CFG based program similarity using Graph Neural Networks
-
Updated
Mar 21, 2023 - Python
CFG based program similarity using Graph Neural Networks
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.
A collection of GNN projects
A Survey of Learning from Graphs with Heterophily
An implementation from scratch of Graph Convolutional Networks (GCN) using Numpy
Awesome GNN Learning For beginners
with GUG, Let's explore the Graph Neural Network!
code & report files for Project of EE394V SPR 2021
The repository is a collection of Jupyter notebooks showcasing various projects related to graph neural networks (GNNs). Each notebook provides a detailed explanation of the project and its implementation, making it easy for users to understand and replicate the results.
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)
Implement GNN models (GAT, GCN)
Literature indexing using Graph Neural Networks and label-guided text embeddings.
In this project I explore an potential approach to estimate a human’s intention in a dyadic collaborative manipulation task by learning to predict the intended future trajectory of the co-manipulated object via the latent graph representation of the system.
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'
Add a description, image, and links to the gnn-learning topic page so that developers can more easily learn about it.
To associate your repository with the gnn-learning topic, visit your repo's landing page and select "manage topics."