gnn-learning

A Graph Deep Learning Library for Music.

CFG based program similarity using Graph Neural Networks

An implementation from scratch of major Graph Neural Network (GNN) architectures using Numpy

Listing the research works related to risk control based on GNN and its interpretability. 1. we can learn the application of GNN in risk control (including fraud detection). 2. For possible prediction, we can use the interpretability of GNN to explaine how can we get such results.

This repository is a brief tutorial about how Graph convolutional networks and message passing networks work with example code demonstration using pytorch and torch_geometric

Official PyTorch Implementation of paper 'Edge-Based Graph Neural Networks for Cell-Graph Modeling and Prediction'

A Graph operation in GNN

Graph Machine Learning Training Tutorial

WIP Bidirectional RGAT for multistage incident classification, prioritization, and an addition to automation processes through a bidirectional R-GAT; the graphs are built using events as nodes, mitigating overfitting risks of entity based nodes. The project is learning by doing approach to understanding its potential and limitations

Developed a Graph Neural Network–based fraud detection system using PyTorch Geometric by modeling financial transactions as a graph, achieving high recall and ROC-AUC on a real-world Bitcoin transaction dataset.

MoleColyte: The Pharmaceutical Acolyte

SMILES converted into Graphs that contains atomic information, bonding informatics. Graphs considered as input for the NN to Predict Melting Pont of Liquid Crystals (LCs)

first commit