xgfs/verse

Reference implementation of the paper VERSE: Versatile Graph Embeddings from Similarity Measures

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Emerging

This tool helps you analyze complex network data, like social networks, biological interactions, or citation graphs, by converting them into a format that machine learning models can easily understand. You input your existing graph data, such as a list of connections or an adjacency matrix, and it outputs a set of numerical representations (embeddings) for each item in your network. Data scientists, researchers, and network analysts can use these embeddings for tasks like predicting missing links, classifying nodes, or recommending items.

134 stars. No commits in the last 6 months.

Use this if you need to transform your graph or network data into a numerical vector format to apply machine learning techniques for tasks like classification or similarity analysis.

Not ideal if you are looking for a visual graph exploration tool or need to perform real-time, high-frequency updates to your graph embeddings.

network-analysis graph-data-science social-network-analysis bioinformatics recommender-systems
Stale 6m No Package No Dependents
Maintenance 0 / 25
Adoption 10 / 25
Maturity 16 / 25
Community 17 / 25

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Stars

134

Forks

22

Language

C++

License

MIT

Last pushed

Feb 21, 2021

Commits (30d)

0

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