EdgeNets：边变图神经网络
EdgeNets:Edge Varying Graph Neural Networks
摘要：
由于神经网络在结构化欧几里德域中的出色表现，近年来人们对开发用于图和图上支持的数据的神经网络的兴趣激增。该图在神经网络的每一层都被用作参数化，以减少参数数量和计算复杂性来捕获节点级别的细节。根据这一基本原理，本文提出了一个通用框架，通过EdgeNet的概念统一了最先进的图神经网络（GNNs）。EdgeNet是一种GNN架构，允许不同的节点使用不同的参数来衡量不同邻居的信息。通过将此策略外推到相邻节点之间的更多迭代，EdgeNet学习边缘和相邻节点相关的权重以捕获局部细节。这是节点可以执行的一般线性和局部操作，并且在一个公式下包含所有现有的图卷积神经网络（GCNN）以及图注意力网络（GAT）。在使用通用语言编写不同的GNN架构时，EdgeNets强调了特定架构的优势和局限性，同时提供了在不影响本地实现的情况下提高其能力的指导方针。一个有趣的结论是GCNN和GAT的统一-到目前为止一直被认为是独立的方法。特别是，我们证明了GAT是从特征中学习的图上的GCNN。这种特殊化为发展其他注意力机制以提高歧视力打开了大门。
英文摘要：
Driven by the outstanding performance of neural networks in the structured Euclidean domain, recent years have seen a surge of interest in developing neural networks for graphs and data supported on graphs. The graph is leveraged at each layer of the neural network as a parameterization to capture detail at the node level with a reduced number of parameters and computational complexity. Following this rationale, this paper puts forth a general framework that unifies state-of-the-art graph neural networks (GNNs) through the concept of EdgeNet. An EdgeNet is a GNN architecture that allows different nodes to use different parameters to weigh the information of different neighbors. By extrapolating this strategy to more iterations between neighboring nodes, the EdgeNet learns edge- and neighbor-dependent weights to capture local detail. This is a general linear and local operation that a node can perform and encompasses under one formulation all existing graph convolutional neural networks (GCNNs) as well as graph attention networks (GATs). In writing different GNN architectures with a common language, EdgeNets highlight specific architecture advantages and limitations, while providing guidelines to improve their capacity without compromising their local implementation. An interesting conclusion is the unification of GCNNs and GATs -- approaches that have been so far perceived as separate. In particular, we show that GATs are GCNNs on a graph that is learned from the features. This particularization opens the doors to develop alternative attention mechanisms for improving discriminatory power.
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。
