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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| Task | Dataset | Model | Metric Name | Metric Value | Global Rank | Benchmark |
|---|---|---|---|---|---|---|
| Node Property Prediction | ogbn-arxiv | JKNet (GCN-based) | Test Accuracy | 0.7219 ± 0.0021 | # 59 | |
| Validation Accuracy | 0.7335 ± 0.0007 | # 59 | ||||
| Number of params | 89000 | # 71 | ||||
| Ext. data | No | # 1 | ||||
| Node Classification | PPI | JK-LSTM | F1 | 97.6 | # 14 |
