论文标题
图形注意架构的鸟眼教程
A Bird's-Eye Tutorial of Graph Attention Architectures
论文作者
论文摘要
图形神经网络（GNNS）在图形结构问题的性能方面表现出了巨大的进步，尤其是在自然语言处理，计算机视觉和推荐系统的领域。受到变压器体系结构的成功的启发，在许多问题中试图推进最新技术的GNN的注意变体中，已经有了不断增长的工作。将“注意”纳入图挖掘中已被视为克服与图形结构数据以及编码软感应偏置相关的噪声，异质性和复杂性的一种方式。因此，研究这些变体从鸟类的观点评估它们的优势和缺点至关重要和有利。我们提供了一个系统的专注教程，以基于注意力的GNN为中心，希望使研究人员受益于处理图形结构问题的研究人员。我们的教程从注意力函数的角度着眼于GNN变体，并迭代地构建了读者对不同图形注意变体的理解。
Graph Neural Networks (GNNs) have shown tremendous strides in performance for graph-structured problems especially in the domains of natural language processing, computer vision and recommender systems. Inspired by the success of the transformer architecture, there has been an ever-growing body of work on attention variants of GNNs attempting to advance the state of the art in many of these problems. Incorporating "attention" into graph mining has been viewed as a way to overcome the noisiness, heterogenity and complexity associated with graph-structured data as well as to encode soft-inductive bias. It is hence crucial and advantageous to study these variants from a bird's-eye view to assess their strengths and weaknesses. We provide a systematic and focused tutorial centered around attention based GNNs in a hope to benefit researchers dealing with graph-structured problems. Our tutorial looks at GNN variants from the point of view of the attention function and iteratively builds the reader's understanding of different graph attention variants.
