Rust library for random graph ensembles

Implements simple sampling and monte carlo (or rather markov-) steps, that can be used to create a markov chain.

This is intended to be used for various different use cases. As such, you can easily define additional data that should be stored at each vertex.

For the whole graph:

• you can always visualize the current graph by creating a .dot file from it. There are different options for that, choose which one fits you best.

Implements measurable quantities

• average degree
• connected components
• diameter
• is_connected
• leaf count
• q_core
• transitivity
• biconnected component
• vertex_load (closely related, often equal to betweeness)

Iterators

• depth first search from index
• breadth first search from index

For each vertex

methods and more

• degree
• check adjacency with other nodes
• access additional data

Iterators

• iterate over indices stored in adjacency list

Documentation:

• current working branch
• v0.1.0

Notes

No warranties whatsoever, but since I am writing this library for my own scientific simulations, I do my best to avoid errors.

You can learn more about me and my research on my homepage.

If you like my library but feel like there is an iterator missing or something like that: feel free to create an issue on the repository, I might add it.

currently implemented network ensembles

• Erdős-Rényi (x2)
• small-world

vertices

• The number of vertices has to be decided when creating a graph and cannot be changed later - at least for now.
• I might add a method to add vertices if requested or I need it myself.

Due to implementation details, where I prioritize fast access of vertices, it is unlikely, that I will implement the option to remove vertices. If I do, it will likely be a relatively costly operation, so keep that in mind.

crates.io

• I might move the Ensemble trait into a different crate in the Future. If I do, the trait will be reexported at the same position as currently

License

Licensed under either of

• Apache License, Version 2.0 (LICENSE-APACHE or http://www.apache.org/licenses/LICENSE-2.0)
• MIT license (LICENSE-MIT or http://opensource.org/licenses/MIT)

at your option.

Contribution

Unless you explicitly state otherwise, any contribution intentionally submitted for inclusion in the work by you, as defined in the Apache-2.0 license, shall be dual licensed as above, without any additional terms or conditions.