HiGCN | hierarchical graph convolution network for representation

by SCUT-CCNL Python Version: Current License: No License

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

kandi X-RAY | HiGCN Summary

HiGCN is a Python library. HiGCN has no bugs, it has no vulnerabilities and it has low support. However HiGCN build file is not available. You can download it from GitHub.

HiGCN: a hierarchical graph convolution network for representation learning of gene expression data. CONTACT: For questions or comments about the code please contact: kwtan0909@qq.com / cskwtan93@mail.scut.edu.cn / sbdong@scut.edu.cn.

Support

Quality

Security

License

Reuse

Support

HiGCN has a low active ecosystem.

It has 9 star(s) with 2 fork(s). There are 1 watchers for this library.

It had no major release in the last 6 months.

HiGCN has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of HiGCN is current.

Quality

HiGCN has no bugs reported.

Security

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

License

HiGCN does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

Without a license, all rights are reserved, and you cannot use the library in your applications.

Reuse

HiGCN releases are not available. You will need to build from source code and install.

HiGCN has no build file. You will be need to create the build yourself to build the component from source.

Top functions reviewed by kandi - BETA

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

Calculate affinity matrix
Load data from a given dataset
Calculate the affinity matrix
Splits data into train indices
Splits training and test and test
Random permutation
Function to plot feature weight
Plot feature weight
Train model
Negative log likelihood
Calculate the R matrix
Evaluate the model
R Normalize an adjacian matrix
Normalize adjacency matrix

Get all kandi verified functions for this library.

HiGCN Key Features

No Key Features are available at this moment for HiGCN.

HiGCN Examples and Code Snippets

No Code Snippets are available at this moment for HiGCN.

Community Discussions

No Community Discussions are available at this moment for HiGCN.Refer to stack overflow page for discussions.

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

Vulnerabilities

No vulnerabilities reported

Install HiGCN

You can download it from GitHub.
You can use HiGCN like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

Find more information at: