The Graph Neural Network Model
The Graph Neural Network Model
复制标题
DOI：
10.1109/tnn.2008.2005605
复制
发表时间：
2009-01-01
影响因子：
--
通讯作者：
Monfardini, Gabriele
中科院分区：
文献类型：
--
作者：
Scarselli, Franco;Gori, Marco;Monfardini, Gabriele
在科学和工程的几个领域中，数据之间的许多基本关系，例如计算机视觉，分子化学，分子生物学，模式识别和数据挖掘，可以用图来表示。在本文中，我们提出了一种称为图形神经网络（GNN）模型的新神经网络模型，该模型扩展了现有的神经网络方法来处理图形域中表示的数据。该GNN模型可以直接处理大多数实际有用的图形类型，例如无环，环状，有向和无方向性，实现a函数t（g，n）是R-M的一个元素，它映射了图G和一个图形g和一个元素。它的节点n进入了m维欧几里得空间。得出了监督的学习算法以估计所提出的GNN模型的参数。还考虑了该算法的计算成本。一些实验结果显示出验证所提出的学习算法，并证明其概括能力。
Many underlying relationships among data in several areas of science and engineering, e.g., computer vision, molecular chemistry, molecular biology, pattern recognition, and data mining, can be represented in terms of graphs. In this paper, we propose a new neural network model, called graph neural network (GNN) model, that extends existing neural network methods for processing the data represented in graph domains. This GNN model, which can directly process most of the practically useful types of graphs, e.g., acyclic, cyclic, directed, and undirected, implements a function T(G, n) is an element of R-m that maps a graph G and one of its nodes n into an m-dimensional Euclidean space. A supervised learning algorithm is derived to estimate the parameters of the proposed GNN model. The computational cost of the proposed algorithm is also considered. Some experimental results are shown to validate the proposed learning algorithm, and to demonstrate its generalization capabilities.