论文标题
简化的图形卷积与异质
Simplified Graph Convolution with Heterophily
论文作者
论文摘要
最近的工作表明，一种称为简单图形卷积（SGC）的简单快速方法（Wu等，2019），它避免了深度学习，它具有公共图形机器学习基准的深度方法（GCNS）（GCNS）（KIPF＆Welling，2017）。 SGC中图形数据的使用隐式假定同质的常见但不是通用图的特征，其中节点链接到相似的节点。在这里，我们确认SGC确实通过合成和现实世界数据集的实验对异性词（即非全体动态）图无效。我们提出了自适应简单的图形卷积（ASGC），我们表明可以适应同粒细胞和异性图结构。像SGC一样，ASGC不是一个深层模型，因此是快速，可扩展和可解释的。此外，我们可以在自然合成数据模型上证明性能保证。从经验上讲，ASGC通常与现实数据集基准的节点分类的最新模型具有竞争力。 SGC论文质疑涉及同粒网络的常见图形问题是否需要图形神经网络的复杂性；我们的结果类似地表明，尽管深度学习通常可以达到最高的性能，但仅杂质结构并不需要这些更多涉及的方法。
Recent work has shown that a simple, fast method called Simple Graph Convolution (SGC) (Wu et al., 2019), which eschews deep learning, is competitive with deep methods like graph convolutional networks (GCNs) (Kipf & Welling, 2017) in common graph machine learning benchmarks. The use of graph data in SGC implicitly assumes the common but not universal graph characteristic of homophily, wherein nodes link to nodes which are similar. Here we confirm that SGC is indeed ineffective for heterophilous (i.e., non-homophilous) graphs via experiments on synthetic and real-world datasets. We propose Adaptive Simple Graph Convolution (ASGC), which we show can adapt to both homophilous and heterophilous graph structure. Like SGC, ASGC is not a deep model, and hence is fast, scalable, and interpretable; further, we can prove performance guarantees on natural synthetic data models. Empirically, ASGC is often competitive with recent deep models at node classification on a benchmark of real-world datasets. The SGC paper questioned whether the complexity of graph neural networks is warranted for common graph problems involving homophilous networks; our results similarly suggest that, while deep learning often achieves the highest performance, heterophilous structure alone does not necessitate these more involved methods.
