Module algorithms 

Hypergraph algorithms.

Implements:

• Spectral clustering via the normalised Laplacian (Zhou et al. 2006).
• Hyperedge cuts: raw cut, ratio cut, normalised cut.
• Generalised random walk (Markov chain on nodes through hyperedges).
• Hypergraph betweenness centrality based on shortest paths through the clique-expansion graph.
• s-walks and s-paths: walks/paths between hyperedges that share ≥ s nodes.

Structs

CutResult

Result of a hyperedge cut computation.

SpectralClusteringResult

Result of hypergraph spectral clustering.

Functions

betweenness_centrality

Compute hypergraph betweenness centrality for every node.

hyperedge_cut

Compute the hyperedge cut, ratio cut, and normalised cut for a binary partition of the node set.

s_betweenness_centrality

Hyperedge betweenness centrality in the s-line graph.

s_diameter

Compute the s-diameter of the hypergraph: the maximum s-distance over all pairs of hyperedges in the same s-connected component.

s_distance

Compute the s-distance between two hyperedges: the length of the shortest s-path (sequence of hyperedges each sharing ≥ s nodes with the next).

s_reachability

Enumerate all s-paths of length ≤ max_len starting from hyperedge start as BFS layers.

spectral_clustering

Perform spectral clustering on a hypergraph using the normalised Laplacian (Zhou et al. NeurIPS 2006).

stationary_distribution

Compute the stationary distribution of the generalised random walk on a hypergraph, using power iteration on the transition matrix.