Representation Learning on Graphs with Jumping Knowledge Networks

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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Results from the Paper

Task Dataset Model Metric Name Metric Value Global Rank Benchmark
Node Property Prediction ogbn-arxiv JKNet (GCN-based) Test Accuracy 0.7219 ± 0.0021 # 53
Validation Accuracy 0.7335 ± 0.0007 # 52
Number of params 89000 # 65
Ext. data No # 1
Node Classification PPI JK-LSTM F1 97.6 # 12