Improving Graph Neural Networks with Simple Architecture Design
Sunil Kumar Maurya Tokyo Institute of TechnologyTokyoJapan , Xin Liu AIRC, AISTTokyoJapan and Tsuyoshi Murata Tokyo Institute of TechnologyTokyoJapan
Abstract
Graph Neural Networks have emerged as a useful tool to learn on the data by applying additional constraints based on the graph structure. These graphs are often created with assumed intrinsic relations between the entities. In recent years, there have been tremendous improvements in the architecture design, pushing the performance up in various prediction tasks. In general, these neural architectures combine layer depth and node feature aggregation steps. This makes it challenging to analyze the importance of features at various hops and the expressiveness of the neural network layers. As different graph datasets show varying levels of homophily and heterophily in features and class label distribution, it becomes essential to understand which features are important for the prediction tasks without any prior information. In this work, we decouple the node feature aggregation step and depth of graph neural network and introduce several key design strategies for graph neural networks. More specifically, we propose to use softmax as a regularizer and ”Soft-Selector” of features aggregated from neighbors at different hop distances; and ”Hop-Normalization” over GNN layers. Combining thes
中文速览
图神经网络(GNN)在节点分类任务中表现出色，但现有模型把特征聚合与神经网络深度捆绑在一起，导致很难判断"哪一跳的邻居特征真正有用"，也难以同时适应同配性（homophily）和异配性（heterophily）两类图数据。为此，作者将特征聚合与表示学习彻底解耦，提出了一个名为FSGNN的轻量模型：用softmax对来自不同跳数的特征做"软选择"（Soft-Selector），让模型自动学习每一跳特征的重要性权重；同时引入Hop-Normalization对各层特征做归一化，并为每类输入特征配置独立的线性映射。实验结果表明，这个仅两层的简单模型在多个基准数据集上比当前最优GNN提升了最高64%的节点分类准确率，还能扩展到拥有数亿节点、数十亿条边的超大规模图上。这项工作的意义在于：它不仅提供了一个高效实用的GNN设计范式，还通过可解释的软选择参数让研究者直观了解图数据中哪些结构特征对预测最为关键。
原文 arXiv:2105.07634;中英对照 + 大白话阅读 https://aha.fim.ai/paper/2105.07634v1