torch_geometric_signed_directed.data.signed.polarized_SSBM

Functions

polarized_SSBM(→ Tuple[Tuple[scipy.sparse.spmatrix, ...)

A polarized signed stochastic block model graph generator from the

Module Contents

polarized_SSBM(total_n: int = 100, num_com: int = 3, N: int = 30, K: int = 2, p: float = 0.1, eta: float = 0.1, size_ratio: float = 1) → Tuple[Tuple[scipy.sparse.spmatrix, scipy.sparse.spmatrix], numpy.array, numpy.array]

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

Arg types:

  • total_n (int) - Total number of nodes in the polarized network.

  • num_com (int) - Number of conflicting communities.

  • N (int) - Default size of an SSBM community.

  • K (int) - Number of blocks(clusters) within a conflicting community.

  • p (int) - Probability of existence of an edge.

  • eta (float) - Sign flip probability, 0 <= eta <= 0.5.

  • 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.

Return types:

  • A_p_new, A_n_new (sp.spmatrix) - Positive and negative parts of the polarized network.

  • labels_new (np.array) - Ordered labels of the nodes, with conflicting communities labeled together, cluster 0 is the ambient cluster.

  • conflict_groups (np.array) - An array indicating which conflicting group the node is in, 0 is ambient.