NEExT is a powerful Python framework for graph analysis, embedding computation, and machine learning on graph-structured data. It provides a unified interface for working with different graph backends (NetworkX and iGraph), computing node features, generating graph embeddings, and training machine learning models.
Detailed documentation is available in the docs directory. Build it locally or visit the online documentation at NEExT Documentation.
-
Flexible Graph Handling
- Support for both NetworkX and iGraph backends
- Automatic graph reindexing and largest component filtering
- Node sampling capabilities for large graphs
- Rich attribute support for nodes and edges
-
Comprehensive Node Features
- PageRank
- Degree Centrality
- Closeness Centrality
- Betweenness Centrality
- Eigenvector Centrality
- Clustering Coefficient
- Local Efficiency
- LSME (Local Structural Motif Embeddings)
-
Graph Embeddings
- Approximate Wasserstein
- Exact Wasserstein
- Sinkhorn Vectorizer
- Customizable embedding dimensions
-
Machine Learning Integration
- Classification and regression support
- Dataset balancing options
- Cross-validation with customizable splits
- Feature importance analysis
pip install NEExT# Clone the repository
git clone https://github.com/ashdehghan/NEExT.git
cd NEExT
# Install with development dependencies
pip install -e ".[dev]"# For running tests
pip install -e ".[test]"
# For building documentation
pip install -e ".[docs]"
# For running experiments
pip install -e ".[experiments]"
# Install all components
pip install -e ".[dev,test,docs,experiments]"from NEExT import NEExT
# Initialize the framework
nxt = NEExT()
nxt.set_log_level("INFO")
# Load graph data
graph_collection = nxt.read_from_csv(
edges_path="edges.csv",
node_graph_mapping_path="node_graph_mapping.csv",
graph_label_path="graph_labels.csv",
reindex_nodes=True,
filter_largest_component=True,
graph_type="igraph"
)
# Compute node features
features = nxt.compute_node_features(
graph_collection=graph_collection,
feature_list=["all"],
feature_vector_length=3
)
# Compute graph embeddings
embeddings = nxt.compute_graph_embeddings(
graph_collection=graph_collection,
features=features,
embedding_algorithm="approx_wasserstein",
embedding_dimension=3
)
# Train a classifier
model_results = nxt.train_ml_model(
graph_collection=graph_collection,
embeddings=embeddings,
model_type="classifier",
sample_size=50
)NEExT supports node sampling for handling large graphs:
# Load graphs with 70% of nodes
graph_collection = nxt.read_from_csv(
edges_path="edges.csv",
node_graph_mapping_path="node_graph_mapping.csv",
node_sample_rate=0.7 # Use 70% of nodes
)# Compute feature importance
importance_df = nxt.compute_feature_importance(
graph_collection=graph_collection,
features=features,
feature_importance_algorithm="supervised_fast",
embedding_algorithm="approx_wasserstein"
)NEExT includes several pre-built experiments in the examples/experiments directory:
Investigates the effect of node sampling on classifier accuracy:
cd examples/experiments
python node_sampling_experiments.pysrc_node_id,dest_node_id
0,1
1,2
...
node_id,graph_id
0,1
1,1
2,2
...
graph_id,graph_label
1,0
2,1
...
# Run all tests
pytest
# Run with coverage
pytest --cov=NEExT
# Run specific test file
pytest tests/test_node_sampling.pycd docs
make htmlThe project uses several tools for code quality:
# Format code
black .
# Sort imports
isort .
# Check style
flake8 .
# Type checking
mypy .- Fork the repository
- Create a feature branch
- Make your changes
- Run tests
- Submit a pull request
This project is licensed under the MIT License - see the LICENSE file for details.
- Ash Dehghan - ash.dehghan@gmail.com
- NetworkX team for the graph algorithms
- iGraph team for the efficient graph operations
- Scikit-learn team for machine learning components
For questions and support:
- Email: ash@anomalypoint.com
- GitHub Issues: NEExT Issues
- 0.1.0
- Initial release
- Basic graph operations
- Node feature computation
- Graph embeddings
- Machine learning integration