def test_uniform_generator():
number_of_nodes = 10
number_of_edges = 5
cardinality = 3
hu = generators.uniform_hypergraph(number_of_nodes, number_of_edges,
cardinality)
nt.assert_true(hu)
nt.assert_equals(len(hu.nodes()), number_of_nodes)
nt.assert_equals(len(hu.hyper_edges()), number_of_edges)
nt.assert_true(all(len(e) == cardinality for e in hu.hyper_edges()))
def test_uniform_generator():
number_of_nodes = 10
number_of_edges = 5
cardinality = 3
hu = generators.uniform_hypergraph(number_of_nodes, number_of_edges,
cardinality)
nt.assert_true(hu)
nt.assert_equals(len(hu.nodes()), number_of_nodes)
nt.assert_equals(len(hu.hyper_edges()), number_of_edges)
nt.assert_true(all(len(e) == cardinality for e in hu.hyper_edges()))
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 show_different_hypergraphs(n=10, k=3, parts=10):
for fraction in np.linspace(0, 1, parts)[1:]:
plt.figure(figsize=(6, 6))
g = uniform_hypergraph(n=n, k=k, number_of_edges=int(n * fraction))
G = convert_to_nx_bipartite_graph(g.nodes(), g.hyper_edges())
utils.plot_bipartite_graph(G, *utils.hypergraph_to_bipartite_parts(G))
hyper_G = convert_to_custom_hyper_G(g.nodes(), g.hyper_edges())
plt.figure(figsize=(6, 6))
nx.draw(hyper_G, node_size=3000, cmap=plt.cm.Blues, alpha=0.6)
plt.show()
def show_different_hypergraphs(n=10, k=3, parts=10):
for fraction in np.linspace(0, 1, parts)[1:]:
plt.figure(figsize=(6, 6))
g = uniform_hypergraph(n=n, k=k,
number_of_edges=int(n * fraction))
G = convert_to_nx_bipartite_graph(g.nodes(), g.hyper_edges())
utils.plot_bipartite_graph(G, *utils.hypergraph_to_bipartite_parts(G))
hyper_G = convert_to_custom_hyper_G(g.nodes(), g.hyper_edges())
plt.figure(figsize=(6, 6))
nx.draw(hyper_G,
node_size=3000, cmap=plt.cm.Blues, alpha=0.6)
plt.show()