论文标题
用于图表中学习的异步神经网络
Asynchronous Neural Networks for Learning in Graphs
论文作者
论文摘要
本文研究了异步消息传递（AMP），这是一种用于将基于神经网络的学习应用于图的新范式。现有的图形神经网络使用同步分布式计算模型，并在每个回合中汇总邻居，这会导致诸如过度平衡和限制其表现力之类的问题。另一方面，AMP基于异步模型，在该模型中，节点对邻居的消息单独反应。我们证明（i）AMP可以模拟同步GNN，并且（ii）AMP理论上可以区分任何一对图。我们通过实验验证AMP的表现力。此外，我们表明AMP可能更适合在图形中大距离传播消息，并且在几个图形分类基准上表现良好。
This paper studies asynchronous message passing (AMP), a new paradigm for applying neural network based learning to graphs. Existing graph neural networks use the synchronous distributed computing model and aggregate their neighbors in each round, which causes problems such as oversmoothing and limits their expressiveness. On the other hand, AMP is based on the asynchronous model, where nodes react to messages of their neighbors individually. We prove that (i) AMP can simulate synchronous GNNs and that (ii) AMP can theoretically distinguish any pair of graphs. We experimentally validate AMP's expressiveness. Further, we show that AMP might be better suited to propagate messages over large distances in graphs and performs well on several graph classification benchmarks.
