pyflagser
.flagser_unweighted¶

pyflagser.
flagser_unweighted
(adjacency_matrix, min_dimension=0, max_dimension=inf, directed=True, coeff=2, approximation=None)¶ Compute homology of a directed/undirected flag complex.
From an adjacency_matrix construct all cells forming its associated flag complex and compute its homology.
 Parameters
 adjacency_matrix2d ndarray or scipy.sparse matrix, required
Adjacency matrix of a directed/undirected unweighted graph. It is understood as a boolean matrix. Offdiagonal,
0
orFalse
values denote absent edges while non0
orTrue
values denote edges which are present. Diagonal values are ignored. min_dimensionint, optional, default:
0
Minimum homology dimension to compute.
 max_dimensionint or np.inf, optional, default:
np.inf
Maximum homology dimension to compute.
 directedbool, optional, default:
True
If
True
, computes homology for the directed flag complex determined by adjacency_matrix. IfFalse
, computes homology for the undirected flag complex obtained by considering all edges as undirected, and it is therefore sufficient (but not necessary) to pass an uppertriangular matrix. coeffint, optional, default:
2
Compute homology with coefficients in the prime field \(\mathbb{F}_p = \{ 0, \ldots, p  1 \}\) where \(p\) equals coeff.
 approximationint or None, optional, default:
None
Skip all cells creating columns in the reduction matrix with more than this number of entries. Use this for hard problems; a good value is often
100,000
. Increase for higher precision, decrease for faster computation. IfNone
, no approximation is made and all cells are used. For more details, please refer to [1].
 Returns
 outdict of list
A dictionary with the following keyvalue pairs:
'betti'
: list of int Betti numbers, per dimension greater than or equal than min_dimension and less than max_dimension.'cell_count'
: list of int Cell counts (number of simplices), per dimension greater than or equal to min_dimension and less than max_dimension.'euler'
: int Euler characteristic.
Notes
The input graphs cannot contain selfloops, i.e. edges that start and end in the same vertex, therefore diagonal elements of the input adjacency matrix will be ignored.
References
 1
D. Luetgehetmann, “Documentation of the C++ flagser library”; GitHub: luetge/flagser.