torch_geometric_signed_directed.data.signed.SSBM

Functions

SSBM(→ Tuple[Tuple[scipy.sparse.spmatrix, ...)	A signed stochastic block model graph generator from the
fill(→ float)	A filling method for the signed stochastic block model graph generator from the

Module Contents

SSBM(n: int, k: int, pin: float, etain: float, pout: float | None = None, size_ratio: float = 2, etaout: float | None = None, values: str = 'ones') → Tuple[Tuple[scipy.sparse.spmatrix, scipy.sparse.spmatrix], numpy.array]

A signed stochastic block model graph generator from the SSSNET: Semi-Supervised Signed Network Clustering paper.

Arg types:

• n (int) - Number of nodes.

• k (int) - Number of communities.

• pin (float) - Sparsity value within communities.

• etain (float) - Noise value within communities.

• pout (float) - Sparsity value between communities.

• etaout (float) - Noise value between communities.

• size_ratio (float) - The communities have number of nodes multiples of each other, with the largest size_ratio times the number of nodes of the smallest.

• values (string) - Edge weight distribution (within community and without sign flip; otherwise weight is negated):

  1. ones: Weights are 1.

  2. "exp": Weights are exponentially distributed, with parameter 1.

  3. "uniform": Weights are uniformly distributed between 0 and 1.

Return types:

• A_p (sp.spmatrix) - A sparse adjacency matrix for the positive part.

• A_n (sp.spmatrix) - A sparse adjacency matrix for the negative part.

• labels (np.array) - Labels.

fill(values: str = 'ones') → float

A filling method for the signed stochastic block model graph generator from the SSSNET: Semi-Supervised Signed Network Clustering” paper. Arg types:

• values (string): Edge weight:

  ‘ones’: Weights are 1.

  ‘exp’: Weights are exponentially distributed, with parameter 1.

  ‘uniform: Weights are uniformly distributed between 0 and 1.

Return types:

• value (float): A filled value.