How Powerful are Graph Neural Networks?

A Deep Learning library for EEG Tasks (Signals) Classification, based on TensorFlow.

A PyTorch implementation of "Predict then Propagate: Graph Neural Networks meet Personalized PageRank" (ICLR 2019).

Code for A GRAPH-CNN FOR 3D POINT CLOUD CLASSIFICATION (ICASSP 2018)

A Tensorflow implementation of "Bayesian Graph Convolutional Neural Networks" (AAAI 2019).

This project is a scalable unified framework for deep graph clustering.

Spatial Temporal Graph Convolutional Networks for Emotion Perception from Gaits

Deep learning for molecules quantum chemistry properties prediction

Code for "Distributed, Egocentric Representations of Graphs for Detecting Critical Structures" (ICML 2019)

Here is the code for the paper ``Aspect-Level Sentiment Analysis via Convolution over Dependency Tree'' accepted by EMNLP2019.

Node Classification with Graph Neural Networks

Graph-based Deterministic Policy Gradient for Repetitive Combinatorial Optimization Problems

A deep learning library to rank protein complexes using graph neural networks

A deep learning library for graph data structures

LCAONet - MPNN including electronic structure and orbital information, physically motivatied by the LCAO method.

Unofficial Tensorflow implementation of the CVPR'19 paper "Two-Stream Adaptive Graph Convolutional Networks for Skeleton-Based Action Recognition"

This project focuses on sign language recognition, using WLASL dataset for training models—one with CNN and the other with TGCN. The goal is to improve communication between the deaf and hearing communities, with potential applications in assistive technologies, education, and human-computer interaction.