Just Jump: Dynamic Neighborhood Aggregation in Graph Neural Networks
arXiv:1904.04849
Abstract
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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- Measuring and Relieving the Over-smoothing Problem for Graph Neural Networks from the Topological View
- NODE-SELECT: A Graph Neural Network Based On A Selective Propagation Technique
- Information Obfuscation of Graph Neural Networks
- Direct Feedback Alignment Scales to Modern Deep Learning Tasks and Architectures
- HighwayGraph: Modelling Long-distance Node Relations for Improving General Graph Neural Network
- BiTe-GCN: A New GCN Architecture via BidirectionalConvolution of Topology and Features on Text-Rich Networks
- Dual GNNs: Graph Neural Network Learning with Limited Supervision