article

Integrated anchor and social link predictions across multiple social networks

Authors:

Philip S. YuAuthors Info & Claims

Knowledge and Information Systems, Volume 60, Issue 1

Pages 303 - 326

https://doi.org/10.1007/s10115-018-1210-1

Published: 01 July 2019 Publication History

Abstract

In recent years, various online social networks offering specific services have gained great popularity and success. To enjoy more online social services, some users can be involved in multiple social networks simultaneously. A challenging problem in social network studies is to identify the common users across networks to gain better understanding of user behavior. This is referred to as the anchor link prediction problem. Meanwhile, across these partially aligned social networks, users can be connected by different kinds of links, e.g., social links among users in one single network and anchor links between accounts of the shared users in different networks. Many different link prediction methods have been proposed so far to predict each type of links separately. In this paper, we want to predict the formation of social links among users in the target network as well as anchor links aligning the target network with other external social networks. The problem is formally defined as the "collective link identification" problem. Predicting the formation of links in social networks with traditional link prediction methods, e.g., classification-based methods, can be very challenging. The reason is that, from the network, we can only obtain the formed links (i.e., positive links) but no information about the links that will never be formed (i.e., negative links). To solve the collective link identification problem, a unified link prediction framework, collective link fusion (CLF) is proposed in this paper, which consists of two phases: step (1) collective link prediction of anchor and social links with positive and unlabeled learning techniques, and step (2) propagation of predicted links across the partially aligned "probabilistic networks" with collective random walk. Extensive experiments conducted on two real-world partially aligned networks demonstrate that CLF can perform very well in predicting social and anchor links concurrently.

References

[1]

Adamic L, Adar E (2001) Friends and neighbors on the web. Soc Netw 25:211---230

[2]

Backstrom L, Leskovec J (2011) Supervised random walks: predicting and recommending links in social networks. In: WSDM

Digital Library

[3]

Chang C-C, Lin C-J (2001) LIBSVM: a library for support vector machines. Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm

Digital Library

[4]

Elkan C, Noto K (2008) Learning classifiers from only positive and unlabeled data. In: KDD

Digital Library

[5]

Fouss F, Pirotte A, Renders J, Saerens M (2007) Random-walk computation of similarities between nodes of a graph with application to collaborative recommendation. TKDE 19:355---369

Digital Library

[6]

Fujiwara Y, Nakatsuji M, Onizuka M, Kitsuregawa M (2012) Fast and exact top-k search for random walk with restart. VLDB 55:442---453

Digital Library

[7]

Getoor L, Diehl CP (2005) Link mining: a survey. SIGKDD Explor Newslett 7:3---12

Digital Library

[8]

Hasan M, Chaoji V, Salem S, Zaki M (2006) Link prediction using supervised learning. In: SDM

[9]

Hasan M, Zaki MJ (2011) A survey of link prediction in social networks. In: Aggarwal CC (ed) Social network data analytics. Springer, New York

[10]

Hsieh C-J, Natarajan N, Dhillon IS (2015) PU learning for matrix completion. In: ICML, pp 2445---2453

Digital Library

[11]

Hwang T, Kuang R (2010) A heterogeneous label propagation algorithm for disease gene discovery. In: SDM

[12]

Iofciu T, Fankhauser P, Abel F, Bischoff K (2011) Identifying users across social tagging systems. In: ICWSM

[13]

Jin S, Zhang J, Yu P, Yang S, Li A (2014) Synergistic partitioning in multiple large scale social networks. In: IEEE BigData

[14]

Kong X, Zhang J, Yu P (2013) Inferring anchor links across multiple heterogeneous social networks. In: CIKM

Digital Library

[15]

Konstas I, Stathopoulos V, Jose JM (2009) On social networks and collaborative recommendation. In: SIGIR

Digital Library

[16]

Lü L, Zhou T (2011) Link prediction in complex networks: a survey. Phys A Stat Mech Its Appl 390:1150---1170

[17]

Leskovec J, Huttenlocher D, Kleinberg J (2010) Predicting positive and negative links in online social networks. In: WWW

Digital Library

[18]

Liben-Nowell D, Kleinberg J (2003) The link prediction problem for social networks. In: CIKM

Digital Library

[19]

Liu B, Dai Y, Li X, Lee W, Yu P (2003) Building text classifiers using positive and unlabeled examples. In: ICDM

Digital Library

[20]

Liu J, Zhang F, Song X, Song Y, Lin C, Hon H (2013) What's in a name? An unsupervised approach to link users across communities. In: WSDM

Digital Library

[21]

Lü L, Zhou T (2011) Link prediction in complex networks: a survey. Phys A Stat Mech Its Appl 390(6):1150---1170

[22]

Namata G, Kok S, Getoor L (2011) Collective graph identification. In: KDD

Digital Library

[23]

Perkins D, Salomon G (1992) Transfer of learning Pergamon Press, Oxford, England

[24]

Sahraeian S, Yoon B (2013) Smetana: accurate and scalable algorithm for probabilistic alignment of large-scale biological networks. PLoS ONE 8:e67995

[25]

Song D, Meyer D (2014) A model of consistent node types in signed directed social networks. In: ASONAM '14 Proceedings of the 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, IEEE Press, Piscataway, NJ, USA, pp 72---80

Digital Library

[26]

Tong H, Faloutsos C, Pan J (2006) Fast random walk with restart and its applications. In: ICDM

Digital Library

[27]

Wilcox K, Stephen AT (2012) Are close friends the enemy? Online social networks, self-esteem, and self-control. J Consum Res 40:90---103

[28]

Xi W, Zhang B, Chen Z, Lu Y, Yan S, Ma W, Fox E (2004) Link fusion: a unified link analysis framework for multi-type interrelated data objects. In: WWW

Digital Library

[29]

Xiang R, Neville J, Rogati M (2010) Modeling relationship strength in online social networks. In: WWW

Digital Library

[30]

Yao Y, Tong H, Yan X, Xu F, Lu J (2013) Matri: a multi-aspect and transitive trust inference model. In: WWW

Digital Library

[31]

Ye J, Cheng H, Zhu Z, Chen M (2013) Predicting positive and negative links in signed social networks by transfer learning. In: WWW

Digital Library

[32]

Zafarani R, Liu H (2009) Connecting corresponding identities across communities. In: ICWSM

[33]

Zhan Q, Wang S, Zhang J, Yu P, Xie J (2015) Influence maximization across partially aligned heterogenous social networks. In: PAKDD

[34]

Zhang J, Kong X, Yu P (2013) Predicting social links for new users across aligned heterogeneous social networks. In: ICDM

[35]

Zhang J, Kong X, Yu P (2014) Transferring heterogeneous links across location-based social networks. In: WSDM

Digital Library

[36]

Zhang J, Shao W, Wang S, Kong X, Yu P (2015) Pna: Partial network alignment with generic stable matching. In: IEEE IRI

Digital Library

[37]

Zhang J, Yu P (2015) Community detection for emerging networks. In: SDM

[38]

Zhang J, Yu P (2015) Mcd: Mutual clustering across multiple heterogeneous networks. In: IEEE BigData Congress

Digital Library

[39]

Zhang J, Yu P, Zhou Z (2014) Meta-path based multi-network collective link prediction. In: KDD

Digital Library

[40]

Zhao Y, Kong X, Yu P (2011) Positive and unlabeled learning for graph classification. In: ICDM

Digital Library

Cited By

Ren GDing XXu XZhang HWooldridge MDy JNatarajan S(2024)Link prediction in multilayer networks via cross-network embeddingProceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence and Thirty-Sixth Conference on Innovative Applications of Artificial Intelligence and Fourteenth Symposium on Educational Advances in Artificial Intelligence10.1609/aaai.v38i8.28742(8939-8947)Online publication date: 20-Feb-2024
https://dl.acm.org/doi/10.1609/aaai.v38i8.28742
Wang HYang WMan DWang WLv J(2023)Anchor Link Prediction for Privacy Leakage via De-Anonymization in Multiple Social NetworksIEEE Transactions on Dependable and Secure Computing10.1109/TDSC.2023.324200920:6(5197-5213)Online publication date: 1-Nov-2023
https://dl.acm.org/doi/10.1109/TDSC.2023.3242009
Wang HYang WMan DWang WLv JEr M(2023)A novel cross-network node pair embedding methodology for anchor link predictionWorld Wide Web10.1007/s11280-023-01154-226:5(2495-2520)Online publication date: 1-Sep-2023
https://dl.acm.org/doi/10.1007/s11280-023-01154-2
Show More Cited By

Integrated anchor and social link predictions across multiple social networks
1. Information systems
  1. Information systems applications

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cover image Knowledge and Information Systems

Knowledge and Information Systems Volume 60, Issue 1

July 2019

581 pages

ISSN:0219-1377

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Copyright © Copyright © 2019 Springer-Verlag London Ltd., part of Springer Nature.

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Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 01 July 2019

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Cited By

Ren GDing XXu XZhang HWooldridge MDy JNatarajan S(2024)Link prediction in multilayer networks via cross-network embeddingProceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence and Thirty-Sixth Conference on Innovative Applications of Artificial Intelligence and Fourteenth Symposium on Educational Advances in Artificial Intelligence10.1609/aaai.v38i8.28742(8939-8947)Online publication date: 20-Feb-2024
https://dl.acm.org/doi/10.1609/aaai.v38i8.28742
Wang HYang WMan DWang WLv J(2023)Anchor Link Prediction for Privacy Leakage via De-Anonymization in Multiple Social NetworksIEEE Transactions on Dependable and Secure Computing10.1109/TDSC.2023.324200920:6(5197-5213)Online publication date: 1-Nov-2023
https://dl.acm.org/doi/10.1109/TDSC.2023.3242009
Wang HYang WMan DWang WLv JEr M(2023)A novel cross-network node pair embedding methodology for anchor link predictionWorld Wide Web10.1007/s11280-023-01154-226:5(2495-2520)Online publication date: 1-Sep-2023
https://dl.acm.org/doi/10.1007/s11280-023-01154-2
Wang HYang WWang WMan DLv J(2022)A Novel Cross-Network Embedding for Anchor Link Prediction with Social Adversarial AttacksACM Transactions on Privacy and Security10.1145/354868526:1(1-32)Online publication date: 7-Nov-2022
https://dl.acm.org/doi/10.1145/3548685

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