def test_directed(self):
G = nx.DiGraph(nx.path_graph(4))
communities = list(nx.girvan_newman(G))
assert_equal(len(communities), 3)
validate_communities(communities[0], [{0, 1}, {2, 3}])
validate_possible_communities(communities[1], [{0}, {1}, {2, 3}],
[{0, 1}, {2}, {3}])
validate_communities(communities[2], [{0}, {1}, {2}, {3}])
def test_most_valuable_edge(self):
G = nx.Graph()
G.add_weighted_edges_from([(0, 1, 3), (1, 2, 2), (2, 3, 1)])
# Let the most valuable edge be the one with the highest weight.
heaviest = lambda G: max(G.edges(data='weight'), key=itemgetter(2))[:2]
communities = list(nx.girvan_newman(G, heaviest))
assert_equal(len(communities), 3)
validate_communities(communities[0], [{0}, {1, 2, 3}])
validate_communities(communities[1], [{0}, {1}, {2, 3}])
validate_communities(communities[2], [{0}, {1}, {2}, {3}])
def test_selfloops(self):
G = nx.path_graph(4)
G.add_edge(0, 0)
G.add_edge(2, 2)
communities = list(nx.girvan_newman(G))
assert_equal(len(communities), 3)
validate_communities(communities[0], [{0, 1}, {2, 3}])
validate_possible_communities(communities[1], [{0}, {1}, {2, 3}],
[{0, 1}, {2}, {3}])
validate_communities(communities[2], [{0}, {1}, {2}, {3}])
def test_girvan_newman_unweighted(self):
g = nx.Graph()
g.add_edges_from([(1, 3), (1, 2), (2, 3), (3, 7), (7, 6),
(6, 4), (6, 5), (5, 4), (7, 8), (8, 9),
(9, 11), (9, 10), (10, 11), (8, 12), (12, 13),
(12, 14), (13, 14)])
result = nx.girvan_newman(g)
assert_equal(len(result), 3)
validate_communities(result[0], [(1, 2, 3, 4, 5, 6, 7), (8, 9, 10, 11, 12, 13, 14)])
validate_communities(result[1], [(1, 2, 3), (4, 5, 6), (9, 10, 11), (12, 13, 14),
(7, ), (8, )])
validate_communities(result[2], [(1,), (2, ), (3, ), (4, ), (5, ), (6, ),
(7, ), (8, ), (9, ), (10,), (11, ), (12, ),
(13, ), (14, )])
dg = g.to_directed()
result = nx.girvan_newman(dg)
validate_communities(result[0], [(1, 2, 3, 4, 5, 6, 7), (8, 9, 10, 11, 12, 13, 14)])
validate_communities(result[1], [(1, 2, 3), (4, 5, 6), (9, 10, 11), (12, 13, 14),
(7, ), (8, )])
validate_communities(result[2], [(1,), (2, ), (3, ), (4, ), (5, ), (6, ),
(7, ), (8, ), (9, ), (10,), (11, ), (12, ),
(13, ), (14, )])
def test_undirected(self):
# Start with the graph .-.-.-.
G = nx.path_graph(4)
communities = list(nx.girvan_newman(G))
assert_equal(len(communities), 3)
# After one removal, we get the graph .-. .-.
validate_communities(communities[0], [{0, 1}, {2, 3}])
# After the next, we get the graph .-. . ., but there are two
# symmetric possible verisons.
validate_possible_communities(communities[1], [{0}, {1}, {2, 3}],
[{0, 1}, {2}, {3}])
# After the last removal, we alway get the empty graph.
validate_communities(communities[2], [{0}, {1}, {2}, {3}])
def get_girvan(g, n):
girv = nx.girvan_newman(g)
return list(islice(girv, 0, n))[n - 1]
lines["from"].append(index1)
lines["to"].append(index2)
lines["geometry"].append(LineString([room1.geometry.centroid, room2.geometry.centroid]))
edges = gpd.GeoDataFrame(lines)
# let's create network, not just displaying..
room_network = nx.Graph()
for i, edge in edges.iterrows():
room_network.add_edge(edge["from"], edge["to"])
# calculate closeness and add the values to rooms
closeness = nx.closeness_centrality(room_network)
rooms['closeness'] = pd.Series(closeness)
# color rooms by closeness
fig, ax = plt.subplots()
fig.set_size_inches(12, 12)
ax.axison = False
rooms.plot(ax=ax, linewidth=0.3, column='closeness', cmap='coolwarm')
edges.plot(ax=ax, linewidth=0.3, color='#888888')
rooms.geometry.centroid.plot(ax=ax, markersize=6, color='#DDDDDD')
ax.set_aspect('equal')
plt.show()
comm = nx.girvan_newman(room_network)
print(comm)
def test_girvan_newman_no_edges(self):
g = nx.Graph()
g.add_nodes_from([1, 2, 3, 4, 5])
result = nx.girvan_newman(g)
assert_equal(len(result), 0)
validate_communities(result, [])
def test_no_edges(self):
G = nx.empty_graph(3)
communities = list(nx.girvan_newman(G))
assert_equal(len(communities), 1)
validate_communities(communities[0], [{0}, {1}, {2}])
