navis.models.network_models.BayesianTraversalModel

class navis.models.network_models.BayesianTraversalModel(*args, traversal_func=None, **kwargs)

Model for traversing a network starting with given seed nodes.

This model is a Bayes net version of TraversalModel that propagates traversal probabilities through the network and converges to a distribution of time of traversal for each node, rather than stochastically sampling.

Unlike TraversalModel, this model should only be run once. Note alse that traversal_func should be a function returing probabilities of traversal, rather than a random boolean of traversal.

Parameters:

• edges (pandas.DataFrame) – DataFrame representing an edge list. Must minimally have a source and target column.

• seeds (iterable) – Seed nodes for traversal. Nodes that aren’t found in edges['source'] will be (silently) removed.

• weights (str, optional) – Name of a column in edges used as weights. If not provided, all edges will be given a weight of 1. If using the default activation function the weights need to be between 0 and 1.

• max_steps (int) – Limits the number of steps for each iteration.

• traversal_func (callable, optional) – Function returning probability whether a given edge will be traversed or not in a given step. Must take numpy array (N, 1) of edge weights and return an array with probabilities of equal size. Defaults to linear_activation_p() which will linearly scale probability of traversal from 0 to 100% between edges weights 0 to 0.3.

Examples

>>> from navis.models import BayesianTraversalModel
>>> import networkx as nx
>>> import numpy as np
>>> # Generate a random graph
>>> G = nx.fast_gnp_random_graph(1000, .2, directed=True)
>>> # Turn into edge list
>>> edges = nx.to_pandas_edgelist(G)
>>> # Add random edge weights
>>> edges['weight'] = np.random.random(edges.shape[0])
>>> # Initialize model
>>> model = BayesianTraversalModel(edges, seeds=list(G.nodes)[:10])
>>> # Run model
>>> res = model.run()
>>> # Get a summary
>>> model.summary.tail()                                    
      layer_min  layer_max  layer_mean  layer_median
node
995           2          2        2.00             2
996           2          3        2.33             2
997           2          2        2.00             2
998           2          2        2.00             2
999           2          2        2.00             2

Above Graph was traversed quickly (3 steps max). Let’s adjust the traversal function:

>>> from navis.models import linear_activation_p
>>> # Use a lower probability for activation
>>> def my_act(x):
...     return linear_activation_p(x, max_w=10)
>>> model = BayesianTraversalModel(edges, seeds=list(G.nodes)[:10],
...                                traversal_func=my_act)
>>> res = model.run()
>>> res.tail()                                              
      layer_min  layer_max  layer_mean  layer_median
node
995           2          4       3.210           3.0
996           2          4       3.280           3.0
997           2          4       3.260           3.0
998           2          4       3.320           3.0
999           2          4       3.195           3.0

Initialize model.

__init__(*args, traversal_func=None, **kwargs)

Initialize model.

Methods

__init__(*args[, traversal_func])	Initialize model.
initializer()
make_summary()	Generate summary.
run(**kwargs)	Run model (single process).
run_parallel(*args, **kwargs)	Run model using parallel processes.

Attributes

has_results	Check if model has results.
n_nodes	Return unique nodes in network.
summary	Per-node summary.