PyTorch Geometric Signed Directed
A PyTorch Geometric extension library for signed and directed graph neural networks, embedding, and clustering methods.
What is PyTorch Geometric Signed Directed?
PyTorch Geometric Signed Directed is an open-source extension library for PyTorch Geometric that provides specialized graph neural network models, data loaders, and utilities for analyzing signed and directed graphs. It solves the problem of applying deep learning to complex network structures where relationships have both direction (e.g., follower/following) and sign (e.g., trust/distrust), which are not handled by standard GNN libraries.
Researchers and practitioners in graph machine learning, network science, and data mining who need to work with signed social networks, directed citation graphs, trust networks, or any domain involving asymmetric and/or polarized relationships.
It consolidates numerous state-of-the-art research papers into a single, PyTorch Geometric-compatible package, saving users from re-implementing complex methods and providing a standardized interface for experimenting with signed and directed graph tasks.
Overview
PyTorch Geometric Signed Directed is a signed/directed graph neural network extension library for PyTorch Geometric. The paper is accepted by LoG 2023.
Use Cases
Best For
- Node classification in signed social networks (e.g., predicting user polarity)
Related Projects
Found a gem we're missing?
Open-Awesome is built by the community, for the community. Submit a project, suggest an awesome list, or help improve the catalog on GitHub.
