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')
def diffusion_on_clique(hypergraph, t_max, plot_results=False):
"""Simulate diffusion on corresponding clique graph"""
nodes = hypergraph.nodes()
hyper_edges = hypergraph.hyper_edges()
clique_graph = converters.convert_to_clique_graph(nodes, hyper_edges)
markov_matrix = create_markov_matrix(clique_graph.edges(),
count_itself=False)
edges = clique_graph.edges()
most_common, most_common_nodes, states = simulate_diffusion(
nodes, edges, markov_matrix, t_max)
if plot_results:
return plot_diffusion_results(most_common, most_common_nodes,
hyper_edges, "clique")
else:
return utils.get_names_and_occurrences(most_common_nodes)[1]
def diffusion_on_clique(hypergraph, t_max, plot_results=False):
"""Simulate diffusion on corresponding clique graph"""
nodes = hypergraph.nodes()
hyper_edges = hypergraph.hyper_edges()
clique_graph = converters.convert_to_clique_graph(nodes, hyper_edges)
markov_matrix = create_markov_matrix(clique_graph.edges(),
count_itself=False)
edges = clique_graph.edges()
most_common, most_common_nodes, states = simulate_diffusion(
nodes, edges, markov_matrix, t_max)
if plot_results:
return plot_diffusion_results(most_common, most_common_nodes,
hyper_edges, "clique")
else:
return utils.get_names_and_occurrences(most_common_nodes)[1]
def plot_different_representations(nodes, hyperedges):
print("Drawing different representations of hypergraph")
print("Bipartite graph")
nx_bipartite = converters.convert_to_nx_bipartite_graph(nodes, hyperedges)
plot_bipartite_graph(nx_bipartite,
*hypergraph_to_bipartite_parts(nx_bipartite))
print("Graph of hypereges as nodes")
custom_hyper_g = converters.convert_to_custom_hyper_G(nodes, hyperedges)
plt.figure()
nx.draw(custom_hyper_g)
print("Clique graph")
clique_graph = converters.convert_to_clique_graph(nodes, hyperedges)
plt.figure()
nx.draw(clique_graph)
def plot_different_representations(nodes, hyperedges):
print("Drawing different representations of hypergraph")
print("Bipartite graph")
nx_bipartite = converters.convert_to_nx_bipartite_graph(nodes, hyperedges)
plot_bipartite_graph(nx_bipartite,
*hypergraph_to_bipartite_parts(nx_bipartite))
print("Graph of hypereges as nodes")
custom_hyper_g = converters.convert_to_custom_hyper_G(nodes, hyperedges)
plt.figure()
nx.draw(custom_hyper_g)
print("Clique graph")
clique_graph = converters.convert_to_clique_graph(nodes, hyperedges)
plt.figure()
nx.draw(clique_graph)
