pyotc.examples package

Submodules

pyotc.examples.edge_awareness module

networkx graphs for edge awareness example and corresponding costs from Table 1

[Alignment and Comparison of Directed Networks via Transition Couplings of Random Walks](https://arxiv.org/abs/2106.07106)

Figure 4 graphs

• G_1 is the regular octogon

• G_2 is the regular octogon removing 1 edge

• G_3 is uniform edge lengths of the octogon removing 1 edge

pyotc.examples.edge_awareness.euclidean_cost(v1, v2)

pyotc.examples.lollipops module

networkx graphs for lollipop examples given in

[Alignment and Comparison of Directed Networks via Transition Couplings of Random Walks](https://arxiv.org/abs/2106.07106)

Figure 5 graphs

• lollipop_1 is the left graph

• lollipop_2 is the right graph

pyotc.examples.stochastic_block_model module

pyotc.examples.stochastic_block_model.stochastic_block_model(sizes: tuple, probs: ndarray) → ndarray

Generate the adjacency for a stochastic block model SBM from a tuple (length n) of sizes an (nxn) matrix of probabilities.

Parameters:

• sizes (tuple) – tuple of node sizes with length of number of blocks

• probs (np.ndarray) – nxn symmetric matrix

Raises:

• ValueError – If probs is not a square numpy array

• ValueError – If probs is not symmetric

• ValueError – If sizes and probs dimensions do not match

Returns:

adjancency matrix for SBM

Return type:

np.ndarray

pyotc.examples.wheel module

networkx graphs for wheel graph examples given in Version 1 of

[Alignment and Comparison of Directed Networks via Transition Couplings of Random Walks](https://arxiv.org/pdf/2106.07106v1.pdf)

Figure 2 graphs

• G_1 is a wheel graph of order 16

• G_2 is a wheel graph removing 1 spoke edge

• G_3 is a wheel graph removing 1 wheel edge

Module contents