GraphOOD-EERM

Graph Neural Network Library for PyTorch

Python package built to ease deep learning on graph, on top of existing DL frameworks.

Anomaly detection related books, papers, videos, and toolboxes

Code for "Training-free Graph Neural Networks and the Power of Labels as Features" (TMLR 2024)

DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphical models and potential outcomes frameworks.

Codes and datasets for NeurIPS21 paper “Towards Open-World Feature Extrapolation: An Inductive Graph Learning Approach”

links to conference publications in graph-based deep learning

A unified, comprehensive and efficient recommendation library

Transfer Learning Library for Domain Adaptation, Task Adaptation, and Domain Generalization

SuperGlue: Learning Feature Matching with Graph Neural Networks (CVPR 2020, Oral)