Graph-Embedding | Four network embedding algorithms | Artificial Intelligence library

by dedekinds Python Version: Current License: No License

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

kandi X-RAY | Graph-Embedding Summary

Graph-Embedding is a Python library typically used in Artificial Intelligence applications. Graph-Embedding has no bugs, it has no vulnerabilities and it has low support. However Graph-Embedding build file is not available. You can download it from GitHub.

Four network embedding algorithms(deepwalk, node2vec, TADW ,LINE) for two datasets(Cora, Tencent Weibo)

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Quality

Security

License

Reuse

Support

Graph-Embedding has a low active ecosystem.

It has 75 star(s) with 24 fork(s). There are 2 watchers for this library.

It had no major release in the last 6 months.

There are 2 open issues and 0 have been closed. On average issues are closed in 754 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of Graph-Embedding is current.

Quality

Graph-Embedding has 0 bugs and 0 code smells.

Security

Graph-Embedding has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

Graph-Embedding code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

Graph-Embedding 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

Graph-Embedding releases are not available. You will need to build from source code and install.

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

Installation instructions are not available. Examples and code snippets are available.

It has 604 lines of code, 23 functions and 5 files.

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

Top functions reviewed by kandi - BETA

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

Linear network
Batch generation function
Create a tensorflow model
Preprocess the transition probability of a transition graph
Given a list of probabilities compute the nodes of the alias
Get edges from source to dest
Load a cora data file
Encode one - hot labels into one - hot encoding
Solve the solve method
Loads a graph from a file
Load features from file
Calculate the split of the dataset

Get all kandi verified functions for this library.

Graph-Embedding Key Features

No Key Features are available at this moment for Graph-Embedding.

Graph-Embedding Examples and Code Snippets

No Code Snippets are available at this moment for Graph-Embedding.

Community Discussions

Trending Discussions on Graph-Embedding

Is the neo4j documentation inconsistent regarding embedding parameter?

Use tensorflow's .ckpt model

QUESTION

Is the neo4j documentation inconsistent regarding embedding parameter?

Asked 2021-May-12 at 13:31

In this tutorial, it has the following example: https://neo4j.com/developer/graph-data-science/applied-graph-embeddings/ where 'embeddingSize' is used for specify the vector length of the embedding.

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ANSWER

Answered 2021-May-12 at 13:31

Graph embeddings were introduced in version 1.3 and the tutorial you found is for that version and it uses embeddingSize. Then 2nd link you found is the recent documentation for node2Vec and it is meant for >= 1.4 version. Look at the header of your 2nd link and you will see below

Source https://stackoverflow.com/questions/67497553

QUESTION

Use tensorflow's .ckpt model

Asked 2020-Sep-10 at 11:03

I used this code for training a model. I now have 3 files:

model.ckpt-1.meta
model.ckpt-1.index
model.ckpt-1.data-00000-of-00001

How (with what methods) can I use these models now?

...

ANSWER

Answered 2020-Sep-10 at 11:03

I'm not exactly sure what you mean with

How (with what methods) can I use these models now?

The model is not saved in those files but i can be restored with them.

Those*.ckpt get saved during training but do not contain your model. If you want to "use" your model you need to restore those files to it. Take a look at Tensorflow's Checkpoint and CheckpointManager. This tutorial shows a simple snipped of how to restore .ckpt files to your model.

Source https://stackoverflow.com/questions/63823994

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

Vulnerabilities

No vulnerabilities reported

Install Graph-Embedding

You can download it from GitHub.
You can use Graph-Embedding 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 .

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