gcnn

Code for "Social-STGCNN: A Social Spatio-Temporal Graph Convolutional Neural Network for Human Trajectory Prediction" CVPR 2020

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

Embedded Graph Convolutional Neural Networks (EGCNN) in TensorFlow

Dynamic Graph Convolutional Neural Network for 3D point cloud semantic segmentation

Bayesian Graph Neural Networks with Adaptive Connection Sampling - Pytorch

Code for: "Skeleton-Graph: Long-Term 3D Motion Prediction From 2D Observations Using Deep Spatio-Temporal Graph CNNs", ICCV2021 Workshops

Algorithms for prediction of congestion from Network State

Automated Headline generation and Aspect Based Sentiment Analysis

Marker-Based Motion Capture Data Denoising

Code for HAR-GCNN: Deep Graph CNNs for Human Activity Recognition From Highly Unlabeled Mobile Sensor Data, IEEE PerCom CoMoRea 2022

Android Malware Detection Model Based on Graph Neural Network

A Lightweight Residual Graph CNN for Pedestrians Trajectory Prediction

Graph Analysis Course Notes

GraphCNN + CNN Network for EEG Emotion Recognition

GCNN for EEG Emotion Recognition

A TensorFlow 2 implementation of Graph Convolutional Networks (GCN)

Weather prediction on stereo images using a graph equivariant convolutional neural network.

The program explores how various network structures influence system behavior under Braess' Paradox — a counterintuitive phenomenon in which adding resources to a network can degrade overall performance. The simulations are implemented using Python and NetworkX to model, analyze, and visualize traffic-like flow in graph-based systems.