Local clustering
Local clustering - Published in JOSS (2018)
Science Score: 93.0%
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Published in Journal of Open Source Software
Keywords
Repository
Python 3 implementation and documentation of the Hermina-Janos local graph clustering algorithm.
Basic Info
- Host: GitHub
- Owner: volfpeter
- License: agpl-3.0
- Language: Python
- Default Branch: master
- Size: 2.48 MB
Statistics
- Stars: 23
- Watchers: 2
- Forks: 1
- Open Issues: 0
- Releases: 6
Topics
Metadata Files
README.md
LocalClustering
The project implements multiple variations of a local graph clustering algorithm named the Hermina-Janos algorithm in memory of my beloved grandparents.
Graph cluster analysis is used in a wide variety of fields. This project does not target one specific field, instead it aims to be a general tool for graph cluster analysis for cases where global cluster analysis is not applicable or practical for example because of the size of the data set or because a different (local) perspective is required.
The algorithms are independent of the cluster definition. The interface cluster definitions must implement can be found in the definitions package along with a simple connectivity based cluster definition implementation. Besides the algorithms and the cluster definition, other utilities are also provided, most notably a module for node ranking.
Installation
Install the latest version of the project from the Python Package Index using pip install localclustering.
Getting started
This section will guide you through the basics using SQLAlchemy and the IGraphWrapper graph implementation from graphscraper. IGraphWrapper requires the igraph project to be installed. You can do this by following the instructions on this page.
Once everything is in place, the analyzed graph can be created:
```Python import igraph from graphscraper.igraphwrapper import IGraphWrapper
graph = IGraphWrapper(igraph.Graph.Famous("Zachary")) ```
The next step is the creation of the cluster definition and the preparation of the clustering algorithm:
```Python from localclustering.definitions.connectivity import ConnectivityClusterDefinition from localclustering.localengine import LocalClusterEngine
clusterdefinition = ConnectivityClusterDefinition(1.5, 0.85) localclusterengine = LocalClusterEngine( clusterdefinition, # The cluster definition the algorithm should use. sourcenodesinresult=True, # Ensure that source nodes are not removed from the cluster. maxcluster_size=34 # Specify an upper limit for the calculated cluster's size. ) ```
Now the source node of the clustering must be retrieved:
Python
source_node = graph.nodes.get_node_by_name("2", can_validate_and_load=True)
And finally the cluster analysis can be executed:
Python
cluster = local_cluster_engine.cluster([source_node])
Additionally you can list the nodes inside the cluster with their rank to get an overview of the result:
Python
rank_provider = local_cluster_engine.get_rank_provider()
for node in cluster.nodes:
print(node.igraph_index, rank_provider.get_node_rank(node))
Additional resources
In addition to the software, a detailed description and an in-depth evaluation of the algorithms is also provided.
Furthermore, a demo module showing the basic usage of the project is also available.
Related projects
You can find related projects here:
Community guidelines
Any form of constructive contribution is welcome:
- Questions, feedback, bug reports: please open an issue in the issue tracker of the project or contact the repository owner by email, whichever you feel appropriate.
- Contribution to the software: please open an issue in the issue tracker of the project that describes the changes you would like to make to the software and open a pull request with the changes. The description of the pull request must references the corresponding issue.
The following types of contribution are especially appreciated:
- Implementation of new cluster definitions.
- Result comparison with global clustering algorithms on well-known and -analyzed graphs.
- Analysis of how cluster definitions should be configured for graphs with different characteristics.
- Analysis of how the weighting coefficients of the connectivity based cluster definition corresponding to the different hierarchy levels relate to each-other in different real-world graphs.
License - GNU AGPLv3
The library is open-sourced under the conditions of the GNU Affero General Public License v3.0, which is the strongest copyleft license. The reason for using this license is that this library is the "publication" of the Hermina-Janos algorithm and it should be referenced accordingly.
Owner
- Name: Peter Volf
- Login: volfpeter
- Kind: user
- Website: https://volfp.com
- Twitter: volfpeter
- Repositories: 11
- Profile: https://github.com/volfpeter
Python & TypeScript
JOSS Publication
Local clustering
Authors
Independent developer
Editor
Pjotr Prins
Tags
graph theory clustering algorithm rankingGitHub Events
Total
- Watch event: 4
Last Year
- Watch event: 4
Committers
Last synced: 7 months ago
Top Committers
| Name | Commits | |
|---|---|---|
| Péter Volf | p****r@m****m | 35 |
| DannyArends | D****s@G****m | 8 |
| volfpeter | d****p@g****m | 3 |
| Arfon Smith | a****n | 1 |
Committer Domains (Top 20 + Academic)
Issues and Pull Requests
Last synced: 6 months ago
All Time
- Total issues: 0
- Total pull requests: 2
- Average time to close issues: N/A
- Average time to close pull requests: 17 minutes
- Total issue authors: 0
- Total pull request authors: 2
- Average comments per issue: 0
- Average comments per pull request: 0.5
- Merged pull requests: 2
- Bot issues: 0
- Bot pull requests: 0
Past Year
- Issues: 0
- Pull requests: 0
- Average time to close issues: N/A
- Average time to close pull requests: N/A
- Issue authors: 0
- Pull request authors: 0
- Average comments per issue: 0
- Average comments per pull request: 0
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 0
Top Authors
Issue Authors
Pull Request Authors
- DannyArends (1)
- arfon (1)
Top Labels
Issue Labels
Pull Request Labels
Packages
- Total packages: 1
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Total downloads:
- pypi 43 last-month
- Total dependent packages: 0
- Total dependent repositories: 1
- Total versions: 5
- Total maintainers: 1
pypi.org: localclustering
Python 3 implementation and documentation of the Hermina-Janos local graph clustering algorithm.
- Homepage: https://github.com/volfpeter/localclustering
- Documentation: https://localclustering.readthedocs.io/
- License: AGPLv3+
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Latest release: 0.13.0
published about 6 years ago
Rankings
Maintainers (1)
Dependencies
- graphscraper >=0.5