cdlib | Community Discovery Library | Data Visualization library

by GiulioRossetti Python Version: 0.4.0 License: BSD-2-Clause

X-Ray Key Features Code Snippets Community Discussions(2)Vulnerabilities Install Support

kandi X-RAY | cdlib Summary

cdlib is a Python library typically used in Analytics, Data Visualization applications. cdlib has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can install using 'pip install cdlib' or download it from GitHub, PyPI.

CDlib is a meta-library for community discovery in complex networks: it implements algorithms, clustering fitness functions as well as visualization facilities. CDlib is designed around the networkx python library: however, when needed, it takes care to automatically convert (from and to) igraph object so to provide an abstraction on specific algorithm implementations to the final user. CDlib provides a standardized input/output facilities for several Community Discovery algorithms: whenever possible, to guarantee literature coherent results, implementations of CD algorithms are inherited from their original projects (acknowledged on the documentation).

Support

Quality

Security

License

Reuse

Support

cdlib has a low active ecosystem.

It has 306 star(s) with 68 fork(s). There are 6 watchers for this library.

There were 1 major release(s) in the last 12 months.

There are 15 open issues and 100 have been closed. On average issues are closed in 210 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of cdlib is 0.4.0

Quality

cdlib has 0 bugs and 0 code smells.

Security

cdlib has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

cdlib code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

cdlib is licensed under the BSD-2-Clause License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

cdlib releases are available to install and integrate.

Deployable package is available in PyPI.

Build file is available. You can build the component from source.

Installation instructions, examples and code snippets are available.

cdlib saves you 2639 person hours of effort in developing the same functionality from scratch.

It has 5726 lines of code, 498 functions and 68 files.

It has high code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi has reviewed cdlib and discovered the below as its top functions. This is intended to give you an instant insight into cdlib implemented functionality, and help decide if they suit your requirements.

Create a XMark benchmark
Generate the communities
Assign random labels
Assign random labels to each community
Compute the paris of a graph
Rank - clustering
Select the k - th clustering
Convert a networkx format to a networkx graph
Return a node clustering
Generate a node clustering
Simulate infomap
Builds a node clustering
Construct a node clustering
Adjust a random rand score based on two partitions
Find all the communities of a graph
Construct a NodeCluster
Adjusts the mutual information between two partitions
Siblin parity algorithm
Execute the algorithm
Performs infomapping on the network
Perform a GEMEC clustering
Create a DynGraph object based on the density of communities
Construct a BiNode clustering
Perform node clustering
Build a node clustering
Run Markov clustering

Get all kandi verified functions for this library.

cdlib Key Features

No Key Features are available at this moment for cdlib.

cdlib Examples and Code Snippets

No Code Snippets are available at this moment for cdlib.

Community Discussions

Trending Discussions on cdlib

How to install optional components (anaconda, jupyter) in custom dataproc image

Optional dependency not satisfied: install igraph and leidenalg to use the selected feature

QUESTION

How to install optional components (anaconda, jupyter) in custom dataproc image

Asked 2021-May-03 at 20:41

To speed up my cluster instantiation time, I've created a custom image with all the additional dependencies installed using miniconda3 available for dataproc image 1.5.34-debian10. (I followed the steps here: GCP Dataproc custom image Python environment to ensure I used the correct python environment).

However, when I start my cluster with --optional-components ANACONDA,JUPYTER my custom dependencies are removed and I'm left with a base installation of anaconda and jupyter. I assume the anaconda installation is overwriting my custom dependencies. Is there any way to ensure my dependencies aren't overwritten? If not, is it possible to install anaconda and jupyter as part of my custom dataproc image instead?

I've used the following command to create the custom image:

...

ANSWER

Answered 2021-May-03 at 20:41

The customize_conda.sh script is the recommended way of customizing Conda env for custom images.

If you need more than the script does, you can read the code and create your own script, but anyway you want to use the absolute path e.g., /opt/conda/anaconda/bin/conda, /opt/conda/anaconda/bin/pip, /opt/conda/miniconda3/bin/conda, /opt/conda/miniconda3/bin/pip to install/uninstall packages for the Anaconda/Miniconda env.

Source https://stackoverflow.com/questions/67357670

QUESTION

Optional dependency not satisfied: install igraph and leidenalg to use the selected feature

Asked 2020-Dec-08 at 21:12

I was trying to run the following example from the cdlib documentation:

...

ANSWER

Answered 2020-Dec-07 at 14:36

Using:

Source https://stackoverflow.com/questions/65181176

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install cdlib

To install the latest version of our library just download (or clone) the current project, open a terminal and run the following commands:.

Support

CDlib is a meta-library for community discovery in complex networks: it implements algorithms, clustering fitness functions as well as visualization facilities. CDlib is designed around the networkx python library: however, when needed, it takes care to automatically convert (from and to) igraph object so to provide an abstraction on specific algorithm implementations to the final user. CDlib provides a standardized input/output facilities for several Community Discovery algorithms: whenever possible, to guarantee literature coherent results, implementations of CD algorithms are inherited from their original projects (acknowledged on the documentation).

Find more information at: