Cliques modules

The cliques module

Cliques class.

class hcga.features.cliques.Cliques(graph=None)

Cliques class.

Here we construct features based on cliques (subsets of vertices, all adjacent to each other, also called complete subgraphs).

References

1

Bron, C. and Kerbosch, J. “Algorithm 457: finding all cliques of an undirected graph”. Communications of the ACM 16, 9 (Sep. 1973), 575–577. <http://portal.acm.org/citation.cfm?doid=362342.362367>

2

F. Cazals, C. Karande, “A note on the problem of reporting maximal cliques”, Theoretical Computer Science, Volume 407, Issues 1–3, 6 November 2008, Pages 564–568, <https://doi.org/10.1016/j.tcs.2008.05.010>

3

Yun Zhang, Abu-Khzam, F.N., Baldwin, N.E., Chesler, E.J., Langston, M.A., Samatova, N.F., “Genome-Scale Computational Approaches to Memory-Intensive Applications in Systems Biology”. Supercomputing, 2005. Proceedings of the ACM/IEEE SC 2005 Conference, pp. 12, 12–18 Nov. 2005. <https://doi.org/10.1109/SC.2005.29>.

Initialise a feature class.

Parameters

graph (Graph) – graph for initialisation, converted to given encoding

compute_features()

Main feature extraction function.

This function should be used by each specific feature class to add new features.

hcga.features.cliques.clique_sizes(graph)

hcga.features.cliques.eval_cliques(graph)

this evaluates the main function and cach it for speed up.

hcga.features.cliques.maximal_clique_sizes(graph)

hcga.features.cliques.n_cliques(graph)