Attacking Similarity-Based Link Prediction in Social Networks

Link prediction is one of the fundamental problems in computational social science. A particularly common means to predict existence of unobserved links is via structural similarity metrics, such as the number of common neighbors; node pairs with higher similarity are thus deemed more likely to be linked. However, a number of applications of link prediction, such as predicting links in gang or terrorist networks, are adversarial, with another party incentivized to minimize its effectiveness by manipulating observed information about the network. We offer a comprehensive algorithmic investigation of the problem of attacking similarity-based link prediction through link deletion, focusing on two broad classes of such approaches, one which uses only local information about target links, and another which uses global network information. While we show several variations of the general problem to be NP-Hard for both local and global metrics, we exhibit a number of well-motivated special cases which are tractable. Additionally, we provide principled and empirically effective algorithms for the intractable cases, in some cases proving worst-case approximation guarantees.

[1]  François Fouss,et al.  Random-Walk Computation of Similarities between Nodes of a Graph with Application to Collaborative Recommendation , 2007, IEEE Transactions on Knowledge and Data Engineering.

[2]  Zheng Zhengzhong.  Link prediction using semi-supervised learning , 2012 .

[3]  Peng Wang,et al.  Link prediction in social networks: the state-of-the-art , 2014, Science China Information Sciences.

[4]  Tomasz P. Michalak,et al.  On the Construction of Covert Networks , 2017, AAMAS.

[5]  Dit-Yan Yeung,et al.  Relational Deep Learning: A Deep Latent Variable Model for Link Prediction , 2017, AAAI.

[6]  Stephen P. Boyd,et al.  Minimizing Effective Resistance of a Graph , 2008, SIAM Rev..

[7]  David Liben-Nowell,et al.  The link-prediction problem for social networks , 2007 .

[8]  Talal Rahwan,et al.  Attack Tolerance of Link Prediction Algorithms: How to Hide Your Relations in a Social Network , 2018, ArXiv.

[9]  Jon M. Kleinberg,et al.  The link-prediction problem for social networks , 2007, J. Assoc. Inf. Sci. Technol..

[10]  Peter G. Doyle,et al.  Random walks and electric networks , 1987, math/0001057.

[11]  Linyuan Lü,et al.  Predicting missing links via local information , 2009, 0901.0553.

[12]  Linyuan Lu,et al.  Link Prediction in Complex Networks: A Survey , 2010, ArXiv.

[13]  Charles Elkan,et al.  Link Prediction via Matrix Factorization , 2011, ECML/PKDD.

[14]  Ulrike von Luxburg,et al.  Hitting and commute times in large random neighborhood graphs , 2014, J. Mach. Learn. Res..

[15]  Mohammad Al Hasan,et al.  Link prediction using supervised learning , 2006 .

[16]  D. Hochbaum,et al.  Analysis of the greedy approach in problems of maximum k‐coverage , 1998 .

[17]  Yixin Chen,et al.  Link Prediction Based on Graph Neural Networks , 2018, NeurIPS.

[18]  Linyuan Lü,et al.  Similarity index based on local paths for link prediction of complex networks. , 2009, Physical review. E, Statistical, nonlinear, and soft matter physics.

[19]  Leo Katz,et al.  A new status index derived from sociometric analysis , 1953 .

[20]  M. L. Fisher,et al.  An analysis of approximations for maximizing submodular set functions—I , 1978, Math. Program..

[21]  Yang-Yu Liu,et al.  Link Prediction through Deep Learning , 2018, bioRxiv.

[22]  Jon Kleinberg,et al.  The link prediction problem for social networks , 2003, CIKM '03.

[23]  Zan Huang,et al.  The Time-Series Link Prediction Problem with Applications in Communication Surveillance , 2009, INFORMS J. Comput..

[24]  Talal Rahwan,et al.  Strategic Social Network Analysis , 2017, AAAI.

[25]  Jianping Li,et al.  Link Prediction via Convex Nonnegative Matrix Factorization on Multiscale Blocks , 2014, J. Appl. Math..

[26]  Futian Wang,et al.  Measuring the robustness of link prediction algorithms under noisy environment , 2016, Scientific Reports.

[27]  Talal Rahwan,et al.  Hiding individuals and communities in a social network , 2016, Nature Human Behaviour.

[28]  M. Newman,et al.  Vertex similarity in networks. , 2005, Physical review. E, Statistical, nonlinear, and soft matter physics.

[29]  Qi Xuan,et al.  Target Defense Against Link-Prediction-Based Attacks via Evolutionary Perturbations , 2018, IEEE Transactions on Knowledge and Data Engineering.

[30]  Jure Leskovec,et al.  {SNAP Datasets}: {Stanford} Large Network Dataset Collection , 2014 .

[31]  Reda Alhajj,et al.  Link prediction and classification in social networks and its application in healthcare and systems biology , 2012, Network Modeling Analysis in Health Informatics and Bioinformatics.