors on the web. Soc Networks, 2003, 25: 211-230. Google Scholar

[36] Sarkar P, Chakrabarti D, Moore A W. Theoretical justification of popular link prediction heuristics. In: Proceedings of the 22nd International Joint Conference on Artificial Intelligence, Barcelona, 2011. 2722-2727. Google Scholar

[37] Lü L, Jin C H, Zhou T. Similarity index based on local paths for link prediction of complex networks. Phys Rev E, 2009, 80: 046122. Google Scholar

[38] Katz L. A new status index derived from sociometric analysis. Psychometrika, 1953, 18: 39-43. Google Scholar

[39] Chen H H, Gou L, Zhang X L, et al. Discovering missing links in networks using vertex similarity measures. In: Proceedings of the Twenty-Seventh Annual ACM Symposium on Applied Computing (SAC'12), Trento, 2012. 138-143. Google Scholar

[40] Papadimitriou A, Symeonidis P, Manolopoulos Y. Fast and accurate link prediction in social networking systems. J Syst Softw, 2012, 85: 2119-2132. Google Scholar

[41] Lichtenwalter R N, Chawla N V. Vertex collocation profiles: subgraph counting for link analysis and prediction. In: Proceedings of the 21st World Wide Web Conference(WWW'12), Lyon, 2012. 1019-1028. Google Scholar

[42] Lichtenwalter R N, Chawla N V. Vertex collocation profiles: theory, computation, and results. SpringerPlus, 2014, 3: 116. Google Scholar

[43] Meng B, Ke H, Yi T. Link prediction based on a semi-local similarity index. Chin Phys B, 2011, 20: 128902. Google Scholar

[44] Feng X, Zhao J C, Xu K. Link prediction in complex networks: a clustering perspective. Eur Phys J B, 2012, 85: 1-9. Google Scholar

[45] Fouss F, Pirotte A, Renders J M, et al. Random-walk computation of similarities between nodes of a graph with application to collaborative recommendation. IEEE Trans Knowl Data Eng, 2007, 19: 355-369. Google Scholar

[46] 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 (KDD'02), Edmonton, 2002. 538-543. Google Scholar

[47] Lichtenwalter R N, Lussier J T, Chawla N V. New perspectives and methods in link prediction. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2010. 243-252. Google Scholar

[48] Symeonidis P, Iakovidou N, Mantas N, et al. From biological to social networks: link prediction based on multi-way spectral clustering. Data Knowl Eng, 2013, 87: 226-242. Google Scholar

[49] Symeonidis P, Mantas N. Spectral clustering for link prediction in social networks with positive and negative links. Soc Netw Anal Min, 2013, 3: 1433-1447. Google Scholar

[50] Valverde-Rebaza J, Lopes A A. Exploiting behaviors of communities of twitter users for link prediction. Soc Netw Anal Min, 2013, 3: 1063-1074. Google Scholar

[51] Liu H, Hu Z, Haddadi H, et al. Hidden link prediction based on node centrality and weak ties. Europhys Lett, 2013, 101: 18004. Google Scholar

[52] Li R H, Yu J X, Liu J. Link prediction: the power of maximal entropy random walk. In: Proceedings of the 20th ACM international Conference on Information and Knowledge Management, Glasgow, 2011. 1147-1156. Google Scholar

[53] Qiu B, Ivanova K, Yen J, et al. Behavior evolution and event-driven growth dynamics in social networks. In: Proceedings of IEEE Second International Conference on Social Computing (SocialCom), Boston, 2010. 217-224. Google Scholar

[54] Qiu B, He Q, Yen J. Evolution of node behavior in link prediction. In: Proceedings of the 25th AAAI Conference on Artificial Intelligence, San Francisco, 2011. 1810-1811. Google Scholar

[55] Yang S H, Long B, Smola A, et al. Like like alike: joint friendship and interest propagation in social networks. In: Proceedings of the 20th International Conference on World Wide Web (WWW'11), Hyderabad. 2011. 537-546. Google Scholar

[56] Dong Y, Tang J, Wu S, et al. Link prediction and recommendation across heterogeneous social networks. In: Proceedings of IEEE 12th International Conference on Data Mining, Brussels, 2012. 181-190. Google Scholar

[57] Li X, Chen H. Recommendation as link prediction in bipartite graphs: a graph kernel-based machine learning approach. Decis Support Syst, 2013, 54: 880-890. Google Scholar

[58] 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, 2011. 1046-1054. Google Scholar

[59] Scripps J, Tan P N, Chen F, et al. A matrix alignment approach for link prediction. In: Proceedings of the 19th International Conference on Pattern Recognition (ICPR'08), Tampa, 2008. 1-4. Google Scholar

[60] de Sá H R, Prudêncio R B C. Supervised link prediction in weighted networks. In: Proceedings of the 2011 International Joint Conference on Neural Networks (IJCNN), San Jose, 2011. 2281-2288. Google Scholar

[61] Lü L, Zhou T. Role of weak ties in link prediction of complex networks. In: Proceedings of the First ACM International Workshop on Complex Networks Meet Information & Knowledge Management, New York, 2009. 55-58. Google Scholar

[62] Kunegis J, Lommatzsch A. Learning spectral graph transformations for link prediction. In: Proceedings of the 26th Annual International Conference on Machine Learning, Montreal, 2009. 561-568. Google Scholar

[63] Pujari M, Kanawati R. Link prediction in complex networks by supervised rank aggregation. In: Proceedings of IEEE 24th International Conference on Tools with Artificial Intelligence, Athens, 2012. 782-789. Google Scholar

[64] Chiang K Y, Natarajan N, Tewari A, et al. Exploiting longer cycles for link prediction in signed networks. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management, Glasgow, 2011. 1157-1162. Google Scholar

[65] Leskovec J, Huttenlocher D, Kleinberg J. Predicting positive and negative links in online social networks. In: Proceedings of the 19th International Conference on World Wide Web (WWW'10), Raleigh, 2010. 641-650. Google Scholar

[66] Cao B, Liu N N, Yang Q. Transfer learning for collective link prediction in multiple heterogeneous domains. In: Proceedings of the 27th International Conference on Machine Learning, Haifa, 2010. 159-166. Google Scholar

[67] Lu Z D, Savas B, Tang W, et al. Supervised link prediction using multiple sources. In: Proceedings of the 10th IEEE International Conference on Data Mining, Sydney, 2010. 923-928. Google Scholar

[68] Brouard C, Szafranski M. Semi-supervised penalized output kernel regression for link prediction. In: Proceedings of the 28th International Conference on Machine Learning (ICML'11), Bellevue, 2011. 593-600. Google Scholar

[69] Kashima H, Kato T, Yamanishi Y, et al. Link propagation: a fast semi-supervised learning algorithm for link prediction. In: Proceedings of the 9th SIAM International Conference on Data Mining (SDM'09), Sparks, 2009. 1099-1110. Google Scholar

[70] Clauset A, Moore C, Newman M E J. Hierarchical structure and the prediction of missing links in networks. Nature, 2008, 453: 98-101. Google Scholar

[71] Guimeràa R, Sales-Pardo M. Missing and spurious interactions and the reconstruction of complex networks. Proc Nat Acad Sci, 2009, 106: 22073-22078. Google Scholar

[72] 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, 2014. 298-306. Google Scholar

[73] Wang C, Satuluri V, Parthasarathy S. Local probabilistic models for link prediction. In: Proceeding of the 7th IEEE International Conference on Data Mining (ICDM'07), Omaha, 2007. 322-331. Google Scholar

[74] Kashima H, Abe N. A parameterized probabilistic model of network evolution for supervised link prediction. In: Proceedings of the 6th International Conference on Data Mining (ICDM'06), Hong Kong, 2006. 340-349. Google Scholar

[75] Backstrom L, Leskovec J. Supervised random walks: predicting and recommending links in social networks. In: Proceedings of the 4th ACM International Conference on Web Search and Data Mining (WSDM'11), Hong Kong, 2011. 635-644. Google Scholar

[76] Yin D, Hong L, Davison B D. Structural link analysis and prediction in microblogs. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management (CIKM'11), Glasgow, 2011. 1163-1168. Google Scholar

[77] Leroy V, Cambazoglu B B, Bonchi F. Cold start link prediction. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Washington, 2010. 393-402. Google Scholar

[78] Kuo T, Yan R, Huang Y, et al. Unsupervised link prediction using aggregative statistics on heterogeneous social networks. In: Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining, Chicago, 2013. 775-783. Google Scholar

[79] Menon A K, Elkan C. Link prediction via matrix factorization. In: Proceedings of the 2011 European Conference on Machine Learning and Knowledge Discovery in Databases, Athens, 2011. 437-452. Google Scholar

[80] Lichtenwalter R N, Chawla N V. LPmade: link prediction made easy. J Mach Learn Res, 2011, 12: 2489-2492. Google Scholar

[81] Dunlavy D M, Kolda T G, Acar E. Temporal link prediction using matrix and tensor factorizations. ACM Trans Knowl Discov D, 2011, 5: 1-27. Google Scholar

[82] O'Madadhain J, Hutchins J, Smyth P. Prediction and ranking algorithms for event-based network data. ACM SIGKDD Explor, 2005, 7: 23-30. Google Scholar

[83] Gao S, Denoyer L, Gallinari P. Temporal link prediction by integrating content and structure information. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management (CIKM'11), Glasgow, 2011. 1169-1174. Google Scholar

[84] Tylenda T, Angelova R, Bedathur S. Towards time-aware link prediction in evolving social networks. In: Proceedings of the Third Workshop on Social Network Mining and Analysis, Paris, 2009. 9-18. Google Scholar

[85] da Silva Soares P R, Bastos Cavalcante Prudêncio R. Time series based link prediction. In: Proceedings of 2012 International Joint Conference on Neural Networks (IJCNN'12), Brisbane, 2012. 1-7. Google Scholar

[86] Oyama S, Hayashi K, Kashima H. Cross-temporal link prediction. In: Proceedings of the 11th IEEE International Conference on Data Mining (ICDM'11), Vancouver, 2011. 1188-1193. Google Scholar

[87] Munasinghe L, Ichise R. Time score: a new feature for link prediction in social networks. IEICE Trans Inform Syst, 2012, E95-D: 821-828. Google Scholar

[88] Soares P R S, Prudêncio R B C. Proximity measures for link prediction based on temporal events. Expert Syst Appl, 2013, 40: 6652-6660. Google Scholar

[89] Richard E, Baskiotis N, Evgeniou T, et al. Link discovery using graph feature tracking. In: Proceedings of the 24th Annual Conference on Neural Information Processing Systems, Vancouver, 2010. 1966-1974. Google Scholar

[90] Jahanbakhsh K, King V, Shoja G C. Predicting missing contacts in mobile social networks. Pervasive Mob Comput, 2012, 8: 698-716. Google Scholar

[91] Gao S, Denoyer L, Gallinari P, et al. Probabilistic latent tensor factorization model for link pattern prediction in multi-relational networks. J China Univ Post Telecommun, 2012, 19: 172-181. Google Scholar

[92] Gao S, Denoyer L, Gallinari P. Link pattern prediction with tensor decomposition in multi-relational networks. In: Proceedings of 2011 IEEE Symposium on Computational Intelligence and Data Mining, Paris, 2011. 333-340. Google Scholar

[93] Sun Y, Barber R, Gupta M, et al. Co-author relationship prediction in heterogeneous bibliographic networks. In: Proceeding of the 2011 IEEE/ACM International Conference on Advanced in Social Networks Analysis and Mining (ASONAM'11), Kaohsiung, 2011. 121-128. Google Scholar

[94] Sun Y, Han J, Aggarwal C C, et al. When will it happen? relationship prediction in heterogeneous information networks. In: Proceedings of the 5th ACM International Conference on Web Search and Data Mining (WSDM'12), Seattle, 2012. 663-672. Google Scholar

[95] Yang Y, Chawla N, Sun Y, et al. Predicting links in multi-relational and heterogeneous networks. In: Proceedings of The 12th IEEE International Conference on Data Mining, Brussels, 2012. 755-764. Google Scholar

[96] Davis D, Lichtenwalter R, Chawla N V. Supervised methods for multi-relational link prediction. Soc Netw Anal Min, 2013, 3: 127-141. Google Scholar

[97] Ströele V, Zimbrão G, Souza J M. Group and link analysis of multi-relational scientific social networks. J Syst Softw, 2013, 86: 1819-1830. Google Scholar

[98] Rossetti G, Berlingerio M, Giannotti F. Scalable link prediction on multidimensional networks. In: Proceedings of 11th IEEE International Conference on Data Mining Workshops, Vancouver, 2011. 979-986. Google Scholar

[99] Wang D, Pedreschi D, Song C, et al. Human mobility, social ties, and link prediction. In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Diego, 2011. 1100-1108. Google Scholar

[100] Munasinghe L, Ichise R. Link prediction in social networks using information flow via active links. IEICE Trans Inf Syst, 2012, E96-D: 1495-1502. Google Scholar

[101] Chen H H, Gou L, Zhang X L, et al. Predicting recent links in FOAF networks. In: Proceedings of the 5th international conference on Social Computing, Behavioral-Cultural Modeling and Prediction, College Park, 2012. 156-163. Google Scholar

[102] Chen H H, Miller D J, Giles C L. The predictive value of young and old links in a social network. In: Proceedings of the ACM SIGMOD Workshop on Databases and Social Networks, New York, 2013. 43-48. Google Scholar

[103] Kunegis J, De Luca E W, Albayrak S. The link prediction problem in bipartite networks. In: Proceedings of the 13th International Conference on Information Processing and Management of Uncertainty, Dortmund, 2010. 380-389. Google Scholar

[104] Xia S, Dai B T, Lim E P, et al. Link prediction for bipartite social networks: the Role of Structural Holes. In: Proceedings of 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), Istanbul, 2012. 153-157. Google Scholar

[105] Chang Y J, Kao H Y. Link prediction in a bipartite network using Wikipedia revision information. In: Proceedings of 2012 Conference on Technologies and Applications of Artificial Intelligence, Tainan, 2012. 50-55. Google Scholar

[106] Allali O, Magnien C, Latapy M. Link prediction in bipartite graphs using internal links and weighted projection. In: INFOCOM Workshop on Network Science for Computer Communications, Shanghai, 2011. 936-941. Google Scholar

[107] Liu J, Deng G. Link prediction in a user-object network based on time-weighted resource allocation. Physica A, 2009, 388: 3643-3650. Google Scholar

[108] Kwak H, Chun H, Moon S. Fragile online relationship: a first look at unfollow dynamics in twitter. In: Proceedings of the International Conference on Human Factors in Computing Systems (CHI'11), Vancouver, 2011. 435-463. Google Scholar

[109] Kwak H, Moon S, Lee W. More of a receiver than a giver: why do people unfollow in Twitter? In: Proceedings of the 6th International Conference on Weblogs and Social Media, Dublin, 2012. 499-504. Google Scholar

[110] Xu B, Huang Y, Kwak H, et al. Structures of broken ties: exploring unfollow behavior on Twitter. In: Proceedings of the 16th ACM Conference on Computer Supported Cooperative Work and Social Computing (CSCW'13), San Antonio, 2013. 871-876. Google Scholar

[111] Kivran-Swaine F, Govindan P, Naaman M. The impact of network structure on breaking ties in online social networks: unfollowing on twitter. In: Proceedings of the International Conference on Human Factors in Computing Systems (CHI'11), Vancouver, 2011. 1101-1104. Google Scholar

[112] Quercia D, Bodaghi M, Crowcroft J. Loosing “friends”on Facebook. In: Proceedings of Web Science, Evanston, 2012. 251-254. Google Scholar

[113] Sarkar P, Chakrabarti D, Jordan M. Nonparametric link prediction in dynamic networks. In: Proceedings of the 29th International Conference on Machine Learning (ICML'12), Edinburgh, 2012. Google Scholar

[114] Song H H, Cho T W, Dave V, et al. Scalable proximity estimation and link prediction in online social networks. In: Proceedings of the 9th ACM SIGCOMM Conference on Internet Measurement, Chicago, 2009. 322-335. Google Scholar

[115] Li X, Du N, Li H, et al. A deep learning approach to link prediction in dynamic networks. In: Proceedings of the 2014 SIAM International Conference on Data Mining, Philadelphia, 2014. 289-297. Google Scholar

[116] Armentano M G, Godoy D, Amandi A A. Followee recommendation based on text analysis of micro-blogging activity. Inform Syst, 2013, 38: 1116-1127. Google Scholar

[117] Sadilek A, Kautz H, Bigham J P. Finding your friends and following them to where you are. In: Proceedings of the 5th ACM International Conference on Web Search and Data Mining, Seattle, 2012. 723-732. Google Scholar

[118] Huang Z, Li X, Chen H. Link prediction approach to collaborative filtering. In: Proceedings of the 5th ACM/IEEE-CS Joint Conference on Digital Libraries (JCDL'05), Denver, 2005. 141-142. Google Scholar

[119] Rowe M, Stankovic M, Alani H. Who will follow whom? exploiting semantics for link prediction in attention information networks. In: Proceedings of the 11th International Semantic Web Conference (ISWC'12), Boston, 2012. 476-491. Google Scholar

[120] Esslimani I, Brun A, Boyer A. Densifying a behavioral recommender system by social networks link prediction methods. Soc Netw Anal Min, 2011, 1: 159-172. Google Scholar

[121] Hopcroft J, Lou T, Tang J. Who will follow you back? reciprocal relationship prediction. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management (CIKM11), Glasgow, 2011. 1137-1146. Google Scholar

[122] Zhang H, Dantu R. Predicting social ties in mobile phone networks. In: Proceedings of 2010 IEEE International Conference on Intelligence and Security Informatics, Vancouver, 2010. 25-30. Google Scholar

[123] Gilbert E, Karahalios K. Predicting tie strength with social media. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, Boston, 2009. 211-220. Google Scholar

[124] Xiang R, Neville J, Rogati M. Modeling relationship strength in online social networks. In: Proceedings of the 19th International Conference on World Wide Web (WWW'10), Raleigh, 2010. 981-990. Google Scholar

[125] Tang W, Zhuang H, Tang J. Learning to infer social ties in large networks. In: Proceedings of Machine Learning and Knowledge Discovery in Databases European Conference (ECML/PKDD), Athens, 2011. 381-397. Google Scholar

[126] Zhuang H, Tang J, Tang W, et al. Actively learning to infer social ties. Data Min Knowl Discov, 2012, 25: 270-297. Google Scholar

[127] Tang J, Lou T, Kleinberg J. Inferring social ties across heterogeneous networks. In: Proceedings of the 5th ACM International Conference on Web Search and Data Mining (WSDM'12), Seattle, 2012. 743-752. Google Scholar

[128] Liu W, Lü L. Link prediction based on local random walk. Europhys Lett, 2010, 89: 58007. Google Scholar

[129] Liu Z, Zhang Q, Lü L, et al. Link prediction in complex networks: a local näve Bayes model. Europhys Lett, 2011, 96: 48007. Google Scholar

[130] Yin Z, Gupta M, Weninger T, et al. LINKREC: a unified framework for link recommendation with user attributes and graph structure. In: Proceedings of the 19th International Conference on World Wide Web (WWW'10), Raleigh, 2010. 1211-1212. Google Scholar

[131] Sachan M, Ichise R. Using semantic information to improve link prediction results in network datasets. Int J Comput Theory Eng, 2011, 3: 71-76. Google Scholar

Copyright 2020 Science China Press Co., Ltd. 《中国科学》杂志社有限责任公司 版权所有

京ICP备17057255号       京公网安备11010102003388号