论文标题
卷积学习
Convolutional Learning on Multigraphs
论文作者
论文摘要
图卷积学习导致了各个领域的许多令人兴奋的发现。但是，在某些应用中，传统图不足以捕获数据的结构和复杂性。在这种情况下，多编码自然出现是可以嵌入复杂动力学的离散结构。在本文中，我们开发了有关多编码的卷积信息处理，并引入了卷积多编码神经网络（MGNN）。为了捕获多个跨每个边缘类中的信息扩散的复杂动力学，我们正式化了一个卷积信号处理模型，从而定义了信号，滤波和频率表示的概念。利用该模型，我们开发了多个学习架构，包括一个采样程序来降低计算复杂性。引入的体系结构用于最佳无线资源分配和仇恨语音本地化任务，从而比传统的图形神经网络的性能提高了。
Graph convolutional learning has led to many exciting discoveries in diverse areas. However, in some applications, traditional graphs are insufficient to capture the structure and intricacies of the data. In such scenarios, multigraphs arise naturally as discrete structures in which complex dynamics can be embedded. In this paper, we develop convolutional information processing on multigraphs and introduce convolutional multigraph neural networks (MGNNs). To capture the complex dynamics of information diffusion within and across each of the multigraph's classes of edges, we formalize a convolutional signal processing model, defining the notions of signals, filtering, and frequency representations on multigraphs. Leveraging this model, we develop a multigraph learning architecture, including a sampling procedure to reduce computational complexity. The introduced architecture is applied towards optimal wireless resource allocation and a hate speech localization task, offering improved performance over traditional graph neural networks.
