论文标题
卷积神经网络进入图形空间
A Convolutional Neural Network into graph space
论文作者
论文摘要
几十年来，卷积神经网络（CNN）在分类环境中的现有方法的表现优于现有的最新方法。但是，以它们的形式化，CNN必定会在欧几里得空间上运行。实际上，卷积是在欧几里得空间上定义的信号操作。这限制了深度学习的主要用途，用于欧几里得定义的数据，例如声音或图像。然而，许多计算机应用程序字段（其中包括网络分析，计算社会科学，化学信息或计算机图形）诱导非欧成功定义的数据，例如图形，网络或流形。在本文中，我们提出了一种直接定义为图形空间的新的卷积神经网络体系结构。卷积和池操作员在图形域中定义。我们在背景环境中显示其可用性。实验结果表明，我们的模型性能在简单任务上处于最新水平。相对于其他欧几里得和非欧盟卷积体系结构，它显示了图形域变化和改进的鲁棒性。
Convolutional neural networks (CNNs), in a few decades, have outperformed the existing state of the art methods in classification context. However, in the way they were formalised, CNNs are bound to operate on euclidean spaces. Indeed, convolution is a signal operation that are defined on euclidean spaces. This has restricted deep learning main use to euclidean-defined data such as sound or image. And yet, numerous computer application fields (among which network analysis, computational social science, chemo-informatics or computer graphics) induce non-euclideanly defined data such as graphs, networks or manifolds. In this paper we propose a new convolution neural network architecture, defined directly into graph space. Convolution and pooling operators are defined in graph domain. We show its usability in a back-propagation context. Experimental results show that our model performance is at state of the art level on simple tasks. It shows robustness with respect to graph domain changes and improvement with respect to other euclidean and non-euclidean convolutional architectures.
