def test_weighted_graph_label(self):
g = nx.Graph()
edges = [(1, 2), (2, 3), (3, 4), (4, 1)]
g.add_edges_from(edges)
g[1][2]["w"] = 1
g[2][3]["w"] = 2
g[3][4]["w"] = 3
g[4][1]["w"] = 4
g = StellarGraph(g, edge_weight_label="w")
nodes = list(g.nodes())
n = 1
length = 1
seed = None
p = 1.0
q = 1.0
biasedrw = BiasedRandomWalk(g)
assert (len(
biasedrw.run(nodes=nodes,
n=n,
p=p,
q=q,
length=length,
seed=seed,
weighted=True)) == 4)
g = nx.Graph()
edges = [(1, 2), (2, 3), (3, 4), (4, 1)]
g.add_edges_from(edges)
g[1][2]["wt"] = 1
g[2][3]["wt"] = 2
g[3][4]["wt"] = 3
g[4][1]["wt"] = 4
# Deliberately use wrong name for edge weight!?
g = StellarGraph(g, edge_weight_label="w")
nodes = list(g.nodes())
n = 1
length = 1
seed = None
p = 1.0
q = 1.0
biasedrw = BiasedRandomWalk(g)
with pytest.raises(ValueError):
biasedrw.run(nodes=nodes,
n=n,
p=p,
q=q,
length=length,
seed=seed,
weighted=True)
def create_multi_test_graph():
"""
Creates a multi graph for testing the SampledHeterogeneousBreadthFirstWalk class. The node ids are string or
integers.
:return: A multi graph with 8 nodes and 9 edges (with no self loops but 1 with only a self loop and 1 isolated node)
in StellarGraph format.
"""
g = StellarGraph()
g.add_nodes_from([0, 1, "5", 4, 7], label="user")
g.add_nodes_from([2, 3, 6], label="movie")
g.add_edges_from([(1, 2), (1, 3), ("5", 6), ("5", 3), (4, 2)], label="rating")
g.add_edges_from([("5", 4), (1, 4), (1, "5")], label="friend")
g.add_edges_from([(1, 4)], label="colleague")
return g