graph foundation models
AI models that leverage graph-based representations to capture complex relationships and interactions in data. These models are particularly effective for social network analysis, knowledge graphs, and other domains where relational structures are key.
- $\texttt{G1}$: Teaching LLMs to Reason on Graphs with Reinforcement Learning
- Deeper with Riemannian Geometry: Overcoming Oversmoothing and Oversquashing for Graph Foundation Models
- Enhanced Expert Merging for Mixture-of-Experts in Graph Foundation Models
- Equivariance Everywhere All At Once: A Recipe for Graph Foundation Models
- Flatten Graphs as Sequences: Transformers are Scalable Graph Generators
- GRAVER: Generative Graph Vocabularies for Robust Graph Foundation Models Fine-tuning
- GraphKeeper: Graph Domain-Incremental Learning via Knowledge Disentanglement and Preservation
- GraphLand: Evaluating Graph Machine Learning Models on Diverse Industrial Data
- GraphMaster: Automated Graph Synthesis via LLM Agents in Data-Limited Environments
- RAG4GFM: Bridging Knowledge Gaps in Graph Foundation Models through Graph Retrieval Augmented Generation
- RAG4GFM: Bridging Knowledge Gaps in Graph Foundation Models through Graph Retrieval Augmented Generation
- Towards Graph Foundation Models: Training on Knowledge Graphs Enables Transferability to General Graphs