GitHub

Data-Centric Learning from Unlabeled Graphs with Diffusion Model

This is the code for DCT, a data-centric transfer learning framework with diffusion model on graphs. This data-centric approach avoids the inappropriate designs of hand-crafted self-supervised tasks ...
IEEE

Transfer Learning-Based Region Statistical Data Completion via Double Graphs

Abstract: Region statistical data is widely utilized as fundamental spatial information for urban status observation, urban planning, and the formulation of urban policies. In practice, the collected ...
GitHub

Stack-Graphs Python bindings

Opinionated Python bindings for the tree-sitter-stack-graphs rust library. It exposes a minimal, opinionated API to leverage the stack-graphs library for reference resolution in source code. The rust ...
IEEE

Counterfactual Data Augmentation With Denoising Diffusion for Graph Anomaly Detection

Abstract: A critical aspect of graph neural networks (GNNs) is to enhance the node representations by aggregating node neighborhood information. However, when detecting anomalies, the representations ...