论文标题
EIGEN-GNN：用于GNN的图形结构保存插件
Eigen-GNN: A Graph Structure Preserving Plug-in for GNNs
论文作者
论文摘要
图形神经网络（GNN）是图形上新兴的机器学习模型。尽管在理论上表明了足够深的GNN能够完全保存图形结构，但实际上，大多数现有的GNN模型都是较浅的，本质上是以功能为中心的。我们从经验和分析上表明，现有的浅GNN不能很好地保留图形结构。为了克服这一基本挑战，我们提出了Eigen-GNN，这是一个简单而有效且一般的插件模块，以提高GNNS在保存图形结构方面的能力。具体而言，我们通过将GNN视为降低尺寸的类型并扩展了初始维度降低碱基，将图形结构的特征空间与GNN集成在一起。不需要增加深度，Eigen-gnn在处理特征驱动和结构驱动的任务方面具有更多的灵活性，因为初始碱基既包含节点特征和图形结构。我们提出了广泛的实验结果，以证明特征GNN对包括节点分类，链接预测和图同构测试在内的任务的有效性。
Graph Neural Networks (GNNs) are emerging machine learning models on graphs. Although sufficiently deep GNNs are shown theoretically capable of fully preserving graph structures, most existing GNN models in practice are shallow and essentially feature-centric. We show empirically and analytically that the existing shallow GNNs cannot preserve graph structures well. To overcome this fundamental challenge, we propose Eigen-GNN, a simple yet effective and general plug-in module to boost GNNs ability in preserving graph structures. Specifically, we integrate the eigenspace of graph structures with GNNs by treating GNNs as a type of dimensionality reduction and expanding the initial dimensionality reduction bases. Without needing to increase depths, Eigen-GNN possesses more flexibilities in handling both feature-driven and structure-driven tasks since the initial bases contain both node features and graph structures. We present extensive experimental results to demonstrate the effectiveness of Eigen-GNN for tasks including node classification, link prediction, and graph isomorphism tests.
