Graph Neural Networks
by Community · free · Last verified 2026-03-17
Applies deep learning directly to graph-structured data by passing and aggregating messages between connected nodes across multiple layers, enabling node classification, link prediction, and graph-level tasks. Powers state-of-the-art knowledge graph completion, molecular property prediction, and social network analysis.
https://pytorch-geometric.readthedocs.io/ ↗B+
B+—Good
Adoption: B+Quality: AFreshness: ACitations: AEngagement: F
Specifications
- License
- MIT
- Pricing
- free
- Capabilities
- message-passing, node-classification, link-prediction, graph-classification, knowledge-graph-embedding
- Integrations
- PyTorch Geometric, DGL, Spektral, GraphSAGE
- Use Cases
- Knowledge graph completion and link prediction, Molecular property prediction for drug discovery, Social network community detection
- API Available
- No
- Difficulty
- advanced
- Prerequisites
- deep-learning, graph-theory, linear-algebra
- Supported Agents
- Tags
- GNN, graph-learning, node-classification, link-prediction, knowledge-graph
- Added
- 2026-03-17
- Completeness
- 100%
Index Score
70.6Adoption
78
Quality
87
Freshness
88
Citations
88
Engagement
0