Source code for netrd.dynamics.single_unbiased_random_walker


Simulate a lonely walker on a network.

from .base import BaseDynamics
import networkx as nx
import numpy as np

[docs]class SingleUnbiasedRandomWalker(BaseDynamics): """Random walk dynamics."""
[docs] def simulate(self, G, L, initial_node=None): r"""Simulate single random-walker dynamics on a ground truth network. Generates an :math:`N \times L` time series `TS` with ``TS[j,t]==1`` if the walker is at node :math:`j` at time :math:`t`, and ``TS[j,t]==0`` otherwise. The results dictionary also stores the ground truth network as `'ground_truth'`. Examples -------- .. code:: python G = nx.ring_of_cliques(4,16) L = 2001 dynamics = SingleUnbiasedRandomWalker() TS = dynamics.simulate(G, L) Parameters ---------- G (nx.Graph) The input (ground-truth) graph with :math:`N` nodes. L (int) The length of the desired time series. Returns ------- TS (np.ndarray) An :math:`N \times L` array of synthetic time series data. """ # get adjacency matrix and set up vector of indices A = nx.to_numpy_array(G) N = G.number_of_nodes() W = np.zeros(L, dtype=int) # place walker at initial location if initial_node: W[0] = initial_node else: W[0] = np.random.randint(N) # run dynamical process for t in range(L - 1): W[t + 1] = np.random.choice(np.where(A[W[t], :])[0]) self.results['node_index_sequence'] = W # turn into a binary-valued TS = np.zeros((N, L)) for t, w in enumerate(W): TS[w, t] = 1 self.results['TS'] = TS self.results['ground_truth'] = G return TS