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Summary
YinYanGNN is a novel graph neural network (GNN) architecture that incorporates negative sampling in the forward pass to improve link prediction performance while maintaining efficiency. It achieves accuracy comparable to edge-wise models while retaining the efficiency of node-wise models.
Highlights
- YinYanGNN integrates negative sampling in the forward pass to improve expressiveness.
- It achieves accuracy comparable to edge-wise models while retaining the efficiency of node-wise models.
- The model uses a principled regularization factor to push node embeddings apart during the forward pass.
- YinYanGNN outperforms state-of-the-art node-wise models in terms of accuracy while matching their efficiency.
- It can be further improved to sublinear complexity using Flashlight.
- The model is robust to different negative samplers and can learn to balance multiple negative sampling graphs.
Key Insights
- YinYanGNN's incorporation of negative sampling in the forward pass allows it to capture more nuanced relationships between nodes, leading to improved link prediction performance.
- The model's ability to balance multiple negative sampling graphs enables it to adapt to different graph structures and improve its expressiveness.
- By using a principled regularization factor, YinYanGNN can effectively push node embeddings apart during the forward pass, reducing the impact of isomorphic nodes.
- The model's efficiency is comparable to node-wise models, making it suitable for large-scale graph datasets.
- YinYanGNN's performance can be further improved by using techniques such as Flashlight to accelerate the decoding process.
- The model's robustness to different negative samplers makes it a reliable choice for link prediction tasks.
- YinYanGNN's ability to learn to balance multiple negative sampling graphs enables it to adapt to different graph structures and improve its expressiveness.
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Citation
Wang, Y., Hu, X., Gan, Q., Huang, X., Qiu, X., & Wipf, D. (2023). Efficient Link Prediction via GNN Layers Induced by Negative Sampling. arXiv. https://doi.org/10.48550/ARXIV.2310.09516