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arXiv · 1806.03536

Representation Learning on Graphs with Jumping Knowledge Networks

Keyulu Xu·Chengtao Li·Yonglong Tian·Tomohiro Sonobe·Ken-ichi Kawarabayashi·Stefanie Jegelka
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Abstract

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.

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Keyulu Xu, Chengtao Li, Yonglong Tian, Tomohiro Sonobe, Ken-ichi Kawarabayashi, Stefanie Jegelka. 2018-06-09. Representation Learning on Graphs with Jumping Knowledge Networks. https://arxiv.org/abs/1806.03536

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