scirs2_graph::algorithms::community

Function infomap_communities

Source 
pub fn infomap_communities<N, E, Ix>(
    graph: &Graph<N, E, Ix>,
    max_iterations: usize,
    tolerance: f64,
) -> InfomapResult<N>
where
    N: Node + Clone + Hash + Eq,
    E: EdgeWeight + Into<f64> + Copy,
    Ix: IndexType,
Expand description

Infomap algorithm for community detection

The Infomap algorithm uses information theory to find communities that minimize the description length of random walks on the graph. It optimizes the map equation which balances the cost of describing the partition with the cost of describing random walks within and between communities.

§Arguments

  • graph - The undirected graph to analyze
  • max_iterations - Maximum number of optimization iterations
  • tolerance - Convergence tolerance for code length improvement

§Returns

  • An InfomapResult with node assignments, code length, and modularity