File size: 14,081 Bytes
d1ceb73 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 |
import pytest
np = pytest.importorskip("numpy")
pytest.importorskip("scipy")
import networkx as nx
from networkx.generators.degree_seq import havel_hakimi_graph
from networkx.generators.expanders import margulis_gabber_galil_graph
class TestLaplacian:
@classmethod
def setup_class(cls):
deg = [3, 2, 2, 1, 0]
cls.G = havel_hakimi_graph(deg)
cls.WG = nx.Graph(
(u, v, {"weight": 0.5, "other": 0.3}) for (u, v) in cls.G.edges()
)
cls.WG.add_node(4)
cls.MG = nx.MultiGraph(cls.G)
# Graph with clsloops
cls.Gsl = cls.G.copy()
for node in cls.Gsl.nodes():
cls.Gsl.add_edge(node, node)
# Graph used as an example in Sec. 4.1 of Langville and Meyer,
# "Google's PageRank and Beyond".
cls.DiG = nx.DiGraph()
cls.DiG.add_edges_from(
(
(1, 2),
(1, 3),
(3, 1),
(3, 2),
(3, 5),
(4, 5),
(4, 6),
(5, 4),
(5, 6),
(6, 4),
)
)
cls.DiMG = nx.MultiDiGraph(cls.DiG)
cls.DiWG = nx.DiGraph(
(u, v, {"weight": 0.5, "other": 0.3}) for (u, v) in cls.DiG.edges()
)
cls.DiGsl = cls.DiG.copy()
for node in cls.DiGsl.nodes():
cls.DiGsl.add_edge(node, node)
def test_laplacian(self):
"Graph Laplacian"
# fmt: off
NL = np.array([[ 3, -1, -1, -1, 0],
[-1, 2, -1, 0, 0],
[-1, -1, 2, 0, 0],
[-1, 0, 0, 1, 0],
[ 0, 0, 0, 0, 0]])
# fmt: on
WL = 0.5 * NL
OL = 0.3 * NL
# fmt: off
DiNL = np.array([[ 2, -1, -1, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[-1, -1, 3, -1, 0, 0],
[ 0, 0, 0, 2, -1, -1],
[ 0, 0, 0, -1, 2, -1],
[ 0, 0, 0, 0, -1, 1]])
# fmt: on
DiWL = 0.5 * DiNL
DiOL = 0.3 * DiNL
np.testing.assert_equal(nx.laplacian_matrix(self.G).todense(), NL)
np.testing.assert_equal(nx.laplacian_matrix(self.MG).todense(), NL)
np.testing.assert_equal(
nx.laplacian_matrix(self.G, nodelist=[0, 1]).todense(),
np.array([[1, -1], [-1, 1]]),
)
np.testing.assert_equal(nx.laplacian_matrix(self.WG).todense(), WL)
np.testing.assert_equal(nx.laplacian_matrix(self.WG, weight=None).todense(), NL)
np.testing.assert_equal(
nx.laplacian_matrix(self.WG, weight="other").todense(), OL
)
np.testing.assert_equal(nx.laplacian_matrix(self.DiG).todense(), DiNL)
np.testing.assert_equal(nx.laplacian_matrix(self.DiMG).todense(), DiNL)
np.testing.assert_equal(
nx.laplacian_matrix(self.DiG, nodelist=[1, 2]).todense(),
np.array([[1, -1], [0, 0]]),
)
np.testing.assert_equal(nx.laplacian_matrix(self.DiWG).todense(), DiWL)
np.testing.assert_equal(
nx.laplacian_matrix(self.DiWG, weight=None).todense(), DiNL
)
np.testing.assert_equal(
nx.laplacian_matrix(self.DiWG, weight="other").todense(), DiOL
)
def test_normalized_laplacian(self):
"Generalized Graph Laplacian"
# fmt: off
G = np.array([[ 1. , -0.408, -0.408, -0.577, 0.],
[-0.408, 1. , -0.5 , 0. , 0.],
[-0.408, -0.5 , 1. , 0. , 0.],
[-0.577, 0. , 0. , 1. , 0.],
[ 0. , 0. , 0. , 0. , 0.]])
GL = np.array([[ 1. , -0.408, -0.408, -0.577, 0. ],
[-0.408, 1. , -0.5 , 0. , 0. ],
[-0.408, -0.5 , 1. , 0. , 0. ],
[-0.577, 0. , 0. , 1. , 0. ],
[ 0. , 0. , 0. , 0. , 0. ]])
Lsl = np.array([[ 0.75 , -0.2887, -0.2887, -0.3536, 0. ],
[-0.2887, 0.6667, -0.3333, 0. , 0. ],
[-0.2887, -0.3333, 0.6667, 0. , 0. ],
[-0.3536, 0. , 0. , 0.5 , 0. ],
[ 0. , 0. , 0. , 0. , 0. ]])
DiG = np.array([[ 1. , 0. , -0.4082, 0. , 0. , 0. ],
[ 0. , 0. , 0. , 0. , 0. , 0. ],
[-0.4082, 0. , 1. , 0. , -0.4082, 0. ],
[ 0. , 0. , 0. , 1. , -0.5 , -0.7071],
[ 0. , 0. , 0. , -0.5 , 1. , -0.7071],
[ 0. , 0. , 0. , -0.7071, 0. , 1. ]])
DiGL = np.array([[ 1. , 0. , -0.4082, 0. , 0. , 0. ],
[ 0. , 0. , 0. , 0. , 0. , 0. ],
[-0.4082, 0. , 1. , -0.4082, 0. , 0. ],
[ 0. , 0. , 0. , 1. , -0.5 , -0.7071],
[ 0. , 0. , 0. , -0.5 , 1. , -0.7071],
[ 0. , 0. , 0. , 0. , -0.7071, 1. ]])
DiLsl = np.array([[ 0.6667, -0.5774, -0.2887, 0. , 0. , 0. ],
[ 0. , 0. , 0. , 0. , 0. , 0. ],
[-0.2887, -0.5 , 0.75 , -0.2887, 0. , 0. ],
[ 0. , 0. , 0. , 0.6667, -0.3333, -0.4082],
[ 0. , 0. , 0. , -0.3333, 0.6667, -0.4082],
[ 0. , 0. , 0. , 0. , -0.4082, 0.5 ]])
# fmt: on
np.testing.assert_almost_equal(
nx.normalized_laplacian_matrix(self.G, nodelist=range(5)).todense(),
G,
decimal=3,
)
np.testing.assert_almost_equal(
nx.normalized_laplacian_matrix(self.G).todense(), GL, decimal=3
)
np.testing.assert_almost_equal(
nx.normalized_laplacian_matrix(self.MG).todense(), GL, decimal=3
)
np.testing.assert_almost_equal(
nx.normalized_laplacian_matrix(self.WG).todense(), GL, decimal=3
)
np.testing.assert_almost_equal(
nx.normalized_laplacian_matrix(self.WG, weight="other").todense(),
GL,
decimal=3,
)
np.testing.assert_almost_equal(
nx.normalized_laplacian_matrix(self.Gsl).todense(), Lsl, decimal=3
)
np.testing.assert_almost_equal(
nx.normalized_laplacian_matrix(
self.DiG,
nodelist=range(1, 1 + 6),
).todense(),
DiG,
decimal=3,
)
np.testing.assert_almost_equal(
nx.normalized_laplacian_matrix(self.DiG).todense(), DiGL, decimal=3
)
np.testing.assert_almost_equal(
nx.normalized_laplacian_matrix(self.DiMG).todense(), DiGL, decimal=3
)
np.testing.assert_almost_equal(
nx.normalized_laplacian_matrix(self.DiWG).todense(), DiGL, decimal=3
)
np.testing.assert_almost_equal(
nx.normalized_laplacian_matrix(self.DiWG, weight="other").todense(),
DiGL,
decimal=3,
)
np.testing.assert_almost_equal(
nx.normalized_laplacian_matrix(self.DiGsl).todense(), DiLsl, decimal=3
)
def test_directed_laplacian():
"Directed Laplacian"
# Graph used as an example in Sec. 4.1 of Langville and Meyer,
# "Google's PageRank and Beyond". The graph contains dangling nodes, so
# the pagerank random walk is selected by directed_laplacian
G = nx.DiGraph()
G.add_edges_from(
(
(1, 2),
(1, 3),
(3, 1),
(3, 2),
(3, 5),
(4, 5),
(4, 6),
(5, 4),
(5, 6),
(6, 4),
)
)
# fmt: off
GL = np.array([[ 0.9833, -0.2941, -0.3882, -0.0291, -0.0231, -0.0261],
[-0.2941, 0.8333, -0.2339, -0.0536, -0.0589, -0.0554],
[-0.3882, -0.2339, 0.9833, -0.0278, -0.0896, -0.0251],
[-0.0291, -0.0536, -0.0278, 0.9833, -0.4878, -0.6675],
[-0.0231, -0.0589, -0.0896, -0.4878, 0.9833, -0.2078],
[-0.0261, -0.0554, -0.0251, -0.6675, -0.2078, 0.9833]])
# fmt: on
L = nx.directed_laplacian_matrix(G, alpha=0.9, nodelist=sorted(G))
np.testing.assert_almost_equal(L, GL, decimal=3)
# Make the graph strongly connected, so we can use a random and lazy walk
G.add_edges_from(((2, 5), (6, 1)))
# fmt: off
GL = np.array([[ 1. , -0.3062, -0.4714, 0. , 0. , -0.3227],
[-0.3062, 1. , -0.1443, 0. , -0.3162, 0. ],
[-0.4714, -0.1443, 1. , 0. , -0.0913, 0. ],
[ 0. , 0. , 0. , 1. , -0.5 , -0.5 ],
[ 0. , -0.3162, -0.0913, -0.5 , 1. , -0.25 ],
[-0.3227, 0. , 0. , -0.5 , -0.25 , 1. ]])
# fmt: on
L = nx.directed_laplacian_matrix(
G, alpha=0.9, nodelist=sorted(G), walk_type="random"
)
np.testing.assert_almost_equal(L, GL, decimal=3)
# fmt: off
GL = np.array([[ 0.5 , -0.1531, -0.2357, 0. , 0. , -0.1614],
[-0.1531, 0.5 , -0.0722, 0. , -0.1581, 0. ],
[-0.2357, -0.0722, 0.5 , 0. , -0.0456, 0. ],
[ 0. , 0. , 0. , 0.5 , -0.25 , -0.25 ],
[ 0. , -0.1581, -0.0456, -0.25 , 0.5 , -0.125 ],
[-0.1614, 0. , 0. , -0.25 , -0.125 , 0.5 ]])
# fmt: on
L = nx.directed_laplacian_matrix(G, alpha=0.9, nodelist=sorted(G), walk_type="lazy")
np.testing.assert_almost_equal(L, GL, decimal=3)
# Make a strongly connected periodic graph
G = nx.DiGraph()
G.add_edges_from(((1, 2), (2, 4), (4, 1), (1, 3), (3, 4)))
# fmt: off
GL = np.array([[ 0.5 , -0.176, -0.176, -0.25 ],
[-0.176, 0.5 , 0. , -0.176],
[-0.176, 0. , 0.5 , -0.176],
[-0.25 , -0.176, -0.176, 0.5 ]])
# fmt: on
L = nx.directed_laplacian_matrix(G, alpha=0.9, nodelist=sorted(G))
np.testing.assert_almost_equal(L, GL, decimal=3)
def test_directed_combinatorial_laplacian():
"Directed combinatorial Laplacian"
# Graph used as an example in Sec. 4.1 of Langville and Meyer,
# "Google's PageRank and Beyond". The graph contains dangling nodes, so
# the pagerank random walk is selected by directed_laplacian
G = nx.DiGraph()
G.add_edges_from(
(
(1, 2),
(1, 3),
(3, 1),
(3, 2),
(3, 5),
(4, 5),
(4, 6),
(5, 4),
(5, 6),
(6, 4),
)
)
# fmt: off
GL = np.array([[ 0.0366, -0.0132, -0.0153, -0.0034, -0.0020, -0.0027],
[-0.0132, 0.0450, -0.0111, -0.0076, -0.0062, -0.0069],
[-0.0153, -0.0111, 0.0408, -0.0035, -0.0083, -0.0027],
[-0.0034, -0.0076, -0.0035, 0.3688, -0.1356, -0.2187],
[-0.0020, -0.0062, -0.0083, -0.1356, 0.2026, -0.0505],
[-0.0027, -0.0069, -0.0027, -0.2187, -0.0505, 0.2815]])
# fmt: on
L = nx.directed_combinatorial_laplacian_matrix(G, alpha=0.9, nodelist=sorted(G))
np.testing.assert_almost_equal(L, GL, decimal=3)
# Make the graph strongly connected, so we can use a random and lazy walk
G.add_edges_from(((2, 5), (6, 1)))
# fmt: off
GL = np.array([[ 0.1395, -0.0349, -0.0465, 0. , 0. , -0.0581],
[-0.0349, 0.093 , -0.0116, 0. , -0.0465, 0. ],
[-0.0465, -0.0116, 0.0698, 0. , -0.0116, 0. ],
[ 0. , 0. , 0. , 0.2326, -0.1163, -0.1163],
[ 0. , -0.0465, -0.0116, -0.1163, 0.2326, -0.0581],
[-0.0581, 0. , 0. , -0.1163, -0.0581, 0.2326]])
# fmt: on
L = nx.directed_combinatorial_laplacian_matrix(
G, alpha=0.9, nodelist=sorted(G), walk_type="random"
)
np.testing.assert_almost_equal(L, GL, decimal=3)
# fmt: off
GL = np.array([[ 0.0698, -0.0174, -0.0233, 0. , 0. , -0.0291],
[-0.0174, 0.0465, -0.0058, 0. , -0.0233, 0. ],
[-0.0233, -0.0058, 0.0349, 0. , -0.0058, 0. ],
[ 0. , 0. , 0. , 0.1163, -0.0581, -0.0581],
[ 0. , -0.0233, -0.0058, -0.0581, 0.1163, -0.0291],
[-0.0291, 0. , 0. , -0.0581, -0.0291, 0.1163]])
# fmt: on
L = nx.directed_combinatorial_laplacian_matrix(
G, alpha=0.9, nodelist=sorted(G), walk_type="lazy"
)
np.testing.assert_almost_equal(L, GL, decimal=3)
E = nx.DiGraph(margulis_gabber_galil_graph(2))
L = nx.directed_combinatorial_laplacian_matrix(E)
# fmt: off
expected = np.array(
[[ 0.16666667, -0.08333333, -0.08333333, 0. ],
[-0.08333333, 0.16666667, 0. , -0.08333333],
[-0.08333333, 0. , 0.16666667, -0.08333333],
[ 0. , -0.08333333, -0.08333333, 0.16666667]]
)
# fmt: on
np.testing.assert_almost_equal(L, expected, decimal=6)
with pytest.raises(nx.NetworkXError):
nx.directed_combinatorial_laplacian_matrix(G, walk_type="pagerank", alpha=100)
with pytest.raises(nx.NetworkXError):
nx.directed_combinatorial_laplacian_matrix(G, walk_type="silly")
|