论文标题
图形神经网络：体系结构，稳定性和可传递性
Graph Neural Networks: Architectures, Stability and Transferability
论文作者
论文摘要
图形神经网络（GNN）是图形支持的信号的信息处理架构。它们在这里作为卷积神经网络（CNN）的概括，其中各个层包含图形卷积过滤器的库，而不是经典卷积过滤器的库。否则，GNNS作为CNN运行。过滤器由侧面的非线性组成，并堆叠成层。结果表明，GNN体系结构表现出与图形变形的置换和稳定性表现出的。这些特性有助于解释可以从经验上观察到的GNN的良好性能。还表明，如果图形收敛到限制对象，则gragron，gnns会收敛到相应的限制对象，即graphon神经网络。这种融合证明了GNN在具有不同数量节点的网络之间的可传递性合理。概念是通过GNN在推荐系统，分散的协作控制和无线通信网络中的应用来说明概念的。
Graph Neural Networks (GNNs) are information processing architectures for signals supported on graphs. They are presented here as generalizations of convolutional neural networks (CNNs) in which individual layers contain banks of graph convolutional filters instead of banks of classical convolutional filters. Otherwise, GNNs operate as CNNs. Filters are composed with pointwise nonlinearities and stacked in layers. It is shown that GNN architectures exhibit equivariance to permutation and stability to graph deformations. These properties help explain the good performance of GNNs that can be observed empirically. It is also shown that if graphs converge to a limit object, a graphon, GNNs converge to a corresponding limit object, a graphon neural network. This convergence justifies the transferability of GNNs across networks with different number of nodes. Concepts are illustrated by the application of GNNs to recommendation systems, decentralized collaborative control, and wireless communication networks.
