`graph_tool` - Core module#

Summary#

`Graph`	General multigraph class.
`GraphView`	A view of selected vertices or edges of another graph.
`Vertex`	Vertex descriptor.
`Edge`	Edge descriptor.
`VertexPropertyMap`	This class provides a mapping from vertices to arbitrary properties.
`EdgePropertyMap`	This class provides a mapping from edges to arbitrary properties.
`GraphPropertyMap`	This class provides a mapping from graphs to arbitrary properties.
`PropertyMap`	This base class provides a mapping from vertices, edges or whole graphs to arbitrary properties.
`PropertyArray`	This is a `numpy.ndarray` subclass which keeps a reference of its `PropertyMap` owner.
`load_graph`	Load a graph from `file_name` (which can be either a string or a file-like object).
`load_graph_from_csv`	Load a graph from a `csv` file containing a list of edges and edge properties.
`group_vector_property`	Group list of properties `props` into a vector property map of the same type.
`ungroup_vector_property`	Ungroup vector property map `vprop` into a list of individual property maps.
`map_property_values`	Map the values of `src_prop` to `tgt_prop` according to the mapping function `map_func`.
`infect_vertex_property`	Propagate the prop values of vertices with value val to all their out-neighbors.
`edge_endpoint_property`	Return an edge property map corresponding to the vertex property prop of either the target and source of the edge, according to endpoint.
`incident_edges_op`	Return a vertex property map corresponding to a specific operation (sum, product, min or max) on the edge property eprop of incident edges on each vertex, following the direction given by direction.
`perfect_prop_hash`	Given a list of property maps props of the same type, a derived list of property maps with integral type htype is returned, where each value is replaced by a perfect (i.e.
`value_types`	Return a list of possible properties value types.
`openmp_enabled`	Return `True` if OpenMP was enabled during compilation.
`openmp_get_num_threads`	Return the number of OpenMP threads.
`openmp_set_num_threads`	Set the number of OpenMP threads.
`openmp_get_schedule`	Return the runtime OpenMP schedule and chunk size.
`openmp_set_schedule`	Set the runtime OpenMP schedule and chunk size.
`show_config`	Show `graph_tool` build configuration.

This module provides:

A Graph class for graph representation and manipulation

Property maps for Vertex, Edge or Graph.

Fast algorithms implemented in C++.

How to use the documentation#

Documentation is available in two forms: docstrings provided with the code, and the full documentation available in the graph-tool homepage.

We recommend exploring the docstrings using IPython, an advanced Python shell with TAB-completion and introspection capabilities.

The docstring examples assume that graph_tool.all has been imported as gt:

>>> import graph_tool.all as gt

Code snippets are indicated by three greater-than signs:

>>> x = x + 1

Use the built-in help function to view a function’s docstring:

>>> help(gt.Graph)

Contents#

Basic classes

class graph_tool.Graph(g=None, directed=True, prune=False, vorder=None, **kwargs)[source]#

General multigraph class.

This class encapsulates either a directed multigraph (default or if directed == True) or an undirected multigraph (if directed == False), with optional internal edge, vertex or graph properties.

If g is specified, it can be one of:

Another Graph object, in which case the corresponding graph (and its internal properties) will be copied.

An integer, in wich case it corresponds to the number of vertices in the graph.
An edge list, i.e. an iterable over (source, target) pairs, which will be used to populate the graph.

This is equivalent to calling:
>>> ng = gt.Graph()
>>> ng.add_edge_list(g)
An adjacency list, i.e. a dictionary with vertex keys mapping to an interable of vertices, which will be used to populate the graph. For directed graphs, the adjacency should list the out-neighbors.

This is equivalent to calling:
>>> ng = gt.Graph()
>>> def elist():
...     for u, vw in g.items():
...         k = 0
...         for v in vw:
...             k += 1
...             yield u, v
...         if k == 0:
...             yield u, None
>>> ng.add_edge_list(elist())
Note

For undirected graphs, if a vertex u appears in the adjacency list of v and vice versa, then the edge (u,v) is added twice in the graph. To prevent this from happening the adjancecy list should mention an edge only once.

In cases 3 and 4 above, all remaining keyword parameters passed to Graph will be passed along to the Graph.add_edge_list() function. If the option hashed == True is passed, the vertex ids will be stored in an internal VertexPropertyMap called "ids".

In case g is specified and points to a Graph object, the following options take effect:

If prune is set to True, only the filtered graph will be copied, and the new graph object will not be filtered. Optionally, a tuple of three booleans can be passed as value to prune, to specify a different behavior to vertex, edge, and reversal filters, respectively.

If vorder is specified, it should correspond to a vertex VertexPropertyMap specifying the ordering of the vertices in the copied graph.

Note

The graph is implemented internally as an adjacency list, where both vertex and edge lists are C++ STL vectors.

copy()[source]#: Return a deep copy of self. All internal property maps are also copied.

Iterating over vertices and edges

See Iterating over vertices and edges for more documentation and examples.

Iterator-based interface with descriptors:

vertices()[source]#

Return an iterator over the vertices.

Note

The order of the vertices traversed by the iterator always corresponds to the vertex index ordering, as given by the vertex_index property map.

Examples

>>> g = gt.Graph()
>>> vlist = list(g.add_vertex(5))
>>> vlist2 = []
>>> for v in g.vertices():
...     vlist2.append(v)
...
>>> assert(vlist == vlist2)

edges()[source]#: Return an iterator over the edges.

Note

The order of the edges traversed by the iterator does not necessarily correspond to the edge index ordering, as given by the edge_index property map. This will only happen after reindex_edges() is called, or in certain situations such as just after a graph is loaded from a file. However, further manipulation of the graph may destroy the ordering.

Iterator-based interface without descriptors:

iter_vertices(vprops=[])[source]#

Return an iterator over the vertex indices, and optional vertex properties list vprops.

Note

This mode of iteration is more efficient than using vertices(), as descriptor objects are not created.

Examples

>>> g = gt.Graph()
>>> g.add_vertex(5)
<...>
>>> for v in g.iter_vertices():
...     print(v)
0
1
2
3
4

iter_edges(eprops=[])[source]#

Return an iterator over the edge `(source, target) pairs, and optional edge properties list eprops.

Note

This mode of iteration is more efficient than using edges(), as descriptor objects are not created.

Examples

>>> g = gt.collection.data["karate"]
>>> for s, t, i in g.iter_edges([g.edge_index]):
...     print(s, t, i)
...     if s == 5:
...         break
1 0 0
2 0 1
2 1 2
3 0 3
3 1 4
3 2 5
4 0 6
5 0 7

iter_out_edges(v, eprops=[])[source]#

Return an iterator over the out-edge `(source, target) pairs for vertex v, and optional edge properties list eprops.

Note

This mode of iteration is more efficient than using out_edges(), as descriptor objects are not created.

Examples

>>> g = gt.collection.data["pgp-strong-2009"]
>>> for s, t, i in g.iter_out_edges(66, [g.edge_index]):
...     print(s, t, i)
66 63 5266
66 20369 5267
66 13980 5268
66 8687 5269
66 38674 5270

iter_in_edges(v, eprops=[])[source]#

Return an iterator over the in-edge `(source, target) pairs for vertex v, and optional edge properties list eprops.

Note

This mode of iteration is more efficient than using in_edges(), as descriptor objects are not created.

Examples

>>> g = gt.collection.data["pgp-strong-2009"]
>>> for s, t, i in g.iter_in_edges(66, [g.edge_index]):
...     print(s, t, i)
8687 66 179681
20369 66 255033
38674 66 300230

iter_all_edges(v, eprops=[])[source]#

Return an iterator over the in- and out-edge `(source, target) pairs for vertex v, and optional edge properties list eprops.

Note

This mode of iteration is more efficient than using all_edges(), as descriptor objects are not created.

Examples

>>> g = gt.collection.data["pgp-strong-2009"]
>>> for s, t, i in g.iter_all_edges(66, [g.edge_index]):
...     print(s, t, i)
63 5266
20369 5267
13980 5268
8687 5269
38674 5270
66 179681
66 255033
66 300230

iter_out_neighbors(v, vprops=[])[source]#

Return an iterator over the out-neighbors of vertex v, and optional vertex properties list vprops.

Note

This mode of iteration is more efficient than using out_neighbors(), as descriptor objects are not created.

Examples

>>> g = gt.collection.data["pgp-strong-2009"]
>>> for u, i in g.iter_out_neighbors(66, [g.vp.uid]):
...     print(u, i)
63 ['paul wilders <webmaster@wilders.org>']
20369 ['Zhen-Xjell <zhen-xjell@teamhelix.net>']
13980 ['Hooman <Hooman@iname.com>']
8687 ['H. Loeung (howe81) <howe81@unixque.com>', 'howe81 <howe81@bigpond.net.au>', 'Howie L (howe81) <howe81@bigpond.net.au>']
38674 ['Howie L (howe81) <howe81@bigpond.net.au>']

iter_in_neighbors(v, vprops=[])[source]#

Return an iterator over the in-neighbors of vertex v, and optional vertex properties list vprops.

Note

This mode of iteration is more efficient than using in_neighbors(), as descriptor objects are not created.

Examples

>>> g = gt.collection.data["pgp-strong-2009"]
>>> for u, i in g.iter_in_neighbors(66, [g.vp.uid]):
...     print(u, i)
8687 ['H. Loeung (howe81) <howe81@unixque.com>', 'howe81 <howe81@bigpond.net.au>', 'Howie L (howe81) <howe81@bigpond.net.au>']
20369 ['Zhen-Xjell <zhen-xjell@teamhelix.net>']
38674 ['Howie L (howe81) <howe81@bigpond.net.au>']

iter_all_neighbors(v, vprops=[])[source]#

Return an iterator over the in- and out-neighbors of vertex v, and optional vertex properties list vprops.

Note

This mode of iteration is more efficient than using all_neighbors(), as descriptor objects are not created.

Examples

>>> g = gt.collection.data["pgp-strong-2009"]
>>> for u, i in g.iter_all_neighbors(66, [g.vp.uid]):
...     print(u, i)
['paul wilders <webmaster@wilders.org>']
['Zhen-Xjell <zhen-xjell@teamhelix.net>']
['Hooman <Hooman@iname.com>']
['H. Loeung (howe81) <howe81@unixque.com>', 'howe81 <howe81@bigpond.net.au>', 'Howie L (howe81) <howe81@bigpond.net.au>']
['Howie L (howe81) <howe81@bigpond.net.au>']
['H. Loeung (howe81) <howe81@unixque.com>', 'howe81 <howe81@bigpond.net.au>', 'Howie L (howe81) <howe81@bigpond.net.au>']
['Zhen-Xjell <zhen-xjell@teamhelix.net>']
['Howie L (howe81) <howe81@bigpond.net.au>']

Array-based interface:

get_vertices(vprops=[])[source]#

Return a numpy.ndarray containing the vertex indices, and optional vertex properties list vprops. If vprops is not empty, the shape of the array will be (V, 1 + len(vprops)), where V is the number of vertices, and each line will contain the vertex and the vertex property values.

Note

The order of the vertices is identical to vertices().

Examples

>>> g = gt.Graph()
>>> g.add_vertex(5)
<...>
>>> g.get_vertices()
array([0, 1, 2, 3, 4])

get_edges(eprops=[])[source]#

Return a numpy.ndarray containing the edges, and optional edge properties list eprops. The shape of the array will be (E, 2 + len(eprops)), where E is the number of edges, and each line will contain the source, target and the edge property values.

Note

The order of the edges is identical to edges().

Examples

>>> g = gt.random_graph(6, lambda: 1, directed=False)
>>> g.get_edges([g.edge_index])
array([[0, 3, 2],
       [1, 4, 0],
       [2, 5, 1]])

get_out_edges(v, eprops=[])[source]#

Return a numpy.ndarray containing the out-edges of vertex v, and optional edge properties list eprops. The shape of the array will be (E, 2 + len(eprops)), where E is the number of edges, and each line will contain the source, target and the edge property values.

Examples

>>> g = gt.collection.data["pgp-strong-2009"]
>>> g.get_out_edges(66, [g.edge_index])
array([[   66,    63,  5266],
       [   66, 20369,  5267],
       [   66, 13980,  5268],
       [   66,  8687,  5269],
       [   66, 38674,  5270]])

get_in_edges(v, eprops=[])[source]#

Return a numpy.ndarray containing the in-edges of vertex v, and optional edge properties list eprops. The shape of the array will be (E, 2 + len(eprops)), where E is the number of edges, and each line will contain the source, target and the edge property values.

Examples

>>> g = gt.collection.data["pgp-strong-2009"]
>>> g.get_in_edges(66, [g.edge_index])
array([[  8687,     66, 179681],
       [ 20369,     66, 255033],
       [ 38674,     66, 300230]])

get_all_edges(v, eprops=[])[source]#

Return a numpy.ndarray containing the in- and out-edges of vertex v, and optional edge properties list eprops. The shape of the array will be (E, 2 + len(eprops)), where E is the number of edges, and each line will contain the source, target and the edge property values.

Examples

>>> g = gt.collection.data["pgp-strong-2009"]
>>> g.get_all_edges(66, [g.edge_index])
array([[    66,     63,   5266],
       [    66,  20369,   5267],
       [    66,  13980,   5268],
       [    66,   8687,   5269],
       [    66,  38674,   5270],
       [  8687,     66, 179681],
       [ 20369,     66, 255033],
       [ 38674,     66, 300230]])

get_out_neighbors(v, vprops=[])[source]#

Return a numpy.ndarray containing the out-neighbors of vertex v, and optional vertex properties list vprops. If vprops is not empty, the shape of the array will be (V, 1 + len(eprops)), where V is the number of vertices, and each line will contain a vertex and its property values.

Examples

>>> g = gt.collection.data["pgp-strong-2009"]
>>> g.get_out_neighbors(66)
array([   63, 20369, 13980,  8687, 38674])

get_in_neighbors(v, vprops=[])[source]#

Return a numpy.ndarray containing the in-neighbors of vertex v, and optional vertex properties list vprops. If vprops is not empty, the shape of the array will be (V, 1 + len(eprops)), where V is the number of vertices, and each line will contain a vertex and its property values.

Examples

>>> g = gt.collection.data["pgp-strong-2009"]
>>> g.get_in_neighbors(66)
array([ 8687, 20369, 38674])

get_all_neighbors(v, vprops=[])[source]#

Return a numpy.ndarray containing the in-neighbors and out-neighbors of vertex v, and optional vertex properties list vprops. If vprops is not empty, the shape of the array will be (V, 1 + len(eprops)), where V is the number of vertices, and each line will contain a vertex and its property values.

Examples

>>> g = gt.collection.data["pgp-strong-2009"]
>>> g.get_all_neighbors(66)
array([   63, 20369, 13980,  8687, 38674,  8687, 20369, 38674])

get_out_degrees(vs, eweight=None)[source]#

Return a numpy.ndarray containing the out-degrees of vertex list vs. If supplied, the degrees will be weighted according to the edge EdgePropertyMap eweight.

Examples

>>> g = gt.collection.data["pgp-strong-2009"]
>>> g.get_out_degrees([42, 666])
array([20, 38], dtype=uint64)

get_in_degrees(vs, eweight=None)[source]#

Return a numpy.ndarray containing the in-degrees of vertex list vs. If supplied, the degrees will be weighted according to the edge EdgePropertyMap eweight.

Examples

>>> g = gt.collection.data["pgp-strong-2009"]
>>> g.get_in_degrees([42, 666])
array([20, 39], dtype=uint64)

get_total_degrees(vs, eweight=None)[source]#

Return a numpy.ndarray containing the total degrees (i.e. in- plus out-degree) of vertex list vs. If supplied, the degrees will be weighted according to the edge EdgePropertyMap eweight.

Examples

>>> g = gt.collection.data["pgp-strong-2009"]
>>> g.get_total_degrees([42, 666])
array([40, 77], dtype=uint64)

Obtaining vertex and edge descriptors

vertex(i, use_index=True, add_missing=False)[source]#

Return the vertex with index i. If use_index=False, the i-th vertex is returned (which can differ from the vertex with index i in case of filtered graphs).

If add_missing == True, and the vertex does not exist in the graph, the necessary number of missing vertices are inserted, and the new vertex is returned.

edge(s, t, all_edges=False, add_missing=False)[source]#

Return the edge from vertex s to t, if it exists. If all_edges=True then a list is returned with all the parallel edges from s to t, otherwise only one edge is returned.

If add_missing == True, a new edge is created and returned, if none currently exists.

This operation will take \(O(min(k(s), k(t)))\) time, where \(k(s)\) and \(k(t)\) are the out-degree and in-degree (or out-degree if undirected) of vertices \(s\) and \(t\).

Number of vertices and edges

num_vertices(ignore_filter=False)[source]#

Get the number of vertices.

If ignore_filter == True, vertex filters are ignored.

Note

If the vertices are being filtered, and ignore_filter == False, this operation is \(O(V)\). Otherwise it is \(O(1)\).

num_edges(ignore_filter=False)[source]#

Get the number of edges.

If ignore_filter == True, edge filters are ignored.

Note

If the edges are being filtered, and ignore_filter == False, this operation is \(O(E)\). Otherwise it is \(O(1)\).

Modifying vertices and edges

The following functions allow for addition and removal of vertices in the graph.

add_vertex(n=1)[source]#: Add a vertex to the graph, and return it. If n != 1, n vertices are inserted and an iterator over the new vertices is returned. This operation is \(O(n)\).

remove_vertex(vertex, fast=False)[source]#

Remove a vertex from the graph. If vertex is an iterable, it should correspond to a sequence of vertices to be removed.

Note

If the option fast == False is given, this operation is \(O(V + E)\) (this is the default). Otherwise it is \(O(k + k_{\text{last}})\), where \(k\) is the (total) degree of the vertex being deleted, and \(k_{\text{last}}\) is the (total) degree of the vertex with the largest index.

Warning

This operation may invalidate vertex descriptors. Vertices are always indexed contiguously in the range \([0, N-1]\), hence vertex descriptors with an index higher than vertex will be invalidated after removal (if fast == False, otherwise only descriptors pointing to vertices with the largest index will be invalidated).

Because of this, the only safe way to remove more than one vertex at once is to sort them in decreasing index order:

# 'del_list' is a list of vertex descriptors
for v in reversed(sorted(del_list)):
    g.remove_vertex(v)

Alternatively (and preferably), a list (or iterable) may be passed directly as the vertex parameter, and the above is performed internally (in C++).

Warning

If fast == True, the vertex being deleted is ‘swapped’ with the last vertex (i.e. with the largest index), which will in turn inherit the index of the vertex being deleted. All property maps associated with the graph will be properly updated, but the index ordering of the graph will no longer be the same.

The following functions allow for addition and removal of edges in the graph.

add_edge(source, target, add_missing=True)[source]#

Add a new edge from source to target to the graph, and return it. This operation is \(O(1)\).

If add_missing == True, the source and target vertices are included in the graph if they don’t yet exist.

remove_edge(edge)[source]#: Remove an edge from the graph.

Note

This operation is normally \(O(k_s + k_t)\), where \(k_s\) and \(k_s\) are the total degrees of the source and target vertices, respectively. However, if set_fast_edge_removal() is set to True, this operation becomes \(O(1)\).

Warning

The relative ordering of the remaining edges in the graph is kept unchanged, unless set_fast_edge_removal() is set to True, in which case it can change.

add_edge_list(edge_list, hashed=False, hash_type='string', eprops=None)[source]#

Add a list of edges to the graph, given by edge_list, which can be an iterator of (source, target) pairs where both source and target are vertex indexes (or can be so converted), or a numpy.ndarray of shape (E,2), where E is the number of edges, and each line specifies a (source, target) pair. If the list references vertices which do not exist in the graph, they will be created.

Optionally, if hashed == True, the vertex values in the edge list are not assumed to correspond to vertex indices directly. In this case they will be mapped to vertex indices according to the order in which they are encountered, and a vertex property map with the vertex values is returned. The option hash_type will determine the expected type used by the hash keys, and they can be any property map value type (see PropertyMap), unless edge_list is a numpy.ndarray, in which case the value of this option is ignored, and the type is determined automatically.

If hashed == False and the target value of an edge corresponds to the maximum interger value (sys.maxsize, or the maximum integer type of the numpy.ndarray object), or is a numpy.nan or numpy.inf value, then only the source vertex will be added to the graph.

If hashed == True, and the target value corresponds to None, then only the source vertex will be added to the graph.

If given, eprops should specify an iterable containing edge property maps that will be filled with the remaining values at each row, if there are more than two. Alternatively, eprops can contain a list of (name, value_type) pairs, in which case new internal dege property maps will be created with the corresponding name name and value type.

Note

If edge_list is a numpy.ndarray object, the execution of this function will be done entirely in C++, and hence much faster.

Examples

>>> edge_list = [(0, 1, .3, 10), (2, 3, .1, 0), (2, 0, .4, 42)]
>>> g = gt.Graph()
>>> eweight = g.new_ep("double")
>>> elayer = g.new_ep("int")
>>> g.add_edge_list(edge_list, eprops=[eweight, elayer])
>>> print(eweight.fa)
[0.3 0.1 0.4]
>>> g.get_edges()
array([[0, 1],
       [2, 3],
       [2, 0]])

set_fast_edge_removal(fast=True)[source]#: If fast == True the fast \(O(1)\) removal of edges will be enabled. This requires an additional data structure of size \(O(E)\) to be kept at all times. If fast == False, this data structure is destroyed.

get_fast_edge_removal()[source]#: Return whether the fast \(O(1)\) removal of edges is currently enabled.

The following functions allow for easy removal of vertices and edges from the graph.

clear()[source]#: Remove all vertices and edges from the graph.

clear_vertex(vertex)[source]#: Remove all in and out-edges from the given vertex.

clear_edges()[source]#: Remove all edges from the graph.

After the removal of many edges and/or vertices, the underlying containers may have a capacity that significantly exceeds the size of the graph. The function below corrects this.

shrink_to_fit()[source]#: Force the physical capacity of the underlying containers to match the graph’s actual size, potentially freeing memory back to the system.

Directedness and reversal of edges

Note

These functions do not actually modify the graph, and are fully reversible. They are also very cheap, with an \(O(1)\) complexity.

set_directed(is_directed)[source]#: Set the directedness of the graph.

Note

This is a \(O(1)\) operation that does not modify the storage of the graph.

Warning

Changing directedness will invalidate existing vertex and edge descriptors, which will still point to the original graph.

is_directed()[source]#: Get the directedness of the graph.

set_reversed(is_reversed)[source]#: Reverse the direction of the edges, if is_reversed is True, or maintain the original direction otherwise.

Note

This is a \(O(1)\) operation that does not modify the storage of the graph.

Warning

Reversing the graph will invalidate existing vertex and edge descriptors, which will still point to the original graph.

is_reversed()[source]#: Return True if the edges are reversed, and False otherwise.

Creation of new property maps

new_property(key_type, value_type, vals=None)[source]#: Create a new (uninitialized) vertex property map of key type key_type (v, e or g), value type value_type, and return it. If provided, the values will be initialized by vals, which should be a sequence.

new_vertex_property(value_type, vals=None, val=None)[source]#: Create a new vertex property map of type value_type, and return it. If provided, the values will be initialized by vals, which should be sequence or by val which should be a single value.

new_vp(value_type, vals=None, val=None)#: Alias to new_vertex_property().

new_edge_property(value_type, vals=None, val=None)[source]#: Create a new edge property map of type value_type, and return it. If provided, the values will be initialized by vals, which should be sequence or by val which should be a single value.

new_ep(value_type, vals=None, val=None)#: Alias to new_edge_property().

new_graph_property(value_type, val=None)[source]#: Create a new graph property map of type value_type, and return it. If val is not None, the property is initialized to its value.

new_gp(value_type, val=None)#: Alias to new_graph_property().

New property maps can be created by copying already existing ones.

copy_property(src, tgt=None, value_type=None, g=None, full=True)[source]#: Copy contents of src property to tgt property. If tgt is None, then a new property map of the same type (or with the type given by the optional value_type parameter) is created, and returned. The optional parameter g specifies the source graph to copy properties from (defaults to the graph than owns src). If full == False, then in the case of filtered graphs only the unmasked values are copied (with the remaining ones taking the type-dependent default value).

Note

In case the source property map belongs to different graphs, this function behaves as follows.

For vertex properties, the source and target graphs must have the same number of vertices, and the properties are copied according to the index values.

For edge properties, the edge index is not important, and the properties are copied by matching edges between the different graphs according to the source and target vertex indexes. In case the graph has parallel edges with the same source and target vertices, they are matched according to their iteration order. The edge sets do not have to be the same between source and target graphs, as the copying occurs only for edges that lie at their intersection.

degree_property_map(deg, weight=None)[source]#: Create and return a vertex property map containing the degree type given by deg, which can be any of "in", "out", or "total". If provided, weight should be an edge EdgePropertyMap containing the edge weights which should be summed.

Index property maps

vertex_index#

Vertex index map.

It maps for each vertex in the graph an unique integer in the range [0, num_vertices() - 1].

Note

Like edge_index, this is a special instance of a VertexPropertyMap class, which is immutable, and cannot be accessed as an array.

edge_index#

Edge index map.

It maps for each edge in the graph an unique integer.

Note

Like vertex_index, this is a special instance of a EdgePropertyMap class, which is immutable, and cannot be accessed as an array.

Additionally, the indexes may not necessarily lie in the range [0, num_edges() - 1]. However this will always happen whenever no edges are deleted from the graph.

edge_index_range#: The size of the range of edge indexes.

reindex_edges()[source]#: Reset the edge indexes so that they lie in the [0, num_edges() - 1] range. The index ordering will be compatible with the sequence returned by the edges() function.

Warning

Calling this function will invalidate all existing edge property maps, if the index ordering is modified! The property maps will still be usable, but their contents will still be tied to the old indexes, and thus may become scrambled.

Internal property maps

Internal property maps are just like regular property maps, with the only exception that they are saved and loaded to/from files together with the graph itself. See internal property maps for more details.

Note

All dictionaries below are mutable. However, any dictionary returned below is only an one-way proxy to the internally-kept properties. If you modify this object, the change will be propagated to the internal dictionary, but not vice-versa. Keep this in mind if you intend to keep a copy of the returned object.

properties#

Dictionary of internal properties. Keys must always be a tuple, where the first element if a string from the set {‘v’, ‘e’, ‘g’}, representing a vertex, edge or graph property, respectively, and the second element is the name of the property map.

Examples

>>> g = gt.Graph()
>>> g.properties[("e", "foo")] = g.new_edge_property("vector<double>")
>>> del g.properties[("e", "foo")]

vertex_properties#: Dictionary of internal vertex properties. The keys are the property names.

vp#: Alias to vertex_properties.

edge_properties#: Dictionary of internal edge properties. The keys are the property names.

ep#: Alias to edge_properties.

graph_properties#: Dictionary of internal graph properties. The keys are the property names.

gp#: Alias to graph_properties.

own_property(prop)[source]#: Return a version of the property map ‘prop’ (possibly belonging to another graph) which is owned by the current graph.

list_properties()[source]#

Print a list of all internal properties.

Examples

>>> g = gt.Graph()
>>> g.properties[("e", "foo")] = g.new_edge_property("vector<double>")
>>> g.vertex_properties["foo"] = g.new_vertex_property("double")
>>> g.vertex_properties["bar"] = g.new_vertex_property("python::object")
>>> g.graph_properties["gnat"] = g.new_graph_property("string", "hi there!")
>>> g.list_properties()
gnat           (graph)   (type: string, val: hi there!)
bar            (vertex)  (type: python::object)
foo            (vertex)  (type: double)
foo            (edge)    (type: vector<double>)

Filtering of vertices and edges.

See Graph filtering for more details.

Note

These functions do not actually modify the graph, and are fully reversible. They are also very cheap, and have an \(O(1)\) complexity.

set_filters(eprop, vprop, inverted_edges=False, inverted_vertices=False)[source]#: Set the boolean properties for edge and vertex filters, respectively. Only the vertices and edges with value different than False are kept in the filtered graph. If either the inverted_edges or inverted_vertex options are supplied with the value True, only the edges or vertices with value False are kept. If any of the supplied property is None, an empty filter is constructed which allows all edges or vertices.

Note

This is a \(O(1)\) operation that does not modify the storage of the graph.

Warning

Setting vertex or edge filters will invalidate existing vertex and edge descriptors, which will still point to the unfiltered graph.

set_vertex_filter(prop, inverted=False)[source]#: Set the vertex boolean filter property. Only the vertices with value different than False are kept in the filtered graph. If the inverted option is supplied with value True, only the vertices with value False are kept. If the supplied property is None, the filter is replaced by an uniform filter allowing all vertices.

Note

This is a \(O(1)\) operation that does not modify the storage of the graph.

Warning

Setting vertex filters will invalidate existing vertex and edge descriptors, which will still point to the unfiltered graph.

get_vertex_filter()[source]#: Return a tuple with the vertex filter property and bool value indicating whether or not it is inverted.

set_edge_filter(prop, inverted=False)[source]#: Set the edge boolean filter property. Only the edges with value different than False are kept in the filtered graph. If the inverted option is supplied with value True, only the edges with value False are kept. If the supplied property is None, the filter is replaced by an uniform filter allowing all edges.

Note

This is a \(O(1)\) operation that does not modify the storage of the graph.

Warning

Setting edge filters will invalidate existing vertex and edge descriptors, which will still point to the unfiltered graph.

get_edge_filter()[source]#: Return a tuple with the edge filter property and bool value indicating whether or not it is inverted.

clear_filters()[source]#: Remove vertex and edge filters, and set the graph to the unfiltered state.

Note

This is a \(O(1)\) operation that does not modify the storage of the graph.

Warning

Clearing vertex and edge filters will invalidate existing vertex and edge descriptors.

Warning

The purge functions below irreversibly remove the filtered vertices or edges from the graph. Note that, contrary to the functions above, these are \(O(V)\) and \(O(E)\) operations, respectively.

purge_vertices(in_place=False)[source]#

Remove all vertices of the graph which are currently being filtered out. This operation is not reversible.

If the option in_place == True is given, the algorithm will remove the filtered vertices and re-index all property maps which are tied with the graph. This is a slow operation which has an \(O(V^2)\) complexity.

If in_place == False, the graph and its vertex and edge property maps are temporarily copied to a new unfiltered graph, which will replace the contents of the original graph. This is a fast operation with an \(O(V + E)\) complexity. This is the default behaviour if no option is given.

purge_edges()[source]#: Remove all edges of the graph which are currently being filtered out. This operation is not reversible.

I/O operations

See Graph I/O for more details.

load(file_name, fmt='auto', ignore_vp=None, ignore_ep=None, ignore_gp=None)[source]#

Load graph from file_name (which can be either a string or a file-like object). The format is guessed from file_name, or can be specified by fmt, which can be either “gt”, “graphml”, “xml”, “dot” or “gml”. (Note that “graphml” and “xml” are synonyms).

If provided, the parameters ignore_vp, ignore_ep and ignore_gp, should contain a list of property names (vertex, edge or graph, respectively) which should be ignored when reading the file.

Warning

The only file formats which are capable of perfectly preserving the internal property maps are “gt” and “graphml”. Because of this, they should be preferred over the other formats whenever possible.

save(file_name, fmt='auto')[source]#: Save graph to file_name (which can be either a string or a file-like object). The format is guessed from the file_name, or can be specified by fmt, which can be either “gt”, “graphml”, “xml”, “dot” or “gml”. (Note that “graphml” and “xml” are synonyms).

Warning

The only file formats which are capable of perfectly preserving the internal property maps are “gt” and “graphml”. Because of this, they should be preferred over the other formats whenever possible.

class graph_tool.GraphView(g, vfilt=None, efilt=None, directed=None, reversed=False, skip_properties=False, skip_vfilt=False, skip_efilt=False)[source]#

Bases: Graph

A view of selected vertices or edges of another graph.

This class uses shared data from another Graph instance, but allows for local filtering of vertices and/or edges, edge directionality or reversal. See Graph views for more details and examples.

The existence of a GraphView object does not affect the original graph, except if the graph view is modified (addition or removal of vertices or edges), in which case the modification is directly reflected in the original graph (and vice-versa), since they both point to the same underlying data. Because of this, instances of PropertyMap can be used interchangeably with a graph and its views.

The argument g must be an instance of a Graph class. If specified, vfilt and efilt select which vertices and edges are filtered, respectively. These parameters can either be a boolean-valued PropertyMap or numpy.ndarray, which specify which vertices/edges are selected, or an unary function that returns True if a given vertex/edge is to be selected, or False otherwise.

The boolean parameter directed can be used to set the directionality of the graph view. If directed is None, the directionality is inherited from g.

If reversed == True, the direction of the edges is reversed.

If vfilt or efilt is anything other than a PropertyMap instance, the instantiation running time is \(O(V)\) and \(O(E)\), respectively. Otherwise, the running time is \(O(1)\).

If either skip_properties, skip_vfilt or skip_efilt is True, then the internal properties, vertex filter or edge filter of the original graph are ignored, respectively.

property base#: Base graph.

class graph_tool.Vertex#

Vertex descriptor.

This class represents a vertex in a Graph instance.

Vertex instances are hashable, and are convertible to integers, corresponding to its index (see vertex_index).

Raises an exception This class cannot be instantiated from Python

all_edges()#: Return an iterator over all edges (both in or out).

all_neighbors()#: Return an iterator over all neighbors (both in or out).

in_degree(weight=None)#: Return the in-degree of the vertex. If provided, weight should be a scalar edge EdgePropertyMap, and the in-degree will correspond to the sum of the weights of the in-edges.

in_edges()#: Return an iterator over the in-edges.

in_neighbors()#: Return an iterator over the in-neighbors.

is_valid()#: Returns True if the descriptor corresponds to an existing vertex in the graph, False otherwise.

out_degree(weight=None)#: Return the out-degree of the vertex. If provided, weight should be a scalar edge EdgePropertyMap, and the out-degree will correspond to the sum of the weights of the out-edges.

out_edges()#: Return an iterator over the out-edges.

out_neighbors()#: Return an iterator over the out-neighbors.

class graph_tool.Edge#

Edge descriptor.

This class represents an edge in a Graph.

Edge instances are hashable, iterable and thus are convertible to a tuple, which contains the source and target vertices.

Raises an exception This class cannot be instantiated from Python

is_valid()#: Returns True if the descriptor corresponds to an existing edge in the graph, False otherwise.

source()#: Returns the source of the edge (a Vertex instance).

target()#: Returns the target of the edge (a Vertex instance).

class graph_tool.PropertyMap(pmap, g, key_type)[source]#

This base class provides a mapping from vertices, edges or whole graphs to arbitrary properties.

See Property maps for more details.

The possible property value types are listed below.

Type name	Alias
`bool`	`uint8_t`
`int16_t`	`short`
`int32_t`	`int`
`int64_t`	`long`, `long long`
`double`	`float`
`long double`
`string`
`vector<bool>`	`vector<uint8_t>`
`vector<int16_t>`	`short`
`vector<int32_t>`	`vector<int>`
`vector<int64_t>`	`vector<long>`, `vector<long long>`
`vector<double>`	`vector<float>`
`vector<long double>`
`vector<string>`
`python::object`	`object`

copy(value_type=None, full=True)[source]#: Return a copy of the property map. If value_type is specified, the value type is converted to the chosen type. If full == False, in the case of filtered graphs only the unmasked values are copied (with the remaining ones taking the type-dependent default value).

coerce_type(full=True)[source]#: Return a copy of the property map with the most appropriate type, i.e. the simplest type necessary to accomodate all the values exactly. If full == False, in the case of filtered graphs only the unmasked values are copied (with the remaining ones taking the type-dependent default value).

get_graph()[source]#: Get the graph class to which the map refers.

key_type()[source]#: Return the key type of the map. Either ‘g’, ‘v’ or ‘e’.

value_type()[source]#: Return the value type of the map.

python_value_type()[source]#: Return the python-compatible value type of the map.

get_array()[source]#: Get a numpy.ndarray subclass (PropertyArray) with the property values.

Note

An array is returned only if the value type of the property map is a scalar. For vector, string or object types, None is returned instead. For vector and string objects, indirect array access is provided via the get_2d_array() and set_2d_array() member functions.

Warning

The returned array does not own the data, which belongs to the property map. Therefore, if the graph changes, the array may become invalid. Do not store the array if the graph is to be modified; store a copy instead.

property a#

Shortcut to the get_array() method as an attribute. This makes assignments more convenient, e.g.:

>>> g = gt.Graph()
>>> g.add_vertex(10)
<...>
>>> prop = g.new_vertex_property("double")
>>> prop.a = np.random.random(10)           # Assignment from array

property fa#

The same as the a attribute, but instead an indexed array is returned, which contains only entries for vertices/edges which are not filtered out. If there are no filters in place, the array is not indexed, and is identical to the a attribute.

Note that because advanced indexing is triggered, a copy of the array is returned, not a view, as for the a attribute. Nevertheless, the assignment of values to the whole array at once works as expected.

property ma#: The same as the a attribute, but instead a numpy.ma.MaskedArray object is returned, which contains only entries for vertices/edges which are not filtered out. If there are no filters in place, a regular PropertyArray is returned, which is identical to the a attribute.

get_2d_array(pos)[source]#: Return a two-dimensional array of shape (M,N), where N is the number of vertices or edges, and M is the size of each property vector, with contains a copy of all entries of the vector-valued property map. The parameter pos must be a sequence of integers which specifies the indexes of the property values which will be copied.

set_2d_array(a, pos=None)[source]#: Set the entries of the vector-valued property map from a two-dimensional array a of shape (M,N), where N is the number of vertices or edges, and M is the size of each property vector. If given, the parameter pos must be a sequence of integers which specifies the indexes of the property values which will be set (i.e. rows if the a matrix).

is_writable()[source]#: Return True if the property is writable.

set_value(val)[source]#: Sets all values in the property map to val.

reserve(size)[source]#: Reserve enough space for size elements in underlying container. If the original size is already equal or larger, nothing will happen.

resize(size)[source]#: Resize the underlying container to contain exactly size elements.

shrink_to_fit()[source]#: Shrink size of underlying container to accommodate only the necessary amount, and thus potentially freeing memory.

swap(other)[source]#: Swap internal storage with other.

data_ptr()[source]#: Return the pointer to memory where the data resides.

class graph_tool.VertexPropertyMap(pmap, g)[source]#

Bases: PropertyMap

This class provides a mapping from vertices to arbitrary properties.

See Property maps and PropertyMap for more details.

class graph_tool.EdgePropertyMap(pmap, g)[source]#

Bases: PropertyMap

This class provides a mapping from edges to arbitrary properties.

See Property maps and PropertyMap for more details.

class graph_tool.GraphPropertyMap(pmap, g)[source]#

Bases: PropertyMap

This class provides a mapping from graphs to arbitrary properties.

See Property maps and PropertyMap for more details.

class graph_tool.PropertyArray(input_array, prop_map)[source]#

Bases: ndarray

This is a numpy.ndarray subclass which keeps a reference of its PropertyMap owner.

property prop_map#: PropertyMap owner instance.

I/O functions

graph_tool.load_graph(file_name, fmt='auto', ignore_vp=None, ignore_ep=None, ignore_gp=None)[source]#

Load a graph from file_name (which can be either a string or a file-like object).

The format is guessed from file_name, or can be specified by fmt, which can be either “gt”, “graphml”, “xml”, “dot” or “gml”. (Note that “graphml” and “xml” are synonyms).

If provided, the parameters ignore_vp, ignore_ep and ignore_gp, should contain a list of property names (vertex, edge or graph, respectively) which should be ignored when reading the file.

Warning

graph_tool.load_graph_from_csv(file_name, directed=False, eprop_types=None, eprop_names=None, hashed=True, hash_type='string', skip_first=False, strip_whitespace=True, ecols=(0, 1), csv_options={'delimiter': ',', 'quotechar': '"'})[source]#

Load a graph from a csv file containing a list of edges and edge properties.

Parameters:

file_namestr or file-like object: File in :mod:csv format, with edges given in each row.
directedbool (optional, default: False): Whether or not the graph is directed.
eprop_typeslist of str (optional, default: None): List of edge property types to be read from remaining columns (if this is None, all properties will be of type string.
eprop_nameslist of str (optional, default: None): List of edge property names to be used for the remaining columns (if this is None, and skip_first is True their values will be obtained from the first line, otherwise they will be called c1, c2, ...).
hashedbool (optional, default: True): If True the vertex values in the edge list are not assumed to correspond to vertex indices directly. In this case they will be mapped to vertex indices according to the order in which they are encountered, and a vertex property map with the vertex values is returned.
hash_typestr (optional, default: string): If hashed == True, this will determined the type of the vertex values. It can be any property map value type (see PropertyMap).
skip_firstbool (optional, default: False): If True the first line of the file will be skipped.
strip_whitespacebool (optional, default: True): If True whitespace will be striped from the start and end of values, before processing them.
ecolspair of int (optional, default: (0,1)): Line columns used as source and target for the edges.
csv_optionsdict (optional, default: {"delimiter": ",", "quotechar": '"'}): Options to be passed to the csv.reader() parser.

Returns:

gGraph: The loaded graph. It will contain additional columns in the file as internal edge property maps. If hashed == True, it will also contain an internal vertex property map with the vertex names.

Property map operations

graph_tool.group_vector_property(props, value_type=None, vprop=None, pos=None)[source]#

Group list of properties props into a vector property map of the same type.

Parameters:

propslist of PropertyMap: Properties to be grouped.
value_typestring (optional, default: None): If supplied, defines the value type of the grouped property.
vpropPropertyMap (optional, default: None): If supplied, the properties are grouped into this property map.
poslist of ints (optional, default: None): If supplied, should contain a list of indexes where each corresponding element of props should be inserted.

Returns:

vpropPropertyMap: A vector property map with the grouped values of each property map in props.

Examples

>>> from numpy.random import seed, randint
>>> from numpy import array
>>> seed(42)
>>> gt.seed_rng(42)
>>> g = gt.random_graph(100, lambda: (3, 3))
>>> props = [g.new_vertex_property("int") for i in range(3)]
>>> for i in range(3):
...    props[i].a = randint(0, 100, g.num_vertices())
>>> gprop = gt.group_vector_property(props)
>>> print(gprop[g.vertex(0)].a)
[51 25  8]
>>> print(array([p[g.vertex(0)] for p in props]))
[51 25  8]

graph_tool.ungroup_vector_property(vprop, pos, props=None)[source]#

Ungroup vector property map vprop into a list of individual property maps.

Parameters:

vpropPropertyMap: Vector property map to be ungrouped.
poslist of ints: A list of indexes corresponding to where each element of vprop should be inserted into the ungrouped list.
propslist of PropertyMap (optional, default: None): If supplied, should contain a list of property maps to which vprop should be ungroupped.

Returns:

propslist of PropertyMap: A list of property maps with the ungrouped values of vprop.

Examples

>>> from numpy.random import seed, randint
>>> from numpy import array
>>> seed(42)
>>> gt.seed_rng(42)
>>> g = gt.random_graph(100, lambda: (3, 3))
>>> prop = g.new_vertex_property("vector<int>")
>>> for v in g.vertices():
...    prop[v] = randint(0, 100, 3)
>>> uprops = gt.ungroup_vector_property(prop, [0, 1, 2])
>>> print(prop[g.vertex(0)].a)
[51 92 14]
>>> print(array([p[g.vertex(0)] for p in uprops]))
[51 92 14]

graph_tool.map_property_values(src_prop, tgt_prop, map_func)[source]#

Map the values of src_prop to tgt_prop according to the mapping function map_func.

Parameters:

src_propPropertyMap: Source property map.
tgt_propPropertyMap: Target property map.
map_funcfunction or callable object: Function mapping values of src_prop to values of tgt_prop.

Returns:

None

Examples

>>> g = gt.collection.data["lesmis"]
>>> label_len = g.new_vertex_property("int64_t")
>>> gt.map_property_values(g.vp.label, label_len,
...                        lambda x: len(x))
>>> print(label_len.a)
[ 6  8 14 11 12  8 12  8  5  6  7  7 10  6  7  7  9  9  7 11  9  6  7  7
 13 10  7  6 12 10  8  8 11  6  5 12  6 10 11  9 12  7  7  6 14  7  9  9
  8 12  6 16 12 11 14  6  9  6  8 10  9  7 10  7  7  4  9 14  9  5 10 12
  9  6  6  6 12]

graph_tool.infect_vertex_property(g, prop, vals=None)[source]#

Propagate the prop values of vertices with value val to all their out-neighbors.

Parameters:

propVertexPropertyMap: Property map to be modified.
valslist (optional, default: None): List of values to be propagated. If not provided, all values will be propagated.

Returns:

NoneNone

Examples

>>> from numpy.random import seed
>>> seed(42)
>>> gt.seed_rng(42)
>>> g = gt.random_graph(100, lambda: (3, 3))
>>> prop = g.vertex_index.copy("int32_t")
>>> gt.infect_vertex_property(g, prop, [10])
>>> print(sum(prop.a == 10))
4

graph_tool.edge_endpoint_property(g, prop, endpoint, eprop=None)[source]#

Return an edge property map corresponding to the vertex property prop of either the target and source of the edge, according to endpoint.

Parameters:

propVertexPropertyMap: Vertex property map to be used to propagated to the edge.
endpoint“source” or “target”: Edge endpoint considered. If the graph is undirected, the source is always the vertex with the lowest index.
epropEdgePropertyMap (optional, default: None): If provided, the resulting edge properties will be stored here.

Returns:

epropEdgePropertyMap: Propagated edge property.

Examples

>>> gt.seed_rng(42)
>>> g = gt.random_graph(100, lambda: (3, 3))
>>> esource = gt.edge_endpoint_property(g, g.vertex_index, "source")
>>> print(esource.a)
[50 71 36 51 86 43 54 92 24 32 85 98 94 72 76  8 12 16 36 91 10 15 22 76
67 50 64 30 75 48  3  4 57 37 11 29 71 57 65 92 48 75 27 47 21 12 86
21 77 73  1 34 63 53 99 14 68 88 53 47  5 38 18 57 79 34 33 32 31  2
24  4 33 49 87 60  1 50 62 39 83 97  0 94 84 56 28 28 30  7 61 67 97
81 69 78 64  6 82 75 26 66 19 28 14 90  1 17 45 23 46 84 58 36 45 72
55  5 34 17 38 20 85 85 99 13 61 17 55 22 18 74 69 91 32 42  9 11 23
31  7 29 78 80 93 60 54 31 49 99 73 77 18 46 68 76 95 21 35 70 89 83
40 67 70 89  9 40 82 16  5 73 52 41 90  3 45 87 40 35 89 51 49 87 20
64 65 70 74 16 62  3 15 98 41 78 56  2 44 94 95 13 62 69 81 55 90 96
97 98 96 77 44 25 10 74 88 88  8 58 37 68  7 83 30  6 96 51 54 15 20
58 61 93 19  0 79 93 26 84 91 39  9 72 59 27 65 63 80 39 33 48 43 35
19 42 12 22 25 44 24 41 59 79  0 13 80 11 47  8 26 53  2 29 82 14 23
95 27 25  4 92 71 60 43 59 37 66]

graph_tool.incident_edges_op(g, direction, op, eprop, vprop=None)[source]#

Return a vertex property map corresponding to a specific operation (sum, product, min or max) on the edge property eprop of incident edges on each vertex, following the direction given by direction.

Parameters:

direction“in” or “out”: Direction of the incident edges.
op“sum”, “prod”, “min” or “max”: Operation performed on incident edges.
epropEdgePropertyMap: Edge property map to be summed.
vpropVertexPropertyMap (optional, default: None): If provided, the resulting vertex properties will be stored here.

Returns:

vpropVertexPropertyMap: Resulting vertex property.

Examples

>>> gt.seed_rng(42)
>>> g = gt.random_graph(100, lambda: (3, 3))
>>> vsum = gt.incident_edges_op(g, "out", "sum", g.edge_index)
>>> print(vsum.a)
[605 241 559 412 398 361 623 469 522 566 459 455 329 615 451 459 390 367
 357 615 556 257 424 543 352 782 633 588 286 467 352 368 217 403 243 613
 137 561 236 592 528 654 646 563 697 413 417 384 332 419 106 427 299 397
 395 467 556 136 585 824 524 466 489 383 320 489 596 289 448 446 531 332
 385 385 556 174 198 427 450 586 684 477 562 482 451 265 171 451 510 525
 504 407 340 640 305 659 669 396 430 340]

graph_tool.perfect_prop_hash(props, htype='int32_t')[source]#: Given a list of property maps props of the same type, a derived list of property maps with integral type htype is returned, where each value is replaced by a perfect (i.e. unique) hash value.

Note

The hash value is deterministic, but it will not be necessarily the same for different values of props.

graph_tool.value_types()[source]#: Return a list of possible properties value types.

OpenMP control

graph_tool.openmp_enabled()[source]#: Return True if OpenMP was enabled during compilation.

graph_tool.openmp_get_num_threads()[source]#: Return the number of OpenMP threads.

graph_tool.openmp_set_num_threads(n)[source]#: Set the number of OpenMP threads.

graph_tool.openmp_get_schedule()[source]#: Return the runtime OpenMP schedule and chunk size. The schedule can by any of: "static", "dynamic", "guided", "auto".

graph_tool.openmp_set_schedule(schedule, chunk=0)[source]#: Set the runtime OpenMP schedule and chunk size. The schedule can by any of: "static", "dynamic", "guided", "auto".

Misc

graph_tool.show_config()[source]#: Show graph_tool build configuration.

graph_tool - Core module#

Summary#

How to use the documentation#

Contents#

Available subpackages#

`graph_tool` - Core module#