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🔗 Source: grlplus.github.io
📊 Size: 2.67 MB
👤 Author: Yunsheng Bai, Ken Gu, Yizhou Sun, Wei Wang
⬇️ Downloads: 62

Summary

Bi-Level Graph Neural Networks (BI-GNN) are introduced for modeling biological link prediction tasks, such as drug-drug interaction (DDI) prediction. The model views the data as a bi-level graph, with a higher-level interaction graph and lower-level representation graphs for each biological entity. BI-GNN consists of two stages: the first stage generates vector representations for each representation graph using Graph Attention Networks (GAT), and the second stage propagates information from the lower-level graph embeddings to neighboring nodes in the interaction graph using another set of GAT layers. The model is evaluated on the DDI prediction task and shows promising results.

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Description

Bi-Level Graph Neural Networks (BI-GNN) are introduced for modeling biological link prediction tasks, such as drug-drug interaction (DDI) prediction.

Technical Information

  • File Format: PDF
  • File Size: 2.67 MB
  • Pages: 7
  • Language: EN
  • Author: Yunsheng Bai, Ken Gu, Yizhou Sun, Wei Wang
  • Total Downloads: 62
  • Last Updated: 6 days ago

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