{"categories":[{"label":"Information Analysis","url":"https://skillfed.io/packages/category/scientific-engineering-information-analysis"},{"label":"Mathematics","url":"https://skillfed.io/packages/category/scientific-engineering-mathematics/2"},{"label":"Sociology","url":"https://skillfed.io/packages/category/sociology"}],"enrichment":{"capability":"Implements the Leiden community detection algorithm for graphs, exposing a C++ implementation to Python via igraph for partitioning networks into communities using multiple optimization methods.","skillfed_tags":["graph-algorithms","network-analysis","community-detection"],"use_cases":["Detect communities in social networks or collaboration graphs to identify groups of closely connected entities.","Analyze temporal networks by applying Leiden to multiple time slices to track community evolution.","Optimize modularity or other quality metrics on large graphs to find the best partition into communities.","Perform community detection on multiplex networks with positive and negative links or multiple relationship types.","Identify clusters in bipartite graphs such as author-paper or user-item networks."],"what_it_does":"leidenalg is a Python interface to a C++ implementation of the Leiden algorithm, a community detection method for partitioning large networks into cohesive groups. It extends the Louvain algorithm with improvements to guarantee well-connected communities and supports multiple optimization objectives: modularity, Reichardt-Bornholdt models, Constant Potts Model (CPM), Significance, and Surprise. The package can handle graphs with millions of nodes (memory-permitting) and includes support for multiplex networks (e.g., temporal or multi-layer graphs) and bipartite graphs, with the option to fix some community assignments during optimization.\n\nThe package depends on igraph for graph representation and manipulation. It is designed for research and scientific computing, where flexibility in community detection methods is valued. Prebuilt wheels are available for Python 3.9+ on Windows, macOS, and Linux, though source builds require C++ compilation tools and igraph's C core library.","worth_installing":"Yes, if you need flexible community detection on large graphs and can accept GPL-3.0-or-later licensing. The active maintenance, prebuilt wheels for modern Python, and lack of known vulnerabilities make it reliable. Medium install friction is manageable via wheels on major platforms. Not suitable for proprietary closed-source applications without license negotiation."},"id":"leidenalg","links":{"html":"https://skillfed.io/packages/leidenalg","md":"https://skillfed.io/packages/leidenalg.md","pypi":"https://pypi.org/project/leidenalg/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-05-24","license_spdx":"GPL-3.0-or-later","license_treatment":"copyleft","name":"leidenalg","python_support":"supports_current","summary":"Leiden is a general algorithm for methods of community detection in large networks."},"popularity":{"monthly_downloads":771839,"position":5100,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.12.0"}