Hiding individuals and communities in a social network

By Marcin Waniek, Tomasz P. Michalak, Talal Rahwan, Michael J. Wooldridge. Nature Human Behaviour, 2016

296 citations recorded by the source corpus.

The Internet and social media have fuelled enormous interest in social network analysis. New tools continue to be developed and used to analyse our personal connections, with particular emphasis on detecting communities or identifying key individuals in a social network. This raises privacy concerns that are likely to exacerbate in the future. With this in mind, we ask the question ‘Can individuals or groups actively manage their connections to evade social network analysis tools?’ By addressing this question, the general public may better protect their privacy, oppressed activist groups may better conceal their existence and security agencies may better understand how terrorists escape detection. We first study how an individual can evade ‘node centrality’ analysis while minimizing the negative impact that this may have on his or her influence. We prove that an optimal solution to this problem is difficult to compute. Despite this hardness, we demonstrate how even a simple heuristic, whereby attention is restricted to the individual’s immediate neighbourhood, can be surprisingly effective in practice; for example, it could easily disguise Mohamed Atta’s leading position within the World Trade Center terrorist network. We also study how a community can increase the likelihood of being overlooked by community-detection algorithms. We propose a measure of concealment—expressing how well a community is hidden—and use it to demonstrate the effectiveness of a simple heuristic, whereby members of the community either ‘unfriend’ certain other members or ‘befriend’ some non-members in a coordinated effort to camouflage their community.Waniek and colleagues show that individuals and communities can disguise themselves from detection online by standard social network analysis tools through simple changes to their social network connections.

Subfields: Computer Networks and Communications, Computer Science Applications, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/6729b698fc28d191f3006690d973974a13c2f501

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Citations in the corpus

Adversarial Attack and Defense on Graph Data: A Survey (2018, methodology): “Attack Model GNN [8], [12], [17], [21], [23], [28], [73], [92], [93], [103], [106], [125], [145], [147] [20], [36], [42], [49], [67], [96], [97], [104], [120], [122], [134], [141], [146] Non-GNN [2], [18], [19], [24], [27], [33], [43], [50], [108], [109], [128], [129], [136], [143] [26], [34], [39] ”