边缘重新布线进入神经：在没有丰富功能的情况下提高网络弹性
Edge Rewiring Goes Neural: Boosting Network Resilience without Rich Features
摘要：
提高网络的弹性是网络科学中的一个基本问题，它保护底层系统免受自然灾害和恶意攻击。传统上，这是通过连续的度保持边缘重新布线操作来实现的，其主要限制是换能的。采用图神经网络结合强化学习的方法，在图特征丰富的场景下，实现了具有顺序动作的图相关任务的归纳求解.然而，这样的框架不能直接应用于只有纯拓扑结构可用的弹性任务。在这种情况下，GNN几乎不能学习有用的信息，导致在强化学习上下文中做出连续重新布线边缘的动作的困难。在本文中，我们深入研究了为什么典型的GNN会导致这种失败的原因。基于这项调查，我们提出了ResiNet，这是第一个端到端的可训练归纳框架，可以在平衡网络效用的同时发现弹性网络拓扑。为此，我们将弹性优化重新表述为配备边缘重布线动作空间的MDP，并提出了一种纯拓扑导向的GNN变体，称为过滤增强图神经网络（FireGNN），它可以从没有丰富特征的图中学习。大量的实验表明，ResiNet在平衡效用的同时，在各种图上实现了接近最佳的弹性增益，并且大大优于现有方法。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
