论文标题
超越局部图神经网络：一个归因的主题正则化框架
Beyond Localized Graph Neural Networks: An Attributed Motif Regularization Framework
论文作者
论文摘要
我们提出了Infomotif，这是一种新的半监督，基序调节的学习框架。我们克服了传递流行图神经网络（GNN）的消息的两个关键局限性：本地化（K-Layer GNN无法利用标签训练节点的K-HOP社区之外的功能）和过度平滑的（结构上不可区分的）表示。我们根据节点在不同的网络图案中的出现（与网络接近度无关）提出了归因的结构作用概念。如果两个节点在共同的属性集上参与拓扑相似的基序实例，则共同具有归因的结构角色。此外，Infomotif通过通过共同信息最大化的任意GNN的节点表示来实现架构独立性。我们的培训课程会在学习过程中动态优先考虑多个主题，而无需依赖基础图或学习任务中的分配假设。我们在框架中整合了三个最先进的GNN，以在六个不同的，现实世界中的数据集中显示出巨大的收益（精度为3-10％）。我们看到在当地邻里结构中具有稀疏训练标签和不同属性的节点的增长。
We present InfoMotif, a new semi-supervised, motif-regularized, learning framework over graphs. We overcome two key limitations of message passing in popular graph neural networks (GNNs): localization (a k-layer GNN cannot utilize features outside the k-hop neighborhood of the labeled training nodes) and over-smoothed (structurally indistinguishable) representations. We propose the concept of attributed structural roles of nodes based on their occurrence in different network motifs, independent of network proximity. Two nodes share attributed structural roles if they participate in topologically similar motif instances over co-varying sets of attributes. Further, InfoMotif achieves architecture independence by regularizing the node representations of arbitrary GNNs via mutual information maximization. Our training curriculum dynamically prioritizes multiple motifs in the learning process without relying on distributional assumptions in the underlying graph or the learning task. We integrate three state-of-the-art GNNs in our framework, to show significant gains (3-10% accuracy) across six diverse, real-world datasets. We see stronger gains for nodes with sparse training labels and diverse attributes in local neighborhood structures.
