  Improving the resilience of a network is a fundamental problem in network
science, which protects the underlying system from natural disasters and
malicious attacks. This is traditionally achieved via successive
degree-preserving edge rewiring operations, with the major limitation of being
transductive. Inductively solving graph-related tasks with sequential actions
is accomplished by adopting graph neural networks (GNNs) coupled with
reinforcement learning under the scenario with rich graph features. However,
such frameworks cannot be directly applied to resilience tasks where only pure
topological structure is available. In this case, GNNs can barely learn useful
information, resulting in prohibitive difficulty in making actions for
successively rewiring edges under a reinforcement learning context. In this
paper, we study in depth the reasons why typical GNNs cause such failure. Based
on this investigation, we propose ResiNet, the first end-to-end trainable
inductive framework to discover resilient network topologies while balancing
network utility. To this end, we reformulate resilience optimization as an MDP
equipped with edge rewiring action space, and propose a pure topology-oriented
variant of GNN called filtration enhanced graph neural network (FireGNN), which
can learn from graphs without rich features. Extensive experiments demonstrate
that ResiNet achieves a near-optimal resilience gain on various graphs while
balancing the utility, and outperforms existing approaches by a large margin.
