Gnn works from DeepFindr

Implementation of Subgraph Transformer (SubFormer) based on torch-geometric package.

Work we did for a practical course in graph learning, organized by department Informatik 7 at RWTH University

🔍 Detect IoT botnets like Mirai using GraphSAGE to analyze device activity as graphs, identifying malicious behavior effectively.

Molecular Graphs Node/Edge-level Classification GNN

The Transformer-based pipeline for urban intersection traffic pattern forecasting, robust to missing data and easy to reproduce.

"Structural Hazards in Global Production: A Graph Neural Network Approach to Sectoral Survival"** by Diego Vallarino (2025), submitted to *Structural Change and Economic Dynamics

Graph Network in Tensorflow

The PyTorch version of gfNN implemented by the paper.

Generative Neural Networks for Horse Image Colourization using Autoencoders. Reconstructing MNIST digits using Convolutional Autoencoders and GANs (Generative Adversarial Networks/ Generator-Discriminator). Adversarial Attack examples with MNIST.

Graph neural network modeling team performance through causal dynamics of internal dynamics, external collaborations, and contextual factors from Complexity Sciences.

This repository presents and compares HeterSUMGraph and variants using GATConv, GATv2Conv and a combination of HeterSUMGraph and SummaRuNNer (using HeterSUMGraph as a sentence encoder).

Applying Graph Convolution Networks for enhanced classification

New York Stock Exchange Temporal Graph Construction and Node Regression for Price Prediction

Research exam paper repo. Title: Scalability and Efficiency of Graph Neural Networks

Collection of Machine Learning and GNN methods for Molecular Property Prediction Task

Learning semi-Markovian DAGs with flow-based VAE

PyTorch implementation of 'How Graph Structure and Label Dependencies Contribute to Node Classification in a Large Network of Documents'

Striking the Balance: Human Pose Estimation based Optimal Fall Recognition