DGCNN | Please cite our paper entiles | Machine Learning library

by nguyenlab Python Version: Current License: No License

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

kandi X-RAY | DGCNN Summary

DGCNN is a Python library typically used in Telecommunications, Media, Advertising, Marketing, Artificial Intelligence, Machine Learning, Deep Learning, Tensorflow applications. DGCNN has no bugs, it has no vulnerabilities and it has low support. However DGCNN build file is not available. You can download it from GitHub.

Please cite our paper entiles "DGCNN: A convolutional neural network over large-scale labeled graphs" published in Neural Network 2018, if you used in your research.

Support

Quality

Security

License

Reuse

Support

DGCNN has a low active ecosystem.

It has 16 star(s) with 8 fork(s). There are no watchers for this library.

It had no major release in the last 6 months.

There are 2 open issues and 1 have been closed. On average issues are closed in 6 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of DGCNN is current.

Quality

DGCNN has 0 bugs and 0 code smells.

Security

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

DGCNN code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

DGCNN 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

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

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

DGCNN saves you 6484 person hours of effort in developing the same functionality from scratch.

It has 13479 lines of code, 795 functions and 99 files.

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

Top functions reviewed by kandi - BETA

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

Parse the grammar rules .
Run the parser .
Construct a tree convolution layer .
Build the parser table .
Lex the module .
Parse the grammar .
Reads the training data from the Xy file .
Serialize layers to a file .
Write a network layer .
Validate rules .

Get all kandi verified functions for this library.

DGCNN Key Features

No Key Features are available at this moment for DGCNN.

DGCNN Examples and Code Snippets

No Code Snippets are available at this moment for DGCNN.

Community Discussions

Trending Discussions on DGCNN

Stellargraph failing to work with data shuffle

tf.gather with indices of higher dimention than input data?

QUESTION

Stellargraph failing to work with data shuffle

Asked 2021-Jun-08 at 16:23

when I ran the StellarGraph's demo on graph classification using DGCNNs, I got the same result as in the demo.

However, when I tested what happens when I first shuffle the data using the following code:

...

ANSWER

Answered 2021-Jun-08 at 16:23

I found the problem. It wasn't anything to do with the shuffling algorithm, nor with StellarGraph's implementation. The problem was in the demo, at the following lines:

Source https://stackoverflow.com/questions/67779173

QUESTION

tf.gather with indices of higher dimention than input data?

Asked 2018-Dec-02 at 21:09

Reading Dynamic Graph CNN for Learning on Point Clouds code, I came across this snippet:

...

ANSWER

Answered 2018-Dec-02 at 21:09

An equivalent function in numpy is np.take, a simple example:

Source https://stackoverflow.com/questions/53578484

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

Vulnerabilities

No vulnerabilities reported

Install DGCNN

You can download it from GitHub.
You can use DGCNN 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: