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Hierarchical Graph Analysis

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# Higra: Hierarchical Graph Analysis

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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 representation of hierarchical clustering: dendrograms (trees) and ultra-metric distances (saliency maps);

  • hierarchical clustering algorithms: agglomerative clustering (single-linkage, average-linkage, complete-linkage, Ward, or custom rule), quasi-flat zones hierarchy, hierarchical watersheds;

  • component trees: min and max trees;

  • various algorithms to manipulate and explore hierarchical clustering: accumulators, filtering/simplification, cluster extraction, partitioning, horizontal and non-horizontal cuts, alignment;

  • optimization on hierarchies: optimal cuts, energy hierarchies;

  • algorithms on graphs: accumulators, computation of dissimilarities, partitioning;

  • assessment: supervised assessment of graph clustering and hierarchical clustering;

  • 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](https://github.com/QuantStack/xtensor) arrays in C++ and [numpy](https://github.com/numpy/numpy) arrays in Python).

## Installation

### Python frontend

The Python frontend can be installed with Pypi:

`bash pip install higra `

Supported systems:

  • Python 3.4, 3.5, 3.6, 3.7

  • Linux 64 bits, macOS, Windows 64 bits

### C++ backend

The C++ backend is an header only library. No facilities for system wide installation is currently provided: just copy/past where you need it!

## Demonstration and tutorials

Check the dedicated repository [Higra-Notebooks](https://github.com/PerretB/Higra-Notebooks) for a collection of Jupyter Notebooks dedicated to Higra.

## Build

### With cmake

Requires:

  • cmake

  • Python + Numpy

  • Google Benchmark (optional for benchmarking of the C++ backend)

Commands:

`bash git clone https://github.com/PerretB/Higra.git mkdir build cd build cmake ../Higra/ make `

Sometimes, cmake gets confused when several Python versions are installed on the system. You can specify which version to use with -DPYTHON_EXECUTABLE:FILEPATH=/PATH-TO-PYTHON/python, e.g.

` cmake -DPYTHON_EXECUTABLE:FILEPATH=/anaconda3/bin/python ../Higra/ `

The python package is build in the directory

` build/higra/ `

### With setup.py

The setup.py is a thin wrapper around the cmake script to provide compatibility with python setuptools.

` pip install cmake python setup.py bdist_wheel cd dist pip install higra*.whl `

## Third-party libraries

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

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