Representation Learning on Graphs with Jumping Knowledge Networks

作者：Keyulu Xu, Chengtao Li, Yonglong Tian, Tomohiro Sonobe, Ken‐ichi Kawarabayashi, Stefanie Jegelka · 发表于：arXiv (Cornell University) · 年份：2018 · DOI：10.48550/arxiv.1806.03536 · 被引用次数：733 · 研究领域：Advanced Graph Neural Networks、Bioinformatics and Genomic Networks、Complex Network Analysis Techniques

Recent deep learning approaches for representation learning on graphs follow a neighborhood aggregation procedure. We analyze some important properties of these models, and propose a strategy to overcome those. In particular, the range of "neighboring" nodes that a node's representation draws from strongly depends on the graph structure, analogous to the spread of a random walk. To adapt to local neighborhood properties and tasks, we explore an architecture -- jumping knowledge (JK) networks -- that flexibly leverages, for each node, different neighborhood ranges to enable better structure-aware representation. In a number of experiments on social, bioinformatics and citation networks, we demonstrate that our model achieves state-of-the-art performance. Furthermore, combining the JK framework with models like Graph Convolutional Networks, GraphSAGE and Graph Attention Networks consistently improves those models' performance.