Papers › Just Jump: Dynamic Neighborhood Aggregation in Graph Neural Networks
Just Jump: Dynamic Neighborhood Aggregation in Graph Neural Networks
Matthias Fey
We propose a dynamic neighborhood aggregation (DNA) procedure guided by (multi-head) attention for representation learning on graphs. In contrast to current graph neural networks which follow a simple neighborhood aggregation scheme, our DNA procedure allows for a selective and node-adaptive aggregation of neighboring embeddings of potentially differing locality. In order to avoid overfitting, we propose to control the channel-wise connections between input and output by making use of grouped linear projections. In a number of transductive node-classification experiments, we demonstrate the effectiveness of our approach.
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Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Node Classification | Citeseer | DNAConv | Accuracy | 74.50% | #27 of 71 | Archive leaderboard | report |
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