Working with graphs in Julia
This is an archived copy of the old Graphs.jl package, which is retained solely for the issues and pull-requests. The new repository for Graphs.jl is https://github.com/JuliaGraphs/Graphs.jl, which includes all the code from this repo along with the development history of LightGraphs, which was later renamed to Graphs and merged into that combined repository. As the name "Graphs" suggests, this was the original graph package in Julia. By 2016, however, it had become largely unmaintained. Another graphs package named LightGraphs, focused on a slightly different set of use cases, eventually became the de facto standard graphs package in the Julia ecosystem. In 2021, the primary author of LightGraphs departed and the name was retired at their request. At that point, the code base of LightGraphs was grafted back into a forked copy of the Graphs repository, which now lives in the JuliaGraphs organization, wholesale replacing the content of this archived repository. So Graphs was the original graph library; then LightGraphs became the standard; then LightGraphs was renamed to Graphs and replaced the code of the old Graphs, which is what you're looking at here. Yes, it's all a bit confusing—sorry about that. What follows is the README of the original Graphs package.
Graphs.jl is a Julia package that provides graph types and algorithms. The design of this package is inspired by the Boost Graph Library (e.g. using standardized generic interfaces), while taking advantage of Julia's language features (e.g. multiple dispatch). This library allows storing of own information in the graph structure -- useful in many cases.
An important aspect of Graphs.jl is the generic abstraction of graph concepts expressed via standardized interfaces, which allows access to a graph's structure while hiding the implementation details. This encourages reuse of data structures and algorithms. In particular, one can write generic graph algorithms that can be applied to different graph types as long as they implement the required interface.
In addition to the generic abstraction, there are other important features:
A variety of graph types tailored to different purposes
A collection of graph algorithms:
Matrix-based characterization: adjacency matrix, weight matrix, Laplacian matrix
All data structures and algorithms are implemented in pure Julia, and thus they are portable.
We paid special attention to the runtime performance. Many of the algorithms are very efficient. For example, a benchmark shows that it takes about 15 milliseconds to run the Dijkstra's algorithm over a graph with 10 thousand vertices and 1 million edges on a macbook pro.
Please refer to Graphs.jl Documentation for latest documentation.