Strategic Social Network Analysis

How can individuals and communities protect their privacy against social network analysis tools? How do criminals or terrorists organizations evade detection by such tools? Under which conditions can these tools be made strategy proof? These fundamental questions have attracted little attention in the literature to date, as most social network analysis tools are built around the assumption that individuals or groups in a network do not act strategically to evade such tools. With this in mind, we outline in this paper a new paradigm for social network analysis, whereby the strategic behaviour of network actors is explicitly modeled. Addressing this research challenge has various implications. For instance, it may allow two individuals to keep their relationship secret or private. It may also allow members of an activist group to conceal their membership, or even conceal the existence of their group from authoritarian regimes. Furthermore, it may assist security agencies and counter terrorism units in understanding the strategies that covert organizations use to escape detection, and give rise to new strategy-proof countermeasures.

[1]  Nicholas R. Jennings,et al.  Efficient Computation of the Shapley Value for Game-Theoretic Network Centrality , 2014, J. Artif. Intell. Res..

[2]  V. Stodden,et al.  Privacy, Big Data, and the Public Good: Frameworks For Engagement , 2014 .

[3]  A. Perliger,et al.  Social Network Analysis in the Study of Terrorism and Political Violence , 2011, PS: Political Science & Politics.

[4]  Ngoc Thanh Nguyen,et al.  Computational collective intelligence. Semantic web, social networks and multiagent systems. Proceedings of the First International Conference on Computational Collective Intelligence,(ICCCI 2009), Wroclaw, Poland, 5-7 October 2009 / Ngoc Thanh Nguyen and Ryszard Kowalczyk (eds.) , 2009 .

[5]  Krishna P. Gummadi,et al.  You are who you know: inferring user profiles in online social networks , 2010, WSDM '10.

[6]  George Danezis,et al.  SybilInfer: Detecting Sybil Nodes using Social Networks , 2009, NDSS.

[7]  Kishan G. Mehrotra,et al.  Game-Theoretic Framework for Community Detection , 2018, Encyclopedia of Social Network Analysis and Mining. 2nd Ed..

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

[9]  Ariel Rubinstein,et al.  Naive Strategies in Competitive Games , 1997 .

[10]  Valdis E. Krebs,et al.  Mapping Networks of Terrorist Cells , 2001 .

[11]  Lise Getoor,et al.  Link mining: a survey , 2005, SKDD.

[12]  Margaret E. Roberts,et al.  How Censorship in China Allows Government Criticism but Silences Collective Expression , 2013, American Political Science Review.

[13]  Günce Keziban Orman,et al.  A Comparison of Community Detection Algorithms on Artificial Networks , 2009, Discovery Science.

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

[15]  G. Owen,et al.  A game theoretic approach to measuring degree of centrality in social networks , 1982 .

[16]  Stefan Richter,et al.  Centrality Indices , 2004, Network Analysis.

[17]  Lise Getoor,et al.  To join or not to join: the illusion of privacy in social networks with mixed public and private user profiles , 2009, WWW '09.

[18]  Jordi Sabater-Mir,et al.  Reputation and social network analysis in multi-agent systems , 2002, AAMAS '02.

[19]  Colin Combe,et al.  Privacy, Big Data, and the Public Good: Frameworks for Engagement , 2015 .

[20]  Zhiwei Steven Wu,et al.  Private algorithms for the protected in social network search , 2016, Proceedings of the National Academy of Sciences.

[21]  R. Aumann,et al.  Epistemic Conditions for Nash Equilibrium , 1995 .

[22]  Jure Leskovec,et al.  Learning to Discover Social Circles in Ego Networks , 2012, NIPS.

[23]  Lior Rokach,et al.  Chapter 1 Links Reconstruction Attack Using Link Prediction Algorithms to Compromise Social Networks Privacy , 2013 .

[24]  Manish Jain,et al.  Efficient solutions for joint activity based security games: fast algorithms, results and a field experiment on a transit system , 2014, Autonomous Agents and Multi-Agent Systems.

[25]  Kwan-Liu Ma,et al.  Visual Reasoning about Social Networks Using Centrality Sensitivity , 2012, IEEE Transactions on Visualization and Computer Graphics.

[26]  Gabrielle Demange,et al.  A Survey of Network Formation Models: Stability and Efficiency , 2005 .

[27]  Éva Tardos,et al.  Maximizing the Spread of Influence through a Social Network , 2015, Theory Comput..

[28]  Michael Luck,et al.  Playing hide-and-seek: an abstract game for cyber security , 2014, ACySE '14.

[29]  Wei Chen,et al.  A game-theoretic framework to identify overlapping communities in social networks , 2010, Data Mining and Knowledge Discovery.

[30]  Tadeusz M. Szuba,et al.  Computational Collective Intelligence , 2001, Lecture Notes in Computer Science.

[31]  Talal Rahwan,et al.  Efficient algorithms for game-theoretic betweenness centrality , 2016, Artif. Intell..

[32]  Chandra Prakash,et al.  SybilInfer: Detecting Sybil Nodes using Social Networks , 2011 .

[33]  Ashish Kumar,et al.  Improving Attribute Inference Attack Using Link Prediction in Online Social Networks , 2016 .

[34]  M. Dufwenberg.  Game theory. , 2011, Wiley interdisciplinary reviews. Cognitive science.

[35]  Mohan S. Kankanhalli,et al.  Mechanism Design for Incentivizing Social Media Contributions , 2011, Social Media Modeling and Computing.