Representation Learning on Graphs with Jumping Knowledge Networks
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.
Cite
@article{arxiv.1806.03536,
title = {Representation Learning on Graphs with Jumping Knowledge Networks},
author = {Keyulu Xu and Chengtao Li and Yonglong Tian and Tomohiro Sonobe and Ken-ichi Kawarabayashi and Stefanie Jegelka},
journal= {arXiv preprint arXiv:1806.03536},
year = {2018}
}
Comments
ICML 2018, accepted as a long oral presentation