// named parameter version
template <class Graph, class ComponentMap, class P, class T, class R>
typename property_traits<ComponentMap>::value_type
strong_components(Graph& g, ComponentMap comp,
const bgl_named_params<P, T, R>& params = all defaults)
// there is not a non-named parameter version of this function
сильная_компоненты()функции вычисляют сильно связанные компоненты направленного графа с использованием алгоритма Тарьяна на основе DFS [41].
Выход алгоритма записывается в карту свойств компонентовcomp, которая будет содержать числа, дающие идентификатор компонента, назначенный каждой вершине. Количество компонентов является обратным значением функции.
A strongly connected
component of a directed graph G=(V,E) is a maximal
set of vertices U which is in V such that for every pair
of vertices u and v in U, we have both a path
from u to v and path from v to u. That is
to say that u and v are reachable from each other.
Parameters
IN: const Graph& g
A directed graph. The graph type must be a model of Vertex List Graph and Incidence Graph. Python: The parameter is named graph.
OUT: ComponentMap c
The algorithm computes how many connected components are in the graph,
and assigning each component an integer label. The algorithm then
records which component each vertex in the graph belongs to by
recording the component number in the component property map. The
ComponentMap type must be a model of Writable Property
Map. The value type shouch be an integer type, preferably the same
as the vertices_size_type of the graph. The key type must be
the graph's vertex descriptor type. Python: Must be an vertex_int_map for the graph. Python default: graph.get_vertex_int_map("component")
Named Parameters
UTIL: root_map(RootMap r_map)
This is used by the algorithm to record the candidate root vertex for
each vertex. By the end of the algorithm, there is a single root vertex
for each component and get(r_map, v) returns the root
vertex for whichever component vertex v is a member.
The RootMap must be a
Read/Write Property Map, where the key type and the value type are
the vertex descriptor type of the graph. Default: an iterator_property_map created from a
std::vector of vertex descriptors of size
num_vertices(g) and using the i_map for the index
map. Python: Unsupported parameter.
UTIL: discover_time_map(TimeMap t_map)
This is used by the algorithm to keep track of the DFS ordering
of the vertices. The TimeMap must be a model
of Read/Write
Property Map and its value type must be an integer type. The key
type must be the vertex descriptor type of the graph. Default:an iterator_property_map created from a
std::vector of integers with size
num_vertices(g) and using the i_map for the index
map. Python: Unsupported parameter.
UTIL: color_map(ColorMap c_map)
This is used by the algorithm to keep track of its progress through
the graph. The type ColorMap must be a model of Read/Write
Property Map and its key type must be the graph's vertex
descriptor type and the value type of the color map must model
ColorValue. Default: an iterator_property_map created from a
std::vector of default_color_type of size
num_vertices(g) and using the i_map for the index
map. Python: Unsupported parameter.
IN: vertex_index_map(VertexIndexMap i_map)
This maps each vertex to an integer in the range [0,
num_vertices(g)). This parameter is only necessary when a
default is used for one of the other named parameters. The type
VertexIndexMap must be a model of Readable Property
Map. The value type of the map must be an integer type. The
vertex descriptor type of the graph needs to be usable as the key
type of the map. Default:get(vertex_index, g)
Note: if you use this default, make sure your graph has
an internal vertex_index property. For example,
adjacency_list with VertexList=listS does
not have an internal vertex_index property.
Python: Unsupported parameter.
Complexity
The time complexity for the strongly connected components algorithm is
O(V + E).
Статья Boost Graph Library: Strongly Connected Components раздела может быть полезна для разработчиков на c++ и boost.
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