论文标题
通过自适应采样的图表表示网络
Graph Representation Learning Network via Adaptive Sampling
论文作者
论文摘要
图形注意网络（GAT）和图形是在图形结构数据上运行的神经网络体系结构，并已广泛研究以进行链接预测和节点分类。图形提出的一个挑战是如何根据图形结构巧妙地结合邻居功能。 GAT通过注意力解决了这个问题，但是GAT面临的挑战是其在较大且密集的图表上的可伸缩性。在这项工作中，我们提出了一种新的体系结构来解决这些更有效的问题，并且能够合并不同的边缘类型信息。它通过参加从加权多步过渡概率采样的邻居来生成节点表示。我们在跨传感和电感设置上进行实验。实验在几个图基准上获得了可比或更好的结果，包括Cora，Citeseer，PubMed，PPI，Twitter和YouTube数据集。
Graph Attention Network (GAT) and GraphSAGE are neural network architectures that operate on graph-structured data and have been widely studied for link prediction and node classification. One challenge raised by GraphSAGE is how to smartly combine neighbour features based on graph structure. GAT handles this problem through attention, however the challenge with GAT is its scalability over large and dense graphs. In this work, we proposed a new architecture to address these issues that is more efficient and is capable of incorporating different edge type information. It generates node representations by attending to neighbours sampled from weighted multi-step transition probabilities. We conduct experiments on both transductive and inductive settings. Experiments achieved comparable or better results on several graph benchmarks, including the Cora, Citeseer, Pubmed, PPI, Twitter, and YouTube datasets.
