The Economics of Community Detection and Hiding

We present a model of surveillance based on the detection of community structure in social networks. We examine the extent of network topology information an adversary is required to gather in order to obtain high quality intelligence about community membership. We show that selective surveillance strategies can improve the adversary’s resource efficiency. However, the use of counter-surveillance defense strategies can significantly reduce the adversary’s capability. We analyze two adversary models drawn from contemporary computer security literature, and explore the dynamics of community detection and hiding in these settings. Our results show that in the absence of counter-surveillance moves, placing a mere 8% of the network under surveillance can uncover the community membership of as much as 50% of the network. Uncovering all community information with targeted selection requires half the surveillance budget where parties use anonymous channels to communicate. Finally, the most determined covert community can escape detection by adopting decentralized counter-surveillance techniques even while facing an adversary with full topology knowledge by investing in a small counter-surveillance budget, a rebel group can induce a steep increase in the false negative ratio.