List of Molecular and Material design using Generative AI and Deep Learning

[IJCAI 2023 survey track]A curated list of resources for chemical pre-trained models

A PyTorch Implementation of Gated Graph Sequence Neural Networks (GGNN)

3D Graph Neural Networks for RGBD Semantic Segmentation

A review of papers proposing novel GNN methods with application to brain connectivity published in 2017-2020.

FedGraphNN: A Federated Learning Platform for Graph Neural Networks with MLOps Support. The previous research version is accepted to ICLR'2021 - DPML and MLSys'21 - GNNSys workshops.

List of molecules (small molecules, RNA, peptide, protein, enzymes, antibody, and PPIs) conformations and molecular dynamics (force fields) using generative artificial intelligence and deep learning

A Survey of Learning from Graphs with Heterophily

(WWW 2021) Source code of PC-GNN

Code for the paper: "On the Bottleneck of Graph Neural Networks and Its Practical Implications"

Awesome De novo drugs design papers

Full Stack Graph Machine Learning: Theory, Practice, Tools and Techniques

[BIBM2024] MEEG and AT-DGNN: Improving EEG Emotion Recognition with Music Introducing and Graph-based Learning

[ACM Computing Surveys'23] Implementations or refactor of some temporal link prediction/dynamic link prediction methods and summary of related open resources for survey paper "Temporal Link Prediction: A Unified Framework, Taxonomy, and Review" which has been accepted by ACM Computing Surveys.

Edge-Augmented Graph Transformer

GNNs in Recommendation Systems

[3DV21] Visual Camera Re-Localization Using Graph Neural Networks and Relative Pose Supervision, M. Türkoǧlu et al.

Re-implementation and extension of the work described in "Learning to Represent Programs with Graphs"

Bags of Tricks in OGB (node classification) with GCNs.

NeuroGNN is a state-of-the-art framework for precise seizure detection and classification from EEG data. It employs dynamic Graph Neural Networks (GNNs) to capture intricate spatial, temporal, semantic, and taxonomic correlations between EEG electrode locations and brain regions, resulting in improved accuracy. Presented at PAKDD '24.