{"categories":[{"label":"Mathematics","url":"https://skillfed.io/packages/category/scientific-engineering-mathematics/3"}],"enrichment":{"capability":"Graspologic provides graph statistical algorithms and utilities for processing, analyzing, and modeling networks using specialized statistical techniques that account for graph structure.","skillfed_tags":["graph-analysis","network-science","statistical-learning"],"use_cases":["Embed graph structures into low-dimensional vector spaces for downstream machine learning tasks.","Test statistical hypotheses about network structure, connectivity, or community organization.","Cluster nodes or detect communities within networks using graph-aware algorithms.","Analyze and compare multiple networks to identify structural differences or similarities.","Preprocess and visualize network data for exploratory analysis or publication."],"what_it_does":"Graspologic is a Python library for statistical analysis of graphs and networks. It provides algorithms and utilities designed to work with the spatial structure of networks, applying specialized statistical techniques rather than treating graph data as unstructured. The package includes methods for graph embedding, clustering, hypothesis testing, and other analyses that respect the relational structure inherent in network data.\n\nThe library depends on a mature scientific Python stack (numpy, scipy, scikit-learn, networkx, matplotlib, seaborn) and is actively maintained with support for Python 3.9 through 3.12. It is intended for researchers and practitioners working with network data in domains like social networks, biological networks, or any domain where relationships between entities matter. The package is production-stable and has been in active development since 2020.","worth_installing":"Yes. Graspologic is a mature, actively maintained library with no known vulnerabilities, permissive licensing, and low installation friction. It is well-suited for anyone working with network or graph data who needs statistical algorithms beyond basic graph operations. The dependency footprint is substantial but standard in scientific Python; if you already use scikit-learn and scipy, adding graspologic is straightforward."},"id":"graspologic","links":{"html":"https://skillfed.io/packages/graspologic","md":"https://skillfed.io/packages/graspologic.md","pypi":"https://pypi.org/project/graspologic/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2025-09-08","license_spdx":null,"license_treatment":"permissive","name":"graspologic","python_support":"capped_below_current","summary":"A set of Python modules for graph statistics"},"popularity":{"monthly_downloads":148154,"position":11043,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"3.4.4"}