PyTorch implementation of Multi-Label Image Recognition with Graph Convolutional Networks, CVPR 2019.

Traffic Forecasting using Graph Convolution + LSTM model is a ML model developed during the learning process of GCN. The primary soorce of this project is https://github.com/stellargraph/stellargraph

MLX implementation of GCN, with benchmark on MPS, CUDA and CPU (M1 Pro, M2 Ultra, M3 Max).

Course project for CS410. Drug Molecular Toxicity Prediction with GCN + Cloud ML Infra.

Unofficial PyTorch implementation of the paper "Multi-Label Image Recognition with Graph Convolutional Networks"

Project codes of (Robust) Privacy-Preserving Multi-label Image Recognition with Graph Convolutional Network

Multilabel image classification using GCN and nearest neighbour binary classifiers

Reproducible research comparing GNN (GraphSAGE, GCN, GAT) vs ML baselines (XGBoost, RF) on Elliptic++ Bitcoin fraud detection. Features ablation experiments revealing when tabular models outperform graph neural networks.

Multi-Label Image Recognition with Graph Convolutional Networks

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GCN - ML model

MLP vs GCN — a hands-on demo of why edges matter.

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Complete MLX implementation of DT-GCNN (Dynamic Triplet GRU-CNN) for text classification, optimized for Apple Silicon devices

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A systematic study comparing GCN and MLP performance across Homophilic (Cora) and Heterophilic (Texas, Chameleon) graph datasets. This repository serves as a foundational benchmark, illustrating the performance degradation of traditional GNNs in low-homophily environments, and synthesizes key research directions in Heterophilic Graph Learning.

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GIZMO and SPH-GCN based ML structure for particle-based cosmological simulation

Graph Machine Learning on the Cora citation dataset using GNN (GCN), traditional ML models, and Genetic Algorithm for hyperparameter optimization.

using ML models(GCN) to recommend bundle(several items) to users, based on user-item and user-bundle interactions and bundle-item information

Predict hospital discharge destinations (home vs. non-home) using MIMIC-IV FHIR data. This project combines traditional tabular ML methods and a knowledge graph–based R-GCN, offering robust classification performance and insights for patient care planning.

A general autonomous ML research framework that designs and runs experiments, learns from history, and improves over time with minimal human intervention. Supports classification, regression, recommendation, ranking, and graph learning across model families (MLP, PMF, NeuralMF, GCN, GraphSAGE, GAT).

EEG-based MG-GCN model for Major Depressive Disorder detection. Uses multi-scale graph representations to capture spatial and frequency features of brain signals, modeling local and global neural connections. Developed in MATLAB, it outperforms traditional ML and deep learning approaches.

ML_Experiment3-GCN

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