docs: fix a lot of outdated links (#824)

Author: szhorvat

doc/examples_sphinx-gallery/cluster_contraction.py

@@ -12,7 +12,7 @@
 
 # %%
 # We begin by load the graph from file. The file containing this network can be
-# downloaded `here <http://www-personal.umich.edu/~mejn/netdata/>`_.
+# downloaded `here <https://www-personal.umich.edu/~mejn/netdata/>`_.
 g = ig.load("./lesmis/lesmis.gml")
 
 # %%

doc/source/install.rst

@@ -131,7 +131,7 @@ Fourth, call ``pip`` to compile and install the package from source::
   $ pip install .
 
 Alternatively, you can call ``build`` or another PEP 517-compliant build frontend
-to build an installable Python wheel. Here we use `pipx <https://pypa.github.io/pipx/>`_
+to build an installable Python wheel. Here we use `pipx <https://pipx.pypa.io>`_
 to invoke ``build`` in a separate virtualenv::
 
   $ pipx run build
@@ -140,7 +140,7 @@ Testing your installation
 -------------------------
 
 Use ``tox`` or another standard test runner tool to run all the unit tests.
-Here we use `pipx <https://pypa.github.io/pipx/>`_` to invoke ``tox``::
+Here we use `pipx <https://pipx.pypa.io>`_ to invoke ``tox``::
 
   $ pipx run tox
 

doc/source/visualisation.rst

@@ -234,6 +234,6 @@ See the :ref:`tutorial <tutorial-layouts-plotting>` for examples and a full list
 .. _matplotlib: https://matplotlib.org
 .. _Jupyter: https://jupyter.org/
 .. _Cairo: https://www.cairographics.org
-.. _graphviz: http://www.graphviz.org
+.. _graphviz: https://www.graphviz.org
 .. _networkx: https://networkx.org/
 .. _graph-tool: https://graph-tool.skewed.de/

setup.py

@@ -953,7 +953,7 @@ def get_tag(self):
 Graph plotting functionality is provided by the Cairo library, so make
 sure you install the Python bindings of Cairo if you want to generate
 publication-quality graph plots. You can try either `pycairo
-<http://cairographics.org/pycairo>`_ or `cairocffi <http://cairocffi.readthedocs.io>`_,
+<http://cairographics.org/pycairo>`_ or `cairocffi <https://doc.courtbouillon.org/cairocffi/>`_,
 ``cairocffi`` is recommended because there were bug reports affecting igraph
 graph plots in Jupyter notebooks when using ``pycairo`` (but not with
 ``cairocffi``).
@@ -983,7 +983,7 @@ def get_tag(self):
         "Bug Tracker": "https://github.com/igraph/python-igraph/issues",
         "Changelog": "https://github.com/igraph/python-igraph/blob/main/CHANGELOG.md",
         "CI": "https://github.com/igraph/python-igraph/actions",
-        "Documentation": "https://igraph.readthedocs.io",
+        "Documentation": "https://python.igraph.org",
         "Source Code": "https://github.com/igraph/python-igraph",
     },
     "ext_modules": [igraph_extension],

src/_igraph/graphobject.c

@@ -14530,7 +14530,7 @@ struct PyMethodDef igraphmodule_Graph_methods[] = {
     "the number of vertices in the graph, a list of shifts giving\n"
     "additional edges to a cycle backbone and another integer giving how\n"
     "many times the shifts should be performed. See\n"
-    "U{http://mathworld.wolfram.com/LCFNotation.html} for details.\n\n"
+    "U{https://mathworld.wolfram.com/LCFNotation.html} for details.\n\n"
     "@param n: the number of vertices\n"
     "@param shifts: the shifts in a list or tuple\n"
     "@param repeats: the number of repeats\n"
@@ -16222,7 +16222,7 @@ struct PyMethodDef igraphmodule_Graph_methods[] = {
    "    small-world networks. I{Nature} 393(6884):440-442, 1998.\n"
    "  - Barrat A, Barthelemy M, Pastor-Satorras R and Vespignani A:\n"
    "    The architecture of complex weighted networks. I{PNAS} 101, 3747 (2004).\n"
-   "    U{http://arxiv.org/abs/cond-mat/0311416}.\n\n"
+   "    U{https://arxiv.org/abs/cond-mat/0311416}.\n\n"
    "@param vertices: a list containing the vertex IDs which should be\n"
    "  included in the result. C{None} means all of the vertices.\n"
    "@param mode: defines how to treat vertices with degree less than two.\n"
@@ -16820,7 +16820,8 @@ struct PyMethodDef igraphmodule_Graph_methods[] = {
    "forces among the vertices and then the physical system is simulated\n"
    "until it reaches an equilibrium or the maximal number of iterations is\n"
    "reached.\n\n"
-   "See U{http://www.schmuhl.org/graphopt/} for the original graphopt.\n\n"
+   "See U{https://web.archive.org/web/20220611030748/http://www.schmuhl.org/graphopt/}\n"
+   "and U{https://sourceforge.net/projects/graphopt/} for the original graphopt.\n\n"
    "@param niter: the number of iterations to perform. Should be a couple\n"
    "  of hundred in general.\n\n"
    "@param node_charge: the charge of the vertices, used to calculate electric\n"
@@ -17135,7 +17136,7 @@ struct PyMethodDef igraphmodule_Graph_methods[] = {
    "Read_DIMACS(f, directed=False)\n--\n\n"
    "Reads a graph from a file conforming to the DIMACS minimum-cost flow file format.\n\n"
    "For the exact description of the format, see\n"
-   "U{http://lpsolve.sourceforge.net/5.5/DIMACS.htm}\n\n"
+   "U{https://lpsolve.sourceforge.net/5.5/DIMACS.htm}\n\n"
    "Restrictions compared to the official description of the format:\n\n"
    "  - igraph's DIMACS reader requires only three fields in an arc definition,\n"
    "    describing the edge's source and target node and its capacity.\n"
@@ -17171,7 +17172,7 @@ struct PyMethodDef igraphmodule_Graph_methods[] = {
    "Read_GraphDB(f, directed=False)\n--\n\n"
    "Reads a GraphDB format file and creates a graph based on it.\n\n"
    "GraphDB is a binary format, used in the graph database for\n"
-   "isomorphism testing (see U{http://amalfi.dis.unina.it/graph/}).\n\n"
+   "isomorphism testing (see U{https://mivia.unisa.it/datasets/graph-database/arg-database/}).\n\n"
    "@param f: the name of the file or a Python file handle\n"
    "@param directed: whether the generated graph should be directed.\n"},
   /* interface to igraph_read_graph_graphml */
@@ -17394,7 +17395,7 @@ struct PyMethodDef igraphmodule_Graph_methods[] = {
    "algorithm.\n\n"
    "Passing the permutation returned here to L{permute_vertices()} will\n"
    "transform the graph into its canonical form.\n\n"
-   "See U{http://www.tcs.hut.fi/Software/bliss/index.html} for more information\n"
+   "See U{https://users.aalto.fi/~tjunttil/bliss/} for more information\n"
    "about the BLISS algorithm and canonical permutations.\n\n"
    "@param sh: splitting heuristics for graph as a case-insensitive string,\n"
    "  with the following possible values:\n\n"
@@ -17420,7 +17421,7 @@ struct PyMethodDef igraphmodule_Graph_methods[] = {
    "count_automorphisms(sh=\"fl\", color=None)\n--\n\n"
    "Calculates the number of automorphisms of a graph using the BLISS isomorphism\n"
    "algorithm.\n\n"
-   "See U{http://www.tcs.hut.fi/Software/bliss/index.html} for more information\n"
+   "See U{https://users.aalto.fi/~tjunttil/bliss/} for more information\n"
    "about the BLISS algorithm and canonical permutations.\n\n"
    "@param sh: splitting heuristics for graph as a case-insensitive string,\n"
    "  with the following possible values:\n\n"
@@ -17471,7 +17472,7 @@ struct PyMethodDef igraphmodule_Graph_methods[] = {
    "  sh1=\"fl\", sh2=None, color1=None, color2=None)\n--\n\n"
    "Checks whether the graph is isomorphic to another graph, using the\n"
    "BLISS isomorphism algorithm.\n\n"
-   "See U{http://www.tcs.hut.fi/Software/bliss/index.html} for more information\n"
+   "See U{https://users.aalto.fi/~tjunttil/bliss/} for more information\n"
    "about the BLISS algorithm.\n\n"
    "@param other: the other graph with which we want to compare the graph.\n"
    "@param color1: optional vector storing the coloring of the vertices of\n"
@@ -18264,15 +18265,15 @@ struct PyMethodDef igraphmodule_Graph_methods[] = {
    "community_infomap(edge_weights=None, vertex_weights=None, trials=10)\n--\n\n"
    "Finds the community structure of the network according to the Infomap\n"
    "method of Martin Rosvall and Carl T. Bergstrom.\n\n"
-   "See U{http://www.mapequation.org} for a visualization of the algorithm\n"
+   "See U{https://www.mapequation.org} for a visualization of the algorithm\n"
    "or one of the references provided below.\n"
    "B{References}\n"
    "  - M. Rosvall and C. T. Bergstrom: I{Maps of information flow reveal\n"
    "    community structure in complex networks}. PNAS 105, 1118 (2008).\n"
-   "    U{http://arxiv.org/abs/0707.0609}\n"
+   "    U{https://arxiv.org/abs/0707.0609}\n"
    "  - M. Rosvall, D. Axelsson and C. T. Bergstrom: I{The map equation}.\n"
    "    I{Eur Phys J Special Topics} 178, 13 (2009).\n"
-   "    U{http://arxiv.org/abs/0906.1405}\n"
+   "    U{https://arxiv.org/abs/0906.1405}\n"
    "\n"
    "@param edge_weights: name of an edge attribute or a list containing\n"
    "  edge weights.\n"
@@ -18301,7 +18302,7 @@ struct PyMethodDef igraphmodule_Graph_methods[] = {
    "B{Reference}: Raghavan, U.N. and Albert, R. and Kumara, S. Near linear\n"
    "time algorithm to detect community structures in large-scale\n"
    "networks. I{Phys Rev E} 76:036106, 2007.\n"
-   "U{http://arxiv.org/abs/0709.2938}.\n"
+   "U{https://arxiv.org/abs/0709.2938}.\n"
    "\n"
    "@param weights: name of an edge attribute or a list containing\n"
    "  edge weights\n"
@@ -18356,7 +18357,7 @@ struct PyMethodDef igraphmodule_Graph_methods[] = {
    "\n"
    "B{Reference}: VD Blondel, J-L Guillaume, R Lambiotte and E Lefebvre: Fast\n"
    "unfolding of community hierarchies in large networks. J Stat Mech\n"
-   "P10008 (2008), U{http://arxiv.org/abs/0803.0476}\n"
+   "P10008 (2008), U{https://arxiv.org/abs/0803.0476}\n"
    "\n"
    "Attention: this function is wrapped in a more convenient syntax in the\n"
    "derived class L{Graph}. It is advised to use that instead of this version.\n\n"
@@ -18495,7 +18496,7 @@ struct PyMethodDef igraphmodule_Graph_methods[] = {
    "Attention: this function is wrapped in a more convenient syntax in the\n"
    "derived class L{Graph}. It is advised to use that instead of this version.\n\n"
    "B{Reference}: Pascal Pons, Matthieu Latapy: Computing communities in large\n"
-   "networks using random walks, U{http://arxiv.org/abs/physics/0512106}.\n\n"
+   "networks using random walks, U{https://arxiv.org/abs/physics/0512106}.\n\n"
    "@param weights: name of an edge attribute or a list containing\n"
    "  edge weights\n"
    "@return: a tuple with the list of merges and the modularity scores corresponding\n"

src/igraph/__init__.py

@@ -860,7 +860,7 @@ def transitivity_avglocal_undirected(self, mode="nan", weights=None):
             networks. I{Nature} 393(6884):440-442, 1998.
           - Barrat A, Barthelemy M, Pastor-Satorras R and Vespignani A:
             The architecture of complex weighted networks. I{PNAS} 101, 3747
-            (2004). U{http://arxiv.org/abs/cond-mat/0311416}.
+            (2004). U{https://arxiv.org/abs/cond-mat/0311416}.
 
         @param mode: defines how to treat vertices with degree less than two.
           If C{TRANSITIVITY_ZERO} or C{"zero"}, these vertices will have zero

src/igraph/community.py

@@ -37,12 +37,12 @@ def _community_infomap(graph, edge_weights=None, vertex_weights=None, trials=10)
 
       - M. Rosvall and C. T. Bergstrom: Maps of information flow reveal
         community structure in complex networks, I{PNAS} 105, 1118 (2008).
-        U{http://dx.doi.org/10.1073/pnas.0706851105},
-        U{http://arxiv.org/abs/0707.0609}.
+        U{https://doi.org/10.1073/pnas.0706851105},
+        U{https://arxiv.org/abs/0707.0609}.
       - M. Rosvall, D. Axelsson, and C. T. Bergstrom: The map equation,
         I{Eur Phys. J Special Topics} 178, 13 (2009).
-        U{http://dx.doi.org/10.1140/epjst/e2010-01179-1},
-        U{http://arxiv.org/abs/0906.1405}.
+        U{https://doi.org/10.1140/epjst/e2010-01179-1},
+        U{https://arxiv.org/abs/0906.1405}.
 
     @param edge_weights: name of an edge attribute or a list containing
       edge weights.
@@ -125,7 +125,7 @@ def _community_label_propagation(graph, weights=None, initial=None, fixed=None):
 
     B{Reference}: Raghavan, U.N. and Albert, R. and Kumara, S. Near linear
     time algorithm to detect community structures in large-scale networks.
-    I{Phys Rev} E 76:036106, 2007. U{http://arxiv.org/abs/0709.2938}.
+    I{Phys Rev} E 76:036106, 2007. U{https://arxiv.org/abs/0709.2938}.
 
     @param weights: name of an edge attribute or a list containing
       edge weights
@@ -165,7 +165,7 @@ def _community_multilevel(graph, weights=None, return_levels=False, resolution=1
 
     B{Reference}: VD Blondel, J-L Guillaume, R Lambiotte and E Lefebvre: Fast
     unfolding of community hierarchies in large networks, I{J Stat Mech}
-    P10008 (2008). U{http://arxiv.org/abs/0803.0476}
+    P10008 (2008). U{https://arxiv.org/abs/0803.0476}
 
     @param weights: edge attribute name or a list containing edge
       weights
@@ -271,11 +271,11 @@ def _community_spinglass(graph, *args, **kwds):
 
       - Reichardt J and Bornholdt S: Statistical mechanics of community
         detection. I{Phys Rev E} 74:016110 (2006).
-        U{http://arxiv.org/abs/cond-mat/0603718}.
+        U{https://arxiv.org/abs/cond-mat/0603718}.
 
       - Traag VA and Bruggeman J: Community detection in networks
         with positive and negative links. I{Phys Rev E} 80:036115 (2009).
-        U{http://arxiv.org/abs/0811.2329}.
+        U{https://arxiv.org/abs/0811.2329}.
 
     @keyword weights: edge weights to be used. Can be a sequence or
       iterable or even an edge attribute name.
@@ -329,7 +329,7 @@ def _community_walktrap(graph, weights=None, steps=4):
     as a dendrogram.
 
     B{Reference}: Pascal Pons, Matthieu Latapy: Computing communities in large
-    networks using random walks, U{http://arxiv.org/abs/physics/0512106}.
+    networks using random walks, U{https://arxiv.org/abs/physics/0512106}.
 
     @param weights: name of an edge attribute or a list containing
       edge weights

src/igraph/drawing/__init__.py

@@ -5,9 +5,9 @@
 It also has experimental support for plotly.
 
 The Cairo backend is dependent on the C{pycairo} or C{cairocffi} libraries that
-provide Python bindings to the popular U{Cairo library<http://www.cairographics.org>}.
-This means that if you don't have U{pycairo<http://www.cairographics.org/pycairo>}
-or U{cairocffi<http://cairocffi.readthedocs.io>} installed, you won't be able
+provide Python bindings to the popular U{Cairo library<https://www.cairographics.org>}.
+This means that if you don't have U{pycairo<https://pycairo.readthedocs.io/>}
+or U{cairocffi<https://doc.courtbouillon.org/cairocffi/>} installed, you won't be able
 to use the Cairo plotting backend. Whenever the documentation refers to the
 C{pycairo} library, you can safely replace it with C{cairocffi} as the two are
 API-compatible.
@@ -21,8 +21,8 @@
 
 If you do not want to (or cannot) install any of the dependencies outlined
 above, you can still save the graph to an SVG file and view it from
-U{Mozilla Firefox<http://www.mozilla.org/firefox>} (free) or edit it in
-U{Inkscape<http://www.inkscape.org>} (free), U{Skencil<http://www.skencil.org>}
+U{Mozilla Firefox<https://www.mozilla.org/firefox>} (free) or edit it in
+U{Inkscape<https://www.inkscape.org>} (free), U{Skencil<https://www.skencil.org>}
 (formerly known as Sketch, also free) or Adobe Illustrator.
 """
 

src/igraph/drawing/cairo/plot.py

@@ -6,7 +6,7 @@
 The Cairo backend is dependent on the C{pycairo} or C{cairocffi} libraries that
 provide Python bindings to the popular U{Cairo library<http://www.cairographics.org>}.
 This means that if you don't have U{pycairo<http://www.cairographics.org/pycairo>}
-or U{cairocffi<http://cairocffi.readthedocs.io>} installed, you won't be able
+or U{cairocffi<https://doc.courtbouillon.org/cairocffi/>} installed, you won't be able
 to use the Cairo plotting backend. Whenever the documentation refers to the
 C{pycairo} library, you can safely replace it with C{cairocffi} as the two are
 API-compatible.
@@ -17,8 +17,8 @@
 
 If you do not want to (or cannot) install any of the dependencies outlined
 above, you can still save the graph to an SVG file and view it from
-U{Mozilla Firefox<http://www.mozilla.org/firefox>} (free) or edit it in
-U{Inkscape<http://www.inkscape.org>} (free), U{Skencil<http://www.skencil.org>}
+U{Mozilla Firefox<https://www.mozilla.org/firefox>} (free) or edit it in
+U{Inkscape<https://www.inkscape.org>} (free), U{Skencil<https://www.skencil.org>}
 (formerly known as Sketch, also free) or Adobe Illustrator.
 """
 

src/igraph/drawing/graph.py

@@ -7,8 +7,8 @@
     - Matplotlib axes (L{MatplotlibGraphDrawer})
 
 It also contains routines to send an igraph graph directly to
-(U{Cytoscape<http://www.cytoscape.org>}) using the
-(U{CytoscapeRPC plugin<http://gforge.nbic.nl/projects/cytoscaperpc/>}), see
+(U{Cytoscape<https://cytoscape.org>}) using the
+(U{CytoscapeRPC plugin<https://apps.cytoscape.org/apps/cytoscaperpc>}), see
 L{CytoscapeGraphDrawer}. L{CytoscapeGraphDrawer} can also fetch the current
 network from Cytoscape and convert it to igraph format.
 """
@@ -24,8 +24,8 @@ class CytoscapeGraphDrawer(AbstractXMLRPCDrawer, AbstractGraphDrawer):
     """Graph drawer that sends/receives graphs to/from Cytoscape using
     CytoscapeRPC.
 
-    This graph drawer cooperates with U{Cytoscape<http://www.cytoscape.org>}
-    using U{CytoscapeRPC<http://wiki.nbic.nl/index.php/CytoscapeRPC>}.
+    This graph drawer cooperates with U{Cytoscape<https://cytoscape.org>}
+    using U{CytoscapeRPC<https://apps.cytoscape.org/apps/cytoscaperpc>}.
     You need to install the CytoscapeRPC plugin first and start the
     XML-RPC server on a given port (port 9000 by default) from the
     appropriate Plugins submenu in Cytoscape.