GraSPy: Graph Statistics in Python

Abstract

We introduce GraSPy, a Python library devoted to statistical inference, machine learning, and visualization of random graphs and graph populations. This package provides flexible and easy-to-use algorithms for analyzing and understanding graphs with a scikit-learn compliant API. GraSPy can be downloaded from Python Package Index (PyPi), and is released under the Apache 2.0 open-source license. The documentation and all releases are available at https://neurodata.io/graspy.

References in corpus (4)

• Scikit-learn: Machine Learning in Python
• Stochastic blockmodels and community structure in networks
• Inference for multiple heterogeneous networks with a common invariant subspace
• A nonparametric two-sample hypothesis testing problem for random dot product graphs

Cited by in corpus (6)

• Inference for multiple heterogeneous networks with a common invariant subspace
• Online Change Point Detection for Weighted and Directed Random Dot Product Graphs
• Community Correlations and Testing Independence Between Binary Graphs
• SCOREH+: A High-Order Node Proximity Spectral Clustering on Ratios-of-Eigenvectors Algorithm for Community Detection
• Multiscale Comparative Connectomics
• Valid Two-Sample Graph Testing via Optimal Transport Procrustes and Multiscale Graph Correlation with Applications in Connectomics