Graph AI Comparison Hub
3 papers - avg viability 4.7
Top Papers
- Mitigating topology biases in Graph Diffusion via Counterfactual Intervention(6.0)
FairGDiff provides a counterfactual-based, debiasing solution for graph diffusion models to ensure fairness in graph generation tasks.
- Toward Graph-Tokenizing Large Language Models with Reconstructive Graph Instruction Tuning(6.0)
Develop an innovative graph alignment tuning for large language models to enhance graph-based tasks.
- Aligning the Unseen in Attributed Graphs: Interplay between Graph Geometry and Node Attributes Manifold(2.0)
A novel variational autoencoder framework for uncovering hidden connectivity patterns in attributed graphs.