论文标题
风险网络：不可靠资源网络中的神经风险评估
RiskNet: Neural Risk Assessment in Networks of Unreliable Resources
论文作者
论文摘要
我们提出了一个基于图形神经网络（GNN）的方法，以预测通信网络中断电引起的惩罚的分布，在该方法中，连接受工作和备份路径之间共享的资源保护。基于GNN的算法仅通过Barabási-Albert模型生成的随机图进行训练。即使获得的测试结果表明，我们可以精确地对各种现有拓扑的惩罚进行建模。 GNN消除了为正在研究的网络拓扑模拟复杂的中断场景的需求。实际上，整个设计操作在现代硬件上受到4ms的限制。这样，我们可以在提高速度的情况下获得高达12,000倍以上。
We propose a graph neural network (GNN)-based method to predict the distribution of penalties induced by outages in communication networks, where connections are protected by resources shared between working and backup paths. The GNN-based algorithm is trained only with random graphs generated with the Barabási-Albert model. Even though, the obtained test results show that we can precisely model the penalties in a wide range of various existing topologies. GNNs eliminate the need to simulate complex outage scenarios for the network topologies under study. In practice, the whole design operation is limited by 4ms on modern hardware. This way, we can gain as much as over 12,000 times in the speed improvement.
