#include <xnetwork/classes/graph.hpp>
template<typename _nodeview_t, typename adjlist_t = py::set<Value_
Base class for undirected graphs.
A Graph stores nodes and edges with optional data, or attributes.
Graphs hold undirected edges. Self loops are allowed but multiple (parallel) edges are not.
Nodes can be arbitrary (hashable) C++ objects with optional key/value attributes. By convention None is not used as a node.
Edges are represented as links between nodes with optional key/value attributes.
Parameters
node_container : input graph (optional, default: None) Data to initialize graph. If None (default) an empty graph is created. The data can be any format that is supported by the to_networkx_graph() function, currently including edge list, dict of dicts, dict of lists, NetworkX graph, NumPy matrix or 2d ndarray, SciPy sparse matrix, or PyGraphviz graph.
See Also
DiGraph MultiGraph MultiDiGraph OrderedGraph
Examples
Create an empty graph structure (a "null graph") with 5 nodes and no edges.
> auto v = std::vector{3, 4, 2, 8};
> auto gra = nx.Graph(v);
> auto va = py::dict{{3, 0.1}, {4, 0.5}, {2, 0.2}};
> auto gra = nx.Graph(va);
> auto r = py::range(100);
> auto gra = nx.Graph(r);
gra can be grown in several ways.
Nodes:**
Add one node at a time:
> gra.add_node(1)
Add the nodes from any container (a list, dict, set or even the lines from a file or the nodes from another graph).
> gra.add_nodes_from([2, 3]) > gra.add_nodes_from(range(100, 110)) > H = nx.path_graph(10) > gra.add_nodes_from(H)
In addition to strings and integers any hashable C++ object (except None) can represent a node, e.g. a customized node object, or even another Graph.
> gra.add_node(H)
Edges:**
gra can also be grown by adding edges.
Add one edge,
> gra.add_edge(1, 2);
a list of edges,
> gra.add_edges_from([(1, 2), (1, 3)]);
or a collection of edges,
> gra.add_edges_from(H.edges());
If some edges connect nodes not yet in the graph, the nodes are added automatically. There are no errors when adding nodes or edges that already exist.
Attributes:**
Each graph can hold key/value attribute pairs in an associated attribute dictionary (the keys must be hashable). By default these are empty, but can be added or changed using direct manipulation of the attribute dictionaries named graph, node and edge respectively.
> gra.graph["day"] = std::any("Friday");
{'day': 'Friday'}
Subclasses (Advanced):**
The Graph class uses a container-of-container-of-container data structure. The outer dict (node_dict) holds adjacency information keyed by node. The next dict (adjlist_dict) represents the adjacency information and holds edge data keyed by neighbor. The inner dict (edge_attr_dict) represents the edge data and holds edge attribute values keyed by attribute names.
Each of these three dicts can be replaced in a subclass by a user defined dict-like object. In general, the dict-like features should be maintained but extra features can be added. To replace one of the dicts create a new graph class by changing the class(!) variable holding the factory for that dict-like structure. The variable names are node_dict_factory, node_attr_dict_factory, adjlist_inner_dict_factory, adjlist_outer_dict_factory, edge_attr_dict_factory and graph_attr_dict_factory.
node_dict_factory : function, (default: dict) Factory function to be used to create the dict containing node attributes, keyed by node id. It should require no arguments and return a dict-like object
node_attr_dict_factory: function, (default: dict) Factory function to be used to create the node attribute dict which holds attribute values keyed by attribute name. It should require no arguments and return a dict-like object
adjlist_outer_dict_factory : function, (default: dict) Factory function to be used to create the outer-most dict in the data structure that holds adjacency info keyed by node. It should require no arguments and return a dict-like object.
adjlist_inner_dict_factory : function, (default: dict) Factory function to be used to create the adjacency list dict which holds edge data keyed by neighbor. It should require no arguments and return a dict-like object
edge_attr_dict_factory : function, (default: dict) Factory function to be used to create the edge attribute dict which holds attribute values keyed by attribute name. It should require no arguments and return a dict-like object.
graph_attr_dict_factory : function, (default: dict) Factory function to be used to create the graph attribute dict which holds attribute values keyed by attribute name. It should require no arguments and return a dict-like object.
Typically, if your extension doesn't impact the data structure all methods will inherit without issue except: to_directed/to_undirected. By default these methods create a DiGraph/Graph class and you probably want them to create your extension of a DiGraph/Graph. To facilitate this we define two class variables that you can set in your subclass.
to_directed_class : callable, (default: DiGraph or MultiDiGraph) Class to create a new graph structure in the to_directed method. If None, a NetworkX class (DiGraph or MultiDiGraph) is used.
to_undirected_class : callable, (default: Graph or MultiGraph) Class to create a new graph structure in the to_undirected method. If None, a NetworkX class (Graph or MultiGraph) is used.
Examples
Create a low memory graph class that effectively disallows edge attributes by using a single attribute dict for all edges. This reduces the memory used, but you lose edge attributes.
> class ThinGraph(nx.Graph):
... all_edge_dict = {'weight': 1} ... def single_edge_dict(self): ... return self.all_edge_dict ... edge_attr_dict_factory = single_edge_dict > gra = ThinGraph() > gra.add_edge(2, 1) > gra[2][1] {'weight': 1} > gra.add_edge(2, 2) > gra[2][1] is gra[2][2] True
Please see :mod:~networkx.classes.ordered for more examples of creating graph subclasses by overwriting the base class dict with a dictionary-like object.
Public types
- using nodeview_t = _nodeview_t
- using Node = typename nodeview_t::value_type
- using adjlist_inner_dict_factory = adjlist_t
- using key_type = typename adjlist_t::key_type
- using value_type = typename adjlist_t::value_type
- using edge_t = std::pair<Node, Node>
- using node_t = Node
Public static functions
-
static auto end_points(edge_
t& e) -> edge_t & -> auto - For compatible with BGL adaptor.
-
static auto end_points(const edge_
t& e) -> const edge_t & -> auto - For compatible with BGL adaptor.
Constructors, destructors, conversion operators
-
Graph(const nodeview_
t& Nodes) explicit - Graph(uint32_t num_nodes) explicit
Public functions
- auto adj() const -> auto
- auto adj() -> auto
- auto _nodes_nbrs() const -> auto
- auto begin() const -> auto
- auto end() const -> auto
- auto contains(const Node& n) -> bool -> auto
- auto operator[](const Node& n) const -> const auto & -> auto
- auto at(const Node& n) const -> const auto & -> auto
- auto operator[](const Node& n) -> auto & -> auto
- auto nodes() -> auto
- auto number_of_nodes() const -> size_t -> auto
- auto order() -> auto
- auto size() const -> size_t -> auto
- auto has_node(const Node& n) const -> bool -> auto
-
template<typename U = key_auto add_edge(const Node& u, const Node& v) -> typename std::enable_if< std::is_same< U, value_type >::value >::type -> auto
type> -
template<typename U = key_auto add_edge(const Node& u, const Node& v) -> typename std::enable_if<!std::is_same< U, value_type >::value >::type -> auto
type> -
template<typename T>auto add_edge(const Node& u, const Node& v, const T& data) -> auto
-
template<typename C1>auto add_edges_from(const C1& edges) -> auto
-
template<typename C1, typename C2>auto add_edges_from(const C1& edges, const C2& data) -> auto
- auto has_edge(const Node& u, const Node& v) -> bool -> auto
- auto degree(const Node& n) const -> auto
- auto clear() -> auto
- auto is_multigraph() -> auto
- auto is_directed() -> auto
Public variables
- size_t _num_of_edges
-
nodeview_
t _node - adjlist_outer_dict_factory _adj
Function documentation
template<typename _nodeview_t, typename adjlist_t, typename adjlist_outer_dict_factory>
static auto xnetwork::
For compatible with BGL adaptor.
| Parameters | |
|---|---|
| e in | |
| Returns | edge_t& |
template<typename _nodeview_t, typename adjlist_t, typename adjlist_outer_dict_factory>
static auto xnetwork::
For compatible with BGL adaptor.
| Parameters | |
|---|---|
| e in | |
| Returns | edge_t& |
template<typename _nodeview_t, typename adjlist_t, typename adjlist_outer_dict_factory>
xnetwork::
Initialize a graph with edges, name, or graph attributes.
Parameters
node_container : input nodes
Examples
> v = std::vector{5, 3, 2}; > gra = nx.Graph(v); // or DiGraph, MultiGraph, MultiDiGraph, etc
> r = py::range(100); > gra = nx.Graph(r); // or DiGraph, MultiGraph, MultiDiGraph, etc
template<typename _nodeview_t, typename adjlist_t, typename adjlist_outer_dict_factory>
auto xnetwork::
Graph adjacency object holding the neighbors of each node.
This object is a read-only dict-like structure with node keys and neighbor-dict values. The neighbor-dict is keyed by neighbor to the edge-data-dict. So ‘gra.adj[3][2]['color’] = 'blue'sets the color of the edge(3, 2)to"blue"`.
Iterating over gra.adj behaves like a dict. Useful idioms include for nbr, datadict in gra.adj[n].items():.
The neighbor information is also provided by subscripting the graph. So ‘for nbr, foovalue in gra[node].data('foo’, default=1):` works.
For directed graphs, gra.adj holds outgoing (successor) info.
template<typename _nodeview_t, typename adjlist_t, typename adjlist_outer_dict_factory>
auto xnetwork::
String identifier of the graph.
This graph attribute appears : the attribute dict gra.graph keyed by the string "name". as well as an attribute (technically a property) gra.name. This is entirely user controlled. Iterate over the nodes. Use: "for (const auto& n : gra)".
Returns
niter : iterator An iterator over all nodes : the graph.
Examples
> gra = nx.path_graph(4); // or DiGraph, MultiGraph, MultiDiGraph, etc > [n for n : gra]; [0, 1, 2, 3]; > list(gra); [0, 1, 2, 3];
template<typename _nodeview_t, typename adjlist_t, typename adjlist_outer_dict_factory>
auto xnetwork::
Return true if (n is a node, false otherwise. Use: "n : gra".
Examples
> gra = nx.path_graph(4); // or DiGraph, MultiGraph, MultiDiGraph, etc > 1 : gra true
template<typename _nodeview_t, typename adjlist_t, typename adjlist_outer_dict_factory>
auto xnetwork::
Return a dict of neighbors of node n. Use: "gra[n]".
Parameters
n : node A node in the graph.
Returns
adj_dict : dictionary The adjacency dictionary for nodes connected to n.
Notes
gra[n] is the same as gra.adj[n] and similar to gra.neighbors(n); (which is an iterator over gra.adj[n]);
Examples
> gra = nx.path_graph(4); // or DiGraph, MultiGraph, MultiDiGraph, etc > gra[0]; AtlasView({1: {}});
template<typename _nodeview_t, typename adjlist_t, typename adjlist_outer_dict_factory>
auto xnetwork::
A NodeView of the Graph as gra.nodes().
Returns
NodeView Allows set-like operations over the nodes as well as node attribute dict lookup and calling to get a NodeDataView. A NodeDataView iterates over (n, data) and has no set operations. A NodeView iterates over n and includes set operations.
When called, if (data == false, an iterator over nodes. Otherwise an iterator of 2-tuples (node, attribute value); where the attribute is specified : data. If data is true then the attribute becomes the entire data dictionary.
Notes
If your node data is not needed, it is simpler and equivalent to use the expression for n : gra, or list(gra).
Examples
There are two simple ways of getting a list of all nodes : the graph) {
> gra = nx.path_graph(3); > list(gra.nodes);
[0, 1, 2]; > list(gra); [0, 1, 2];
To get the node data along with the nodes) {
> gra.add_node(1, time="5pm"); > gra.nodes[0]["foo"] = "bar"; > list(gra.nodes(data=true));
[(0, {"foo": "bar"}), (1, {"time": "5pm"}), (2, {})]; > list(gra.nodes.data()); [(0, {"foo": "bar"}), (1, {"time": "5pm"}), (2, {})];
> list(gra.nodes(data="foo"));
[(0, "bar"), (1, None), (2, None)]; > list(gra.nodes.data("foo")); [(0, "bar"), (1, None), (2, None)];
> list(gra.nodes(data="time"));
[(0, None), (1, "5pm"), (2, None)]; > list(gra.nodes.data("time")); [(0, None), (1, "5pm"), (2, None)];
> list(gra.nodes(data="time", default="Not Available"));
[(0, "Not Available"), (1, "5pm"), (2, "Not Available")]; > list(gra.nodes.data("time", default="Not Available")); [(0, "Not Available"), (1, "5pm"), (2, "Not Available")];
If some of your nodes have an attribute and the rest are assumed to have a default attribute value you can create a dictionary from node/attribute pairs using the default keyword argument to guarantee the value is never None:) {
> gra = nx.Graph();
> gra.add_node(0);
> gra.add_node(1, weight=2);
> gra.add_node(2, weight=3);
> dict(gra.nodes(data="weight", default=1));
{0: 1, 1: 2, 2: 3}
template<typename _nodeview_t, typename adjlist_t, typename adjlist_outer_dict_factory>
auto xnetwork::
Return the number of nodes : the graph.
Returns
nnodes : int The number of nodes : the graph.
See Also
order, size which are identical
Examples
> gra = nx.path_graph(3); // or DiGraph, MultiGraph, MultiDiGraph, etc > len(gra); 3
template<typename _nodeview_t, typename adjlist_t, typename adjlist_outer_dict_factory>
auto xnetwork::
Return the number of nodes : the graph.
Returns
nnodes : int The number of nodes : the graph.
See Also
number_of_nodes, size which are identical
template<typename _nodeview_t, typename adjlist_t, typename adjlist_outer_dict_factory>
auto xnetwork::
Return the number of nodes : the graph.
Returns
nnodes : int The number of nodes : the graph.
See Also
number_of_nodes, order which are identical
template<typename _nodeview_t, typename adjlist_t, typename adjlist_outer_dict_factory>
auto xnetwork::
Return true if (the graph contains the node n.
Identical to n : gra
Parameters
n : node
Examples
> gra = nx.path_graph(3); // or DiGraph, MultiGraph, MultiDiGraph, etc > gra.has_node(0); true
template<typename _nodeview_t, typename adjlist_t, typename adjlist_outer_dict_factory>
template<typename U = key_
Add an edge between u and v.
The nodes u and v will be automatically added if (they are not already : the graph.
Edge attributes can be specified with keywords or by directly accessing the edge"s attribute dictionary. See examples below.
Parameters
u, v : nodes Nodes can be, for example, strings or numbers. Nodes must be hashable (and not None) C++ objects.
See Also
add_edges_from : add a collection of edges
Notes
Adding an edge that already exists updates the edge data.
Many XNetwork algorithms designed for weighted graphs use an edge attribute (by default weight) to hold a numerical value.
Examples
The following all add the edge e=(1, 2) to graph gra) {
> gra = nx.Graph() // or DiGraph, MultiGraph, MultiDiGraph, etc > e = (1, 2); > gra.add_edge(1, 2) // explicit two-node form > gra.add_edges_from([(1, 2)]); // add edges from iterable
container
Associate data to edges using keywords) {
> gra.add_edge(1, 2);
For non-string attribute keys, use subscript notation.
> gra.add_edge(1, 2);
> gra[1][2].update({0: 5});
> gra.edges()[1, 2].update({0: 5});
template<typename _nodeview_t, typename adjlist_t, typename adjlist_outer_dict_factory>
auto xnetwork::
Return true if (the edge (u, v) is : the graph.
This is the same as v : gra[u] without KeyError exceptions.
Parameters
u, v : nodes Nodes can be, for example, strings or numbers. Nodes must be hashable (and not None) C++ objects.
Returns
edge_ind : bool true if (edge is : the graph, false otherwise.
Examples
> gra = nx.path_graph(4); // or DiGraph, MultiGraph, MultiDiGraph, etc > gra.has_edge(0, 1); // using two nodes true > e = (0, 1); > gra.has_edge(*e); // e is a 2-tuple (u, v); true > e = (0, 1, {"weight":7}); > gra.has_edge(*e[:2]); // e is a 3-tuple (u, v, data_dictionary); true
The following syntax are equivalent) {
> gra.has_edge(0, 1);
true > 1 : gra[0]; // though this gives KeyError if (0 not : gra true
template<typename _nodeview_t, typename adjlist_t, typename adjlist_outer_dict_factory>
auto xnetwork::
An EdgeView of the Graph as gra.edges().
edges( nbunch=None, data=false, default=None);
The EdgeView provides set-like operations on the edge-tuples as well as edge attribute lookup. When called, it also provides an EdgeDataView object which allows control of access to edge attributes (but does not provide set-like operations). Hence, gra.edges[u, v]["color"] provides the value of the color attribute for edge (u, v) while for (auto [u, v, c] : gra.edges.data("color", default="red") { iterates through all the edges yielding the color attribute with default "red" if (no color attribute exists.
Parameters
nbunch : single node, container, or all nodes (default= all nodes); The view will only report edges incident to these nodes. data : string or bool, optional (default=false); The edge attribute returned : 3-tuple (u, v, ddict[data]). If true, return edge attribute dict : 3-tuple (u, v, ddict). If false, return 2-tuple (u, v). default : value, optional (default=None); Value used for edges that don"t have the requested attribute. Only relevant if (data is not true or false.
Returns
edges : EdgeView A view of edge attributes, usually it iterates over (u, v); or (u, v, d) tuples of edges, but can also be used for attribute lookup as edges[u, v]["foo"].
Notes
Nodes : nbunch that are not : the graph will be (quietly) ignored. For directed graphs this returns the out-edges.
Examples
> gra = nx.path_graph(3) // or MultiGraph, etc > gra.add_edge(2, 3, weight=5); > [e for e : gra.edges]; [(0, 1), (1, 2), (2, 3)]; > gra.edges.data(); // default data is {} (empty dict); EdgeDataView([(0, 1, {}), (1, 2, {}), (2, 3, {"weight": 5})]); > gra.edges.data("weight", default=1); EdgeDataView([(0, 1, 1), (1, 2, 1), (2, 3, 5)]); > gra.edges([0, 3]); // only edges incident to these nodes EdgeDataView([(0, 1), (3, 2)]); > gra.edges(0); // only edges incident to a single node (use gra.adj[0]?); EdgeDataView([(0, 1)]); An OutEdgeView of the DiGraph as gra.edges().
edges(self, nbunch=None, data=False, default=None)
The OutEdgeView provides set-like operations on the edge-tuples as well as edge attribute lookup. When called, it also provides an EdgeDataView object which allows control of access to edge attributes (but does not provide set-like operations). Hence, ‘gra.edges()[u, v]['color’]provides the value of the color attribute for edge(u, v)whilefor (u, v, c) in gra.edges().data('color', default='red'):` iterates through all the edges yielding the color attribute with default ‘'red’` if no color attribute exists.
Parameters
nbunch : single node, container, or all nodes (default= all nodes) The view will only report edges incident to these nodes. data : string or bool, optional (default=False) The edge attribute returned in 3-tuple (u, v, ddict[data]). If True, return edge attribute dict in 3-tuple (u, v, ddict). If False, return 2-tuple (u, v). default : value, optional (default=None) Value used for edges that don't have the requested attribute. Only relevant if data is not True or False.
Returns
edges : OutEdgeView A view of edge attributes, usually it iterates over (u, v) or (u, v, d) tuples of edges, but can also be used for attribute lookup as ‘edges[u, v]['foo’]`.
See Also
in_edges, out_edges
Notes
Nodes in nbunch that are not in the graph will be (quietly) ignored. For directed graphs this returns the out-edges.
Examples
> gra = nx.DiGraph() # or MultiDiGraph, etc > nx.add_path(gra, [0, 1, 2]) > gra.add_edge(2, 3, weight=5) > [e for e in gra.edges()] [(0, 1), (1, 2), (2, 3)] > gra.edges().data() # default data is {} (empty dict) OutEdgeDataView([(0, 1, {}), (1, 2, {}), (2, 3, {'weight': 5})]) > gra.edges().data('weight', default=1) OutEdgeDataView([(0, 1, 1), (1, 2, 1), (2, 3, 5)]) > gra.edges()([0, 2]) # only edges incident to these nodes OutEdgeDataView([(0, 1), (2, 3)]) > gra.edges()(0) # only edges incident to a single node (use gra.adj[0]?) OutEdgeDataView([(0, 1)]) Remove all nodes and edges from the graph.
This also removes the name, and all graph, node, and edge attributes.
Examples
> gra = nx.path_graph(4); // or DiGraph, MultiGraph, MultiDiGraph, etc > gra.clear(); > list(gra.nodes); []; > list(gra.edges()); [];
template<typename _nodeview_t, typename adjlist_t, typename adjlist_outer_dict_factory>
auto xnetwork::
Return true if (graph is a multigraph, false otherwise.
template<typename _nodeview_t, typename adjlist_t, typename adjlist_outer_dict_factory>
auto xnetwork::
Return true if (graph is directed, false otherwise.