On Manipulating Weight Predictions in Signed Weighted Networks

Adversarial social network analysis studies how graphs can be rewired or otherwise manipulated to evade social network analysis tools. While there is ample literature on manipulating simple networks, more sophisticated network types are much less understood in this respect. In this paper, we focus on the problem of evading FGA---an edge weight prediction method for signed weighted networks by Kumar et al. 2016. Among others, this method can be used for trust prediction in reputation systems. We study the theoretical underpinnings of FGA and its computational properties in terms of manipulability. Our positive finding is that, unlike many other tools, this measure is not only difficult to manipulate optimally, but also it can be difficult to manipulate in practice.

[1]  Zibin Zheng,et al.  A Survey of Adversarial Learning on Graphs , 2020, ArXiv.

[2]  Kai Zhou,et al.  How to Hide One’s Relationships from Link Prediction Algorithms , 2019, Scientific Reports.

[3]  Qi Xuan,et al.  GA-Based Q-Attack on Community Detection , 2018, IEEE Transactions on Computational Social Systems.

[4]  Valeria Fionda,et al.  Community Deception or: How to Stop Fearing Community Detection Algorithms , 2018, IEEE Transactions on Knowledge and Data Engineering.

[5]  Pierluigi Crescenzi,et al.  Improving the Betweenness Centrality of a Node by Adding Links , 2017, ACM J. Exp. Algorithmics.

[6]  Yuanfeng Cai,et al.  Fraud detections for online businesses: a perspective from blockchain technology , 2016, Financial Innovation.

[7]  Christos Faloutsos,et al.  Edge Weight Prediction in Weighted Signed Networks , 2016, 2016 IEEE 16th International Conference on Data Mining (ICDM).

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

[9]  Pierluigi Crescenzi,et al.  Greedily Improving Our Own Closeness Centrality in a Network , 2016, ACM Trans. Knowl. Discov. Data.

[10]  Charu C. Aggarwal,et al.  A Survey of Signed Network Mining in Social Media , 2015, ACM Comput. Surv..

[11]  Michal Pilipczuk,et al.  Parameterized Algorithms , 2015, Springer International Publishing.

[12]  Dina Mayzlin,et al.  Promotional Reviews: An Empirical Investigation of Online Review Manipulation , 2012 .

[13]  Jure Leskovec,et al.  Signed networks in social media , 2010, CHI.

[14]  Jure Leskovec,et al.  Predicting positive and negative links in online social networks , 2010, WWW '10.

[15]  C. Small.  Functional Equations and How to Solve Them , 2006 .

[16]  E. Friedman,et al.  Reputation systems , 2000, CACM.

[17]  Talal Rahwan,et al.  The Manipulability of Centrality Measures-An Axiomatic Approach , 2020, AAMAS.

[18]  H. W. Turnbull.  A Matrix Form of Taylor's Theorem , 1930 .