Module raphtory::algorithms

Implementations of various graph algorithms that can be run on the graph.

The algorithms are grouped into modules based on the type of graph they can be run on.

To run an algorithm simply import the module and call the function.

Examples

use raphtory::algorithms::degree::{average_degree};
use raphtory::db::graph::Graph;
  
 let g = Graph::new(1);
 let vs = vec![
     (1, 1, 2),
     (2, 1, 3),
     (3, 2, 1),
     (4, 3, 2),
     (5, 1, 4),
     (6, 4, 5),
  ];

 for (t, src, dst) in &vs {
   g.add_edge(*t, *src, *dst, &vec![], None);
 };
println!("average_degree: {:?}", average_degree(&g));

Modules

clustering_coefficient

connected_components

degree

Varying degree calculations for the entire graph. The degree of a vertex is the number of edges connected to it.

directed_graph_density

Graph density - measures how dense or sparse a graph is.

generic_taint

hits

local_clustering_coefficient

Local Clustering coefficient - measures the degree to which nodes in a graph tend to cluster together.

local_triangle_count

Local triangle count - calculates the number of triangles (a cycle of length 3) for a node. It measures the local clustering of a graph.

pagerank

reciprocity

Reciprocity - measure of the symmetry of relationships in a graph. This calculates the number of reciprocal connections (edges that go in both directions) in a graph and normalizes it by the total number of edges.

triangle_count

triplet_count

Computes the number of both open and closed triplets within a graph