Improving the Robustness of Graphs through Reinforcement Learning and Graph Neural Networks

Graphs can be used to represent and reason about real world systems and a variety of metrics have been devised to quantify their global characteristics. An important property is robustness to failures and attacks, which is relevant for the infrastructure and communication networks that power modern society. Prior work on making topological modifications to a graph, e.g., adding edges, in order to increase robustness is typically based on local and spectral properties or a shallow search since robustness is expensive to compute directly. However, such strategies are necessarily suboptimal. In this work, we present RNet-DQN, an approach for constructing networks that uses Reinforcement Learning to address improving the robustness of graphs to random and targeted removals of nodes. In particular, the approach relies on changes in the estimated robustness as a reward signal and Graph Neural Networks for representing states. Experiments on synthetic and real-world graphs show that this approach can deliver performance superior to existing methods while being much cheaper to evaluate and generalizing to out-of-sample graphs, as well as to larger out-of-distribution graphs in some cases. The approach is readily applicable to optimizing other global structural properties of graphs.