Code for running experiments and benchmarking on GNNExplainer: Generating Explanations for Graph Neural Networks

This is a Pytorch implementation of paper: DropEdge: Towards Deep Graph Convolutional Networks on Node Classification

Implementation Codes for NeurIPS22 paper "Dynamic Graph Neural Networks Under Spatio-Temporal Distribution Shift"

[TNNLS 2022] Code for "Learning Disentangled Graph Convolutional Networks Locally and Globally"

Open source tools for computational pathology - Nature BME

Implementation codes for NeurIPS23 paper "Spectral Invariant Learning for Dynamic Graphs under Distribution Shifts"

Learning Graphons via Structured Gromov-Wasserstein Barycenters

Official implementation of NeurIPS'21 paper"Motif-based Graph Self-Supervised Learning for Molecular Property Prediction"

Few-Shot Graph Learning for Molecular Property Prediction

The code base for the research project exploring disentangled representation-based self-supervised meta-learning.

Unsupervised Deep Disentangled Representation of Single-Cell Omics

Code of the "Leveraging Relational Information for Learning Weakly Disentangled Representations" paper.

Code for paper ''Mutual Information-based Disentangled Neural Networks for Classifying Unseen Categories in Different Domains: Application to Fetal Ultrasound Imaging'

This package contains a PyTorch Implementation of IB-GAN of the submitted paper in AAAI 2021

Disentangled gEnerative cAusal Representation (DEAR)

Pytorch implementation of Learning Disentangled Representations via Mutual Information Estimation (ECCV 2020)

[ICLR 2025] BrainOOD: Out-of-distribution Generalizable Brain Network Analysis

[NeurIPS 2023] Combating Bilateral Edge Noise for Robust Link Prediction

[ICML2023] InfoOT: Information Maximizing Optimal Transport

Code for AAAI-2024 paper: Graph Contrastive Invariant Learning from the Causal Perspective

Contrastive Disentangled Learning on Graph for Node Classification

[ICLR 2023] Learnable Randomness Injection (LRI) for interpretable Geometric Deep Learning.

[ICML 2024] How Interpretable Are Interpretable Graph Neural Networks?

[AAAI 2025] Subgraph Aggregation for Out-of-Distribution Generalization on Graphs

Code for ICML2020 paper - CLUB: A Contrastive Log-ratio Upper Bound of Mutual Information

[NeurIPS 2023] "Combating Bilateral Edge Noise for Robust Link Prediction"