Load graph collections
Work with CSV, DataFrame, URL, and NetworkX inputs through NEExT graph collections.
Graph machine learning research toolkit
Network embedding experiments, from graph data to model insight.
NEExT is a Python framework and local Workbench for graph loading, structural features, graph embeddings, model training, and scientific workflow inspection.
MUTAG graphs Structural node features Graph embeddings Classifier metrics
Python library
Core graph experimentation primitives.
Compute features
Generate structural node features and trusted custom feature methods for graph workflows.
Train and inspect models
Build graph-level embeddings and supervised models with persisted experiment artifacts.
Local Workbench
A desktop-style UI over real NEExT workflows.
The Workbench organizes projects, datasets, features, embeddings, models, job output, and artifact lineage in a local browser interface for research workflows.
Updates
The public site is being prepared.
Newsletter, demos, screenshots, documentation links, and release notes will be added here.