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Tiago Peixoto
graph-tool
Commits
cad9691d
Commit
cad9691d
authored
Dec 21, 2010
by
Tiago Peixoto
Browse files
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Plain Diff
Documentation improvements
Added more DOI links to references, and other cosmetic changes.
parent
d2e8ce3a
Changes
10
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10 changed files
with
80 additions
and
65 deletions
+80
-65
doc/conf.py
doc/conf.py
+5
-3
doc/graph_tool.rst
doc/graph_tool.rst
+1
-0
src/graph_tool/__init__.py
src/graph_tool/__init__.py
+3
-3
src/graph_tool/centrality/__init__.py
src/graph_tool/centrality/__init__.py
+44
-38
src/graph_tool/clustering/__init__.py
src/graph_tool/clustering/__init__.py
+5
-3
src/graph_tool/community/__init__.py
src/graph_tool/community/__init__.py
+4
-3
src/graph_tool/correlations/__init__.py
src/graph_tool/correlations/__init__.py
+2
-2
src/graph_tool/flow/__init__.py
src/graph_tool/flow/__init__.py
+1
-1
src/graph_tool/generation/__init__.py
src/graph_tool/generation/__init__.py
+6
-6
src/graph_tool/topology/__init__.py
src/graph_tool/topology/__init__.py
+9
-6
No files found.
doc/conf.py
View file @
cad9691d
...
...
@@ -132,6 +132,7 @@ from numpy import *
import scipy
import scipy.stats
from math import *
import numpy as np
import graph_tool.all as gt
"""
...
...
@@ -231,7 +232,8 @@ intersphinx_mapping = {'http://docs.python.org': None,
extlinks
=
{
'ticket'
:
(
'http://projects.skewed.de/graph-tool/ticket/%s'
,
'ticket '
),
'doi'
:
(
'http://dx.doi.org/%s'
,
'DOI: '
)}
'doi'
:
(
'http://dx.doi.org/%s'
,
'DOI: '
),
'arxiv'
:
(
'http://arxiv.org/abs/%s'
,
'arXiv: '
)}
autodoc_member_order
=
'bysource'
autodoc_default_flags
=
[
'members'
]
autodoc_member_order
=
'bysource'
autodoc_default_flags
=
[
'members'
]
doc/graph_tool.rst
View file @
cad9691d
.. automodule:: graph_tool
:members:
Available subpackages
=====================
...
...
src/graph_tool/__init__.py
View file @
cad9691d
...
...
@@ -92,7 +92,7 @@ from . core import __version__, Graph, Vector_bool, Vector_int32_t, \
__all__
=
[
"Graph"
,
"Vertex"
,
"Edge"
,
"Vector_bool"
,
"Vector_int32_t"
,
"Vector_int64_t"
,
"Vector_double"
,
"Vector_long_double"
,
"Vector_string"
,
"value_types"
,
"load_graph"
,
"Property
Array
"
,
"
PropertyMap"
,
"group_vector_property"
,
"ungroup_vector_property
"
,
"
show_config
"
,
"__author__"
,
"__copyright__"
,
"__URL__"
,
"Vector_string"
,
"value_types"
,
"load_graph"
,
"Property
Map
"
,
"
group_vector_property"
,
"ungroup_vector_property"
,
"show_config
"
,
"
PropertyArray
"
,
"__author__"
,
"__copyright__"
,
"__URL__"
,
"__version__"
]
src/graph_tool/centrality/__init__.py
View file @
cad9691d
...
...
@@ -237,7 +237,7 @@ def betweenness(g, vprop=None, eprop=None, weight=None, norm=True):
----------
.. [betweenness-wikipedia] http://en.wikipedia.org/wiki/Centrality#Betweenness_centrality
.. [brandes-faster-2001] U. Brandes, "A faster algorithm for betweenness
centrality",
Journal of Mathematical Sociology, 2001
centrality",
Journal of Mathematical Sociology, 2001, :doi:`10.1080/0022250X.2001.9990249`
"""
if
vprop
==
None
:
vprop
=
g
.
new_vertex_property
(
"double"
)
...
...
@@ -302,7 +302,7 @@ def central_point_dominance(g, betweenness):
References
----------
.. [freeman-set-1977] Linton C. Freeman, "A Set of Measures of Centrality
Based on Betweenness", Sociometry, Vol. 40, No. 1, pp. 35-41
(1977)
Based on Betweenness", Sociometry, Vol. 40, No. 1, pp. 35-41
, 1977
"""
return
libgraph_tool_centrality
.
\
...
...
@@ -373,30 +373,30 @@ def eigentrust(g, trust_map, vprop=None, norm=False, epslon=1e-6, max_iter=0,
>>> trust.get_array()[:] = random(g.num_edges())*42
>>> t = gt.eigentrust(g, trust, norm=True)
>>> print t.get_array()
[ 0.0
1610395 0.03518828 0.00387335 0.00506519 0. 0.02120586
0.00
328345 0.00514034 0.00361398 0.01331587 0.00626757 0.00788882
0.01
599836 0.00607798 0.00879484 0.01028104 0.01742029 0.00522399
0.020
6618 0.0098984 0.00918508 0.01344131 0. 0.00047679
0.017
60032 0.00078869 0.01045936 0. 0.00387405 0.01761267
0.00
730843 0.00514523 0.01708638 0.0084908 0.01237811 0.01401104
0.0
209564 0.0132232 0.00031255 0.01400855 0. 0. 0.0077233
0.004
79587 0.01646928 0.01499744 0.01901516 0.00843277 0.
0.
01764526 0.00243523 0.01726375 0.01272935 0.0163525 0.00382533
0.0
2037745 0.00758792 0.00350063 0.01303079 0. 0.02086308
0.00
062028 0.00841231 0.00983605 0.00327547 0. 0.01016667
0.0
170241 0.00782474 0.00516862 0.02394048 0. 0.00747778
0.00
792131 0.01495136 0.01513948 0.02287957 0.00788276 0.0053207
0.00
145811 0.00183203 0.0033493 0.01627589 0. 0.00476343
0.00
937439 0.00200381 0.01400712 0.02135004 0.00549685 0.00230923
0.0
1426992 0.01083921 0.03439618 0.00514281 0.00114438 0.02259093
0.0
0672266 0.02753108 0.0185935
1]
[ 0.0
2100449 0.01735932 0.00227182 0.00342703 0. 0.01739914
0.00
658874 0.00592764 0.00879695 0.01483758 0.00390145 0.00939709
0.01
038803 0.00896039 0.0080222 0.00583084 0.01510505 0.01106463
0.020
48866 0.0179936 0.02196625 0.00604554 0. 0.00038504
0.017
04679 0.00431482 0.00538866 0. 0.00163772 0.02009726
0.00
254747 0.00440903 0.02305541 0.01061566 0.00583414 0.01521545
0.0
1894677 0.00941793 0.00259066 0.00454916 0. 0.
0.004
11855 0.01005776 0.029152 0.01500648 0.00797009 0.02057446
0.
0.02100182 0.00519358 0.02503401 0.00368714 0.02176737
0.0
0111934 0.02763714 0.00615445 0.00163793 0.01998869 0.
0.00
831816 0.00692008 0.00439715 0.01287125 0.00534507 0.
0.0
0805071 0.02094972 0.00622514 0.00285397 0.01009464 0.
0.00
360911 0.00653993 0.00800227 0.01521205 0.02901848 0.01693622
0.00
323205 0.00748302 0.00443795 0.0076314 0.01147831 0.
0.00
129362 0.00173367 0.00188625 0.02110825 0.01349257 0.00956502
0.0
0694694 0.01780551 0.00344632 0.02869166 0.00388418 0.0016279
0.0
1691452 0.00783781 0.02795918 0.0332707
1]
References
----------
.. [kamvar-eigentrust-2003] S. D. Kamvar, M. T. Schlosser, H. Garcia-Molina
"The eigentrust algorithm for reputation management in p2p networks",
Proceedings of the 12th international conference on World Wide Web,
Pages: 640 - 651, 2003
Pages: 640 - 651, 2003
, :doi:`10.1145/775152.775242`
"""
if
vprop
==
None
:
...
...
@@ -454,7 +454,7 @@ def trust_transitivity(g, trust_map, source=None, target=None, vprop=None):
Notes
-----
The
absolute trust
between vertices i and j is defined as
The
pervasive trust transitivity
between vertices i and j is defined as
.. math::
...
...
@@ -491,23 +491,29 @@ def trust_transitivity(g, trust_map, source=None, target=None, vprop=None):
>>> trust.a = random(g.num_edges())
>>> t = gt.trust_transitivity(g, trust, source=g.vertex(0))
>>> print t.a
[ 1. 0.15271582 0.07130332 0.10597708 0. 0.58940763
0.04233924 0.03619048 0.04137002 0.05926363 0.06584407 0.06315985
0.22301815 0.02671845 0.10566551 0.08018763 0.57668762 0.08440303
0.17612948 0.37579015 0.0415804 0.19919108 0. 0.0141547
0.14901031 0.00910391 0.02680543 0. 0.0887711 0.0296914
0.09800672 0.06421615 0.16420105 0.10226839 0.08667606 0.07944174
0.17174637 0.10932321 0.0137295 0.09342906 0. 0.
0.11065065 0.03725047 0.23554212 0.10971862 0.54564134 0.0462946 0.
0.24820041 0.15281463 0.09449931 0.22419781 0.03108608 0.10964166
0.08642532 0.03495468 0.05656444 0.04045297 0. 0.13789871
0.0197414 0.05512572 0.08297112 0.21448002 0. 0.08649514
0.0718887 0.16546776 0.04108292 0.11710843 0. 0.12518596
0.04797708 0.02275816 0.10413969 0.1294644 0.08656727 0.28371423
0.1036658 0.01575087 0.02023104 0.067158 0. 0.03241519
0.19613692 0.05684533 0.29652909 0.03038526 0.02423028 0.01695595
0.0759531 0.17360708 0.51113999 0.03714076 0.03167552 0.04359062
0.0267188 0.47605313 0.06471942]
[ 1. 0.09649648 0.01375374 0.09864347 0. 0.52668732
0.02655169 0.05771735 0.25651251 0.13071344 0.1258206 0.13065921
0.12051013 0.13754053 0.26727787 0.06951245 0.38774441 0.25343023
0.21297027 0.59232433 0.10843174 0.02810649 0. 0.04000351
0.13784095 0.06125175 0.04156937 0. 0.05771925 0.04967184
0.11251086 0.25172931 0.1982562 0.28225643 0.05339001 0.10629504
0.04440744 0.05815895 0.097983 0.03333347 0. 0.
0.10845473 0.13751647 0.27567139 0.03946153 0.25063883 0.0755547 0.
0.25167962 0.33205973 0.08237051 0.12983804 0.02587608 0.09694727
0.16435599 0.09445501 0.07402817 0.06425702 0. 0.22420236
0.11284837 0.05567628 0.0561254 0.36563496 0. 0.09358333
0.06315609 0.3853858 0.01338133 0.08506159 0. 0.23226712
0.0841518 0.07274848 0.17553984 0.14032908 0.15737553 0.13703351
0.25035262 0.03570828 0.04341688 0.11955905 0. 0.01757771
0.04990193 0.10457395 0.41668972 0.04546921 0.04404905 0.24922167
0.09752267 0.03872946 0.26113888 0.04677363 0.03220735 0.03928181
0.08696124 0.21697483 0.1388346 ]
References
----------
.. [richters-trust-2010] Oliver Richters, Tiago P. Peixoto, "Trust
transitivity in social networks", :arXiv:`1012.1358`, 2010
"""
if
vprop
==
None
:
...
...
src/graph_tool/clustering/__init__.py
View file @
cad9691d
...
...
@@ -121,7 +121,7 @@ def local_clustering(g, prop=None, undirected=False):
----------
.. [watts-collective-1998] D. J. Watts and Steven Strogatz, "Collective
dynamics of 'small-world' networks", Nature, vol. 393, pp 440-442, 1998.
doi:10.1038/30918
:doi:`10.1038/30918`
"""
if
prop
==
None
:
...
...
@@ -183,7 +183,8 @@ def global_clustering(g):
References
----------
.. [newman-structure-2003] M. E. J. Newman, "The structure and function of
complex networks", SIAM Review, vol. 45, pp. 167-256, 2003
complex networks", SIAM Review, vol. 45, pp. 167-256, 2003,
:doi:`10.1137/S003614450342480`
"""
c
=
_gt
.
global_clustering
(
g
.
_Graph__graph
)
...
...
@@ -261,7 +262,7 @@ def extended_clustering(g, props=None, max_depth=3, undirected=False):
References
----------
.. [abdo-clustering] A. H. Abdo, A. P. S. de Moura, "Clustering as a
measure of the local topology of networks",
arXiv:physics/0605235v4
measure of the local topology of networks",
:arxiv:`physics/0605235`
"""
was_directed
=
g
.
is_directed
()
...
...
@@ -345,6 +346,7 @@ def motifs(g, k, p=1.0, motif_list=None, undirected=None):
.. [wernicke-efficient-2006] S. Wernicke, "Efficient detection of network
motifs", IEEE/ACM Transactions on Computational Biology and
Bioinformatics (TCBB), Volume 3, Issue 4, Pages 347-359, 2006.
:doi:`10.1109/TCBB.2006.51`
"""
seed
=
random
.
randint
(
0
,
sys
.
maxint
)
...
...
src/graph_tool/community/__init__.py
View file @
cad9691d
...
...
@@ -193,10 +193,10 @@ def community_structure(g, n_iter, n_spins, gamma=1.0, corr="erdos",
----------
.. [reichard-statistical-2006] Joerg Reichardt and Stefan Bornholdt,
"Statistical Mechanics of Community Detection", Phys. Rev. E 74 (2006)
016110,
arXiv:cond-mat/0603718
016110,
:doi:`10.1103/PhysRevE.74.016110`, :arxiv:`cond-mat/0603718`
.. [newman-modularity-2006] M. E. J. Newman, "Modularity and community
structure in networks", Proc. Natl. Acad. Sci. USA 103, 8577-8582 (2006),
arXiv:physics/0602124
:doi:`10.1073/pnas.0601602103`, :arxiv:`physics/0602124`
.. _simulated annealing: http://en.wikipedia.org/wiki/Simulated_annealing
"""
...
...
@@ -216,6 +216,7 @@ def community_structure(g, n_iter, n_spins, gamma=1.0, corr="erdos",
_prop
(
"v"
,
g
,
spins
))
return
spins
def
modularity
(
g
,
prop
,
weight
=
None
):
r
"""
Calculate Newman's modularity.
...
...
@@ -269,7 +270,7 @@ def modularity(g, prop, weight=None):
----------
.. [newman-modularity-2006] M. E. J. Newman, "Modularity and community
structure in networks", Proc. Natl. Acad. Sci. USA 103, 8577-8582 (2006),
arXiv:physics/0602124
:doi:`10.1073/pnas.0601602103`, :arxiv:`physics/0602124`
"""
m
=
libgraph_tool_community
.
modularity
(
g
.
_Graph__graph
,
...
...
src/graph_tool/correlations/__init__.py
View file @
cad9691d
...
...
@@ -113,7 +113,7 @@ def assortativity(g, deg):
References
----------
.. [newman-mixing-2003] M. E. J. Newman, "Mixing patterns in networks",
Phys. Rev. E 67, 026126 (2003)
Phys. Rev. E 67, 026126 (2003)
, :doi:`10.1103/PhysRevE.67.026126`
.. _jackknife method: http://en.wikipedia.org/wiki/Resampling_%28statistics%29#Jackknife
"""
...
...
@@ -188,7 +188,7 @@ def scalar_assortativity(g, deg):
References
----------
.. [newman-mixing-2003] M. E. J. Newman, "Mixing patterns in networks",
Phys. Rev. E 67, 026126 (2003)
Phys. Rev. E 67, 026126 (2003)
, :doi:`10.1103/PhysRevE.67.026126`
.. _jackknife method: http://en.wikipedia.org/wiki/Resampling_%28statistics%29#Jackknife
"""
return
libgraph_tool_correlations
.
\
...
...
src/graph_tool/flow/__init__.py
View file @
cad9691d
...
...
@@ -130,7 +130,7 @@ def edmonds_karp_max_flow(g, source, target, capacity, residual=None):
.. [boost-edmonds-karp] http://www.boost.org/libs/graph/doc/edmonds_karp_max_flow.html
.. [edmonds-theoretical-1972] Jack Edmonds and Richard M. Karp, "Theoretical
improvements in the algorithmic efficiency for network flow problems.
Journal of the ACM", 19
72 19:248-264
Journal of the ACM", 19
:248-264, 1972 :doi:`10.1145/321694.321699`
.. [ravindra-network-1993] Ravindra K. Ahuja and Thomas L. Magnanti and
James B. Orlin,"Network Flows: Theory, Algorithms, and Applications".
Prentice Hall, 1993.
...
...
src/graph_tool/generation/__init__.py
View file @
cad9691d
...
...
@@ -807,7 +807,7 @@ def geometric_graph(points, radius, ranges=None):
References
----------
.. [geometric-graph] Jesper Dall and Michael Christensen, "Random geometric
graphs", Phys. Rev. E 66, 016121 (2002),
DOI: 10.1103/PhysRevE.66.016121
graphs", Phys. Rev. E 66, 016121 (2002),
:doi:`10.1103/PhysRevE.66.016121`
"""
...
...
@@ -923,17 +923,17 @@ def price_network(N, m=1, c=None, gamma=1, directed=True, seed_graph=None):
.. [yule] Yule, G. U. "A Mathematical Theory of Evolution, based on the
Conclusions of Dr. J. C. Willis, F.R.S.". Philosophical Transactions of
the Royal Society of London, Ser. B 213: 21–87, 1925,
doi:10.1098/rstb.1925.0002
:doi:`10.1098/rstb.1925.0002`
.. [price] Derek De Solla Price, "A general theory of bibliometric and other
cumulative advantage processes", Journal of the American Society for
Information Science, Volume 27, Issue 5, pages 292–306, September 1976,
DOI: 10.1002/asi.4630270505
:doi:`10.1002/asi.4630270505`
.. [barabasi-albert] Barabási, A.-L., and Albert, R., "Emergence of
scaling in random networks", Science, 286, 509, 1999
;
DOI: 10.1126/science.286.5439.509
scaling in random networks", Science, 286, 509, 1999
,
:doi:`10.1126/science.286.5439.509`
.. [dorogovtsev-evolution] S. N. Dorogovtsev and J. F. F. Mendes, "Evolution
of networks", Advances in Physics, 2002, Vol. 51, No. 4, 1079-1187,
DOI: 10.1080/00018730110112519
:doi:`10.1080/00018730110112519`
"""
if
c
is
None
:
...
...
src/graph_tool/topology/__init__.py
View file @
cad9691d
...
...
@@ -143,7 +143,7 @@ def subgraph_isomorphism(sub, g, max_n=0, random=True):
References
----------
.. [ullmann-algorithm-1976] Ullmann, J. R., "An algorithm for subgraph
isomorphism", Journal of the ACM 23 (1): 31–42, 1976,
doi:10.1145/321921.321925
isomorphism", Journal of the ACM 23 (1): 31–42, 1976,
:doi:`10.1145/321921.321925`
.. [subgraph-isormophism-wikipedia] http://en.wikipedia.org/wiki/Subgraph_isomorphism_problem
"""
...
...
@@ -255,7 +255,8 @@ def min_spanning_tree(g, weights=None, root=None, tree_map=None):
----------
.. [kruskal-shortest-1956] J. B. Kruskal. "On the shortest spanning subtree
of a graph and the traveling salesman problem", In Proceedings of the
American Mathematical Sofiety, volume 7, pages 48-50, 1956.
American Mathematical Society, volume 7, pages 48-50, 1956.
:doi:`10.1090/S0002-9939-1956-0078686-7`
.. [prim-shortest-1957] R. Prim. "Shortest connection networks and some
generalizations", Bell System Technical Journal, 36:1389-1401, 1957.
.. [boost-mst] http://www.boost.org/libs/graph/doc/graph_theory_review.html#sec:minimum-spanning-tree
...
...
@@ -659,10 +660,11 @@ def shortest_distance(g, source=None, weights=None, max_dist=None,
----------
.. [bfs] Edward Moore, "The shortest path through a maze", International
Symposium on the Theory of Switching (1959), Harvard University
Press;http://www.boost.org/libs/graph/doc/breadth_first_search.html
Press;
.. [bfs-boost] http://www.boost.org/libs/graph/doc/breadth_first_search.html
.. [dijkstra] E. Dijkstra, "A note on two problems in connexion with
graphs." Numerische Mathematik, 1:269-271, 1959.
http://www.boost.org/libs/graph/doc/dijkstra_shortest_paths.html
.. [dijkstra-boost]
http://www.boost.org/libs/graph/doc/dijkstra_shortest_paths.html
.. [johnson-apsp] http://www.boost.org/libs/graph/doc/johnson_all_pairs_shortest.html
.. [floyd-warshall-apsp] http://www.boost.org/libs/graph/doc/floyd_warshall_shortest.html
"""
...
...
@@ -764,10 +766,11 @@ def shortest_path(g, source, target, weights=None, pred_map=None):
----------
.. [bfs] Edward Moore, "The shortest path through a maze", International
Symposium on the Theory of Switching (1959), Harvard University
Press;http://www.boost.org/libs/graph/doc/breadth_first_search.html
Press
.. [bfs-boost] http://www.boost.org/libs/graph/doc/breadth_first_search.html
.. [dijkstra] E. Dijkstra, "A note on two problems in connexion with
graphs." Numerische Mathematik, 1:269-271, 1959.
http://www.boost.org/libs/graph/doc/dijkstra_shortest_paths.html
.. [dijkstra-boost]
http://www.boost.org/libs/graph/doc/dijkstra_shortest_paths.html
"""
if
pred_map
==
None
:
...
...
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