Refefer/propagon

Fast, large scale library for computing rankings and features based on various pairwise and graph algorithms

21
/ 100
Experimental

This tool helps analyze complex relationships within large datasets, such as competitive outcomes or network influence, by treating them as graphs. You provide data on connections or interactions (e.g., game results, website links), and it outputs calculated scores, rankings, or feature representations for each item or person. It's designed for analysts, researchers, or data scientists working with very large networks who need to understand hierarchies, importance, or group similar entities.

No commits in the last 6 months.

Use this if you need to rank entities like sports teams or chess players, identify influential nodes in a network (like important websites), or generate numerical representations (embeddings) of items based on their connections within a very large graph.

Not ideal if your data is small, not structured as a network, or if you need to perform general statistical analysis beyond graph-specific algorithms.

network-analysis competitive-ranking social-network-analysis data-science influence-modeling
No License Stale 6m No Package No Dependents
Maintenance 0 / 25
Adoption 5 / 25
Maturity 8 / 25
Community 8 / 25

How are scores calculated?

Stars

9

Forks

1

Language

Rust

License

Last pushed

Sep 21, 2022

Commits (30d)

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