Papers
1–3 of 3Research Paper·Mar 2, 2026
Mitigating topology biases in Graph Diffusion via Counterfactual Intervention
Graph diffusion models have gained significant attention in graph generation tasks, but they often inherit and amplify topology biases from sensitive attributes (e.g. gender, age, region), leading to ...
6.0 viability
Research Paper·Mar 2, 2026
Toward Graph-Tokenizing Large Language Models with Reconstructive Graph Instruction Tuning
The remarkable success of large language models (LLMs) has motivated researchers to adapt them as universal predictors for various graph-related tasks, with the ultimate goal of developing a graph fou...
6.0 viability
Research Paper·Jan 30, 2026
Aligning the Unseen in Attributed Graphs: Interplay between Graph Geometry and Node Attributes Manifold
The standard approach to representation learning on attributed graphs -- i.e., simultaneously reconstructing node attributes and graph structure -- is geometrically flawed, as it merges two potentiall...
2.0 viability