Welcome to Absorbing Random-Walk Centrality’s documentation!

Contents:

Overview

Absorbing Random-Walk Centrality

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This is an implementation of the absorbing random-walk centrality measure for nodes in graphs. For the definition of the measure, as well as a study of the related optimization problem and algorithmic techniques, please see the pre-print publication on arXiv. A short version of this paper will appear in the ICDM 2015.

To cite this work, please use

Mavroforakis, Charalampos, Michael Mathioudakis, and Aristides Gionis.
"Absorbing random-walk centrality: Theory and algorithms"
Data Mining (ICDM), 2015 IEEE International Conference on. IEEE, 2015.

Installation

You can install the absorbing_centrality package by executing the following command in a terminal.

pip install absorbing_centrality

Documentation

For instructions on how to use the package, consult its documentation.

Example

You can find an example of how to use this package in this IPython notebook.

Development

To run all the tests for the code, you will need tox – check its webpage for instructions on how to install it.

Once tox is installed, use your terminal to enter the directory with the local copy of the code (here it’s named ‘absorbing-centrality‘) and simply type the following command.

absorbing-centrality $ tox

If everything goes well, you’ll receive a congratulatory message.

Note that the code is distributed under the Open Source Initiative (ISC) license. For the exact terms of distribution, see the LICENSE.

Copyright (c) 2015, absorbing-centrality contributors,
Charalampos Mavroforakis <cmav@bu.edu>,
Michael Mathioudakis <michael.mathioudakis@aalto.fi>,
Aristides Gionis <aristides.gionis@aalto.fi>

Installation

At the command line:

pip install absorbing_centrality

Usage

To use Absorbing Random-Walk Centrality in a project:

import absorbing_centrality

Reference

Computing the centrality of a set

absorbing_centrality(G, team[, query, P, ...]) Compute the absorbing centrality of a team.
absorbing_centrality_inversion(G, team[, ...]) Compute the absorbing centrality of a team using a fast inversion with SuperLU solver.

Team-selection algorithms

greedy_team(G, k[, query, candidates, ...]) Selects a team of nodes according to the greedy algorithm.

Preprocessing the graph

canonical_relabel_nodes(G) Relabels the nodes in the graph, such that the new names belong in the set [1,n].
is_canonical(G) Tests if the graph has been canonicalized.
add_supernode(G[, query]) Adds a supernode to the graph and connects it with directed edges to the query nodes.
has_supernode(G) Checks if there exist a supernode in the graph.

Exceptions

CanonicalizationError(message) Exception related to the graph canonicalization procedure.

Contributing

Contributions are welcome, and they are greatly appreciated! Every little bit helps, and credit will always be given.

Bug reports

When reporting a bug please include:

  • Your operating system name and version.
  • Any details about your local setup that might be helpful in troubleshooting.
  • Detailed steps to reproduce the bug.

Documentation improvements

Absorbing Random-Walk Centrality could always use more documentation, whether as part of the official Absorbing Random-Walk Centrality docs, in docstrings, or even on the web in blog posts, articles, and such.

Feature requests and feedback

The best way to send feedback is to file an issue at https://github.com/harrymvr/absorbing-centrality/issues.

If you are proposing a feature:

  • Explain in detail how it would work.
  • Keep the scope as narrow as possible, to make it easier to implement.
  • Remember that this is a volunteer-driven project, and that contributions are welcome :)

Development

To set up absorbing-centrality for local development:

  1. Fork absorbing-centrality on GitHub.

  2. Clone your fork locally:

    git clone git@github.com:your_name_here/absorbing-centrality.git

  3. Create a branch for local development:

    git checkout -b name-of-your-bugfix-or-feature

    Now you can make your changes locally.

  4. When you’re done making changes, run all the checks, doc builder and spell checker with tox one command:

    tox

  5. Commit your changes and push your branch to GitHub:

    git add .
git commit -m "Your detailed description of your changes."
git push origin name-of-your-bugfix-or-feature

  6. Submit a pull request through the GitHub website.

Pull Request Guidelines

If you need some code review or feedback while you’re developing the code just make the pull request.

For merging, you should:

  1. Include passing tests (run tox) [1].
  2. Update documentation when there’s new API, functionality etc.
  3. Add a note to CHANGELOG.rst about the changes.
  4. Add yourself to AUTHORS.rst.
[1]

If you don’t have all the necessary python versions available locally you can rely on Travis - it will run the tests for each change you add in the pull request.

It will be slower though ...

Tips

To run a subset of tests:

tox -e envname -- py.test -k test_myfeature

To run all the test environments in parallel (you need to pip install detox):

detox

Authors

Changelog

0.1.0 (2015-08-31)

  • Working version of the package.

Indices and tables