Hiding in Multilayer Networks


  • Marcin Waniek New York University Abu Dhabi
  • Tomasz Michalak University of Warsaw
  • Talal Rahwan New York University Abu Dhabi




Multilayer networks allow for modeling complex relationships, where individuals are embedded in multiple social networks at the same time. Given the ubiquity of such relationships, these networks have been increasingly gaining attention in the literature. This paper presents the first analysis of the robustness of centrality measures against strategic manipulation in multilayer networks. More specifically, we consider an “evader” who strategically chooses which connections to form in a multilayer network in order to obtain a low centrality-based ranking—thereby reducing the chance of being highlighted as a key figure in the network—while ensuring that she remains connected to a certain group of people. We prove that determining an optimal way to “hide” is NP-complete and hard to approximate for most centrality measures considered in our study. Moreover, we empirically evaluate a number of heuristics that the evader can use. Our results suggest that the centrality measures that are functions of the entire network topology are more robust to such a strategic evader than their counterparts which consider each layer separately.




How to Cite

Waniek, M., Michalak, T., & Rahwan, T. (2020). Hiding in Multilayer Networks. Proceedings of the AAAI Conference on Artificial Intelligence, 34(01), 1021-1028. https://doi.org/10.1609/aaai.v34i01.5451



AAAI Technical Track: Applications