Skip to main content

Hierarchical Graph Analysis

Project description

Higra: Hierarchical Graph Analysis

Build Status Build status codecov Documentation Status

Higra is a C++/Python library for efficient sparse graph analysis with a special focus on hierarchical methods. Some of the main features are:

  • efficient methods and data structures to handle the dual representations of hierarchical clustering: trees (dendrograms) and saliency maps (ultrametric distances);
  • hierarchical clusterings: quasi-flat zone hierarchy, hierarchical watersheds, agglomerative clustering (single-linkage, average-linkage, complete-linkage, exponential-linkage, Ward, or user provided linkage rule), constrained connectivity hierarchy;
  • component trees: min and max trees;
  • manipulate and explore hierarchies: simplification, accumulators, cluster extraction, various attributes (size, volume, dynamics, perimeter, compactness, moments, etc.), horizontal and non-horizontal cuts, hierarchies alignment;
  • optimization on hierarchies: optimal cuts, energy hierarchies;
  • algorithms on graphs: accumulators, vertices and clusters dissimilarities, region adjacency graphs, minimum spanning trees and forests, watershed cuts;
  • assessment: supervised assessment of graph clusterings and hierarchical clusterings;
  • image toolbox: special methods for grid graphs, tree of shapes, hierarchical clustering methods dedicated to image analysis, optimization of Mumford-Shah energy.

Higra is thought for modularity, performance and seamless integration with classical data analysis pipelines. The data structures (graphs and trees) are decoupled from data (vertex and edge weights ) which are simply arrays (xtensor arrays in C++ and numpy arrays in Python).


The Python package can be installed with Pypi:

pip install higra

Supported systems:

Prebuilt binaries are currently not available for ARM architectures.


Demonstration and tutorials

A collection of demonstration notebooks is available in the documentation. Notebooks are stored in a dedicated repository Higra-Notebooks.

Code samples

This example demonstrates the construction of a single-linkage hierarchical clustering and its simplification by a cluster size criterion.

Example on clustering

This example demonstrates the use of hierarchical clustering for image filtering.

Example on image filtering

Developing C++ extensions

While Higra provides many vectorized operators to implement algorithms efficiently in Python, it is possible that some operations cannot be done efficiently in Python. In such case, the Higra-cppextension-cookiecutter enables to easily setup and generate c++ extension using Higra with Python bindings.

License and how-to cite

The license Cecill-B is fully compatible with BSD-like licenses (BSD, X11, MIT) with an attribution requirement.

The recommended way to give attribution is by citing the following presentation article:

B. Perret, G. Chierchia, J. Cousty, S.J. F. Guimarães, Y. Kenmochi, L. Najman, Higra: Hierarchical Graph Analysis, SoftwareX, Volume 10, 2019. DOI: 10.1016/j.softx.2019.100335

     title = "Higra: Hierarchical Graph Analysis",
     journal = "SoftwareX",
     volume = "10",
     pages = "1--6",
     year = "2019",
     issn = "2352-7110",
     doi = "10.1016/j.softx.2019.100335",
     author = "B. Perret and G. Chierchia and J. Cousty and S.J. F. Guimar\~{a}es and Y. Kenmochi and L. Najman",

Third-party libraries

Higra bundles several third-party libraries (inside the lib folder):

Project details

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distributions

No source distribution files available for this release.See tutorial on generating distribution archives.

Built Distributions

higra-0.6.8-cp312-cp312-win_amd64.whl (5.6 MB view hashes)

Uploaded cp312

higra-0.6.8-cp311-cp311-win_amd64.whl (5.6 MB view hashes)

Uploaded cp311

higra-0.6.8-cp310-cp310-win_amd64.whl (5.6 MB view hashes)

Uploaded cp310

higra-0.6.8-cp39-cp39-win_amd64.whl (5.7 MB view hashes)

Uploaded cp39

higra-0.6.8-cp38-cp38-win_amd64.whl (5.6 MB view hashes)

Uploaded cp38

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page