论文标题
如何找到您友好的邻居：与自学的图形注意力设计
How to Find Your Friendly Neighborhood: Graph Attention Design with Self-Supervision
论文作者
论文摘要
图形神经网络中的注意机制旨在为重要的邻居节点分配较大的权重，以更好地表示。但是，图表所学的知识并不理想，尤其是当图形嘈杂时。在本文中，我们提出了一个自我监督的图形注意力网络（SuperGat），这是一个改进的嘈杂图形的图形注意模型。具体而言，我们利用两种与自我监督任务兼容的注意形式来预测边缘，其存在和不存在包含有关节点之间关系重要性的固有信息。通过编码边缘，Supergat在区分错误连接的邻居时会学到更具表现力的关注。我们发现两个图形特征会影响注意力形式和自我统治的有效性：同质和平均程度。因此，我们的食谱提供了指导，以了解这两个图形特征是知道的。我们在17个现实世界数据集上的实验表明，我们的配方在其中15个数据集中概括了，而由食谱设计的模型显示出比基线的性能改善。
Attention mechanism in graph neural networks is designed to assign larger weights to important neighbor nodes for better representation. However, what graph attention learns is not understood well, particularly when graphs are noisy. In this paper, we propose a self-supervised graph attention network (SuperGAT), an improved graph attention model for noisy graphs. Specifically, we exploit two attention forms compatible with a self-supervised task to predict edges, whose presence and absence contain the inherent information about the importance of the relationships between nodes. By encoding edges, SuperGAT learns more expressive attention in distinguishing mislinked neighbors. We find two graph characteristics influence the effectiveness of attention forms and self-supervision: homophily and average degree. Thus, our recipe provides guidance on which attention design to use when those two graph characteristics are known. Our experiment on 17 real-world datasets demonstrates that our recipe generalizes across 15 datasets of them, and our models designed by recipe show improved performance over baselines.
