Welcome to HiDeF’s documentation!

HiDeF 1 aims to reimagine hierarchical data clustering. The name HiDeF stands for “Hierarchical community Decoding Framework”. HiDeF integrates graph-based community detection and the idea of “persistent homology” in order to determine robust clustering patterns in complex data at multiple scales. Given the inputs in data points in graph or matrix formats, HiDeF returns a list of multiscale clusters with measurement of their robustness, as well as a directed acyclic graph (DAG) to represent the organization of these clusters.


Local installation of python package

From source:

python setup.py install

Installation via pip

pip install hidef


HiDeF is separately distributed via the CDAPS framework 2 in Cytoscape.


We try to maintain timely synchronization of the HiDeF versions across the Python package and Cytoscape. However, it may be possible to have small difference in results across the platforms due to the Cytoscape version is behind the latest version of the Python package.

What’s new

Version 1.1.3:

  • Community detection with multiple resolutions now run in parallel with python multiprocessing module

  • The default algorithm changed to Leiden as it is faster than Louvain

  • Now support multiplex community detection






Zheng, F, Zhang, S, et al. HiDeF: identifying persistent structures in multiscale ‘omics data. Genome Biology, 22, 21 (2021).


Singhal, A. Cao, S. Churas, C. et al. Multiscale community detection in Cytoscape. PLoS Comput. Biol. 16, e1008239 (2020).