def compute_states_per_time(HG, t_max, t_per_walker):
markov_matrix = create_markov_matrix(HG.hyper_edges())
engine = DiffusionEngine(markov_matrix, t_per_walker=t_per_walker)
most_common, states = engine.simulate(t_max)
plt.show()
states_per_time = list(zip(*states))
return states_per_time
def simulate_diffusion(nodes, edges, markov_matrix, t_max):
"""Simulate diffusion using diffusion engine.
Returns:
most_common: hyper_edges to their probabilities of occurrences
most_common_nodes: similar to above, but with nodes
states: list of states which were visited by workers
"""
engine = DiffusionEngine(markov_matrix, t_per_walker=100)
most_common, states = engine.simulate(t_max)
most_common_nodes = count_nodes(nodes, edges, most_common)
return most_common, most_common_nodes, states
def test_diffusion_engine():
hyper_graph = generators.generic_hypergraph(10, ((3, 2), (4, 3), (5, 3)))
t_max = 1000
number_of_walkers = 1
offset = 10
markov_matrix = create_markov_matrix_model_nodes(hyper_graph)
chosen_states = []
for x in range(10, t_max, offset):
engine = DiffusionEngine(markov_matrix)
frequencies, states = engine.simulate(offset)
chosen_states += states[0]
nt.assert_equals(len(chosen_states), t_max / number_of_walkers - 10)
def compare_hypergraph_with_cliques(number_of_nodes,
cardinality, fraction, t_max,
plot_representations=False):
"""Create hypergraph and clique, run diffusion and compare"""
HG = uniform_hypergraph(
n=number_of_nodes,
k=cardinality,
number_of_edges=int(
number_of_nodes *
fraction))
nodes = HG.nodes()
hyperedges = HG.hyper_edges()
all_nodes = []
for hyperedge in hyperedges:
all_nodes += hyperedge
if plot_representations:
utils.plot_different_representations(nodes, hyperedges)
markov_matrix = create_markov_matrix(hyperedges)
print(markov_matrix)
engine = DiffusionEngine(markov_matrix)
most_common, states = engine.simulate(t_max)
plt.figure(figsize=(12, 10))
utils.plot_hyperedges_frequencies(most_common, hyperedges,
'Occurrences of hyperedges'
' in a hypergraph')
most_common_nodes = count_nodes(nodes, hyperedges, most_common)
plt.figure(figsize=(12, 10))
utils.plot_nodes_frequencies(most_common_nodes, 'Nodes in a hypergraph')
clique_graph = converters.convert_to_clique_graph(nodes, hyperedges)
clique_markov_matrix = create_markov_matrix(clique_graph.edges())
print("clique markov matrix")
print(clique_markov_matrix)
engine = DiffusionEngine(markov_matrix)
most_common, states = engine.simulate(t_max)
plt.figure(figsize=(12, 10))
utils.plot_hyperedges_frequencies(most_common, clique_graph.edges(),
'Occurrences of edges in a graph')
most_common_nodes = count_nodes(clique_graph.nodes(), clique_graph.edges(),
most_common)
plt.figure(figsize=(12, 10))
utils.plot_nodes_frequencies(most_common_nodes, 'Nodes in a graph')
