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
- generic adjacency list
- generic incidence list
- a simple graph type with compact and efficient representation
- an extended graph type that supports labels and attributes
- A collection of graph algorithms:
- graph traversal with visitor support: BFS, DFS
- cycle detection
- connected components
- topological sorting
- shortest paths: Dijkstra, Floyd-Warshall
- minimum spanning trees: Prim, Kruskal
- more algorithms are being implemented
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.