networkx.algorithms.minors.quotient_graph¶

quotient_graph(G, partition, edge_relation=None, node_data=None, edge_data=None, relabel=False, create_using=None)[source]¶

Returns the quotient graph of G under the specified equivalence relation on nodes.

Parameters:	G (NetworkX graph) – The graph for which to return the quotient graph with the specified node relation. partition (function or list of sets) – If a function, this function must represent an equivalence relation on the nodes of `G`. It must take two arguments u and v and return True exactly when u and v are in the same equivalence class. The equivalence classes form the nodes in the returned graph. If a list of sets, the list must form a valid partition of the nodes of the graph. That is, each node must be in exactly one block of the partition. edge_relation (Boolean function with two arguments) – This function must represent an edge relation on the blocks of `G` in the partition induced by `node_relation`. It must take two arguments, B and C, each one a set of nodes, and return True exactly when there should be an edge joining block B to block C in the returned graph. If `edge_relation` is not specified, it is assumed to be the following relation. Block B is related to block C if and only if some node in B is adjacent to some node in C, according to the edge set of `G`. edge_data (function) – This function takes two arguments, B and C, each one a set of nodes, and must return a dictionary representing the edge data attributes to set on the edge joining B and C, should there be an edge joining B and C in the quotient graph (if no such edge occurs in the quotient graph as determined by `edge_relation`, then the output of this function is ignored). If the quotient graph would be a multigraph, this function is not applied, since the edge data from each edge in the graph `G` appears in the edges of the quotient graph. node_data (function) – This function takes one argument, B, a set of nodes in `G`, and must return a dictionary representing the node data attributes to set on the node representing B in the quotient graph. If None, the following node attributes will be set: ‘graph’, the subgraph of the graph `G` that this block represents, ‘nnodes’, the number of nodes in this block, ‘nedges’, the number of edges within this block, ‘density’, the density of the subgraph of `G` that this block represents. relabel (bool) – If True, relabel the nodes of the quotient graph to be nonnegative integers. Otherwise, the nodes are identified with `frozenset` instances representing the blocks given in `partition`. create_using (NetworkX graph constructor, optional (default=nx.Graph)) – Graph type to create. If graph instance, then cleared before populated.
Returns:	The quotient graph of `G` under the equivalence relation specified by `partition`. If the partition were given as a list of `set` instances and `relabel` is False, each node will be a `frozenset` corresponding to the same `set`.
Return type:	NetworkX graph
Raises:	`NetworkXException` – If the given partition is not a valid partition of the nodes of `G`.

Examples

The quotient graph of the complete bipartite graph under the “same neighbors” equivalence relation is K_2. Under this relation, two nodes are equivalent if they are not adjacent but have the same neighbor set:

>>> import networkx as nx
>>> G = nx.complete_bipartite_graph(2, 3)
>>> same_neighbors = lambda u, v: (u not in G[v] and v not in G[u]
...                                and G[u] == G[v])
>>> Q = nx.quotient_graph(G, same_neighbors)
>>> K2 = nx.complete_graph(2)
>>> nx.is_isomorphic(Q, K2)
True

The quotient graph of a directed graph under the “same strongly connected component” equivalence relation is the condensation of the graph (see condensation()). This example comes from the Wikipedia article `Strongly connected component`_:

>>> import networkx as nx
>>> G = nx.DiGraph()
>>> edges = ['ab', 'be', 'bf', 'bc', 'cg', 'cd', 'dc', 'dh', 'ea',
...          'ef', 'fg', 'gf', 'hd', 'hf']
>>> G.add_edges_from(tuple(x) for x in edges)
>>> components = list(nx.strongly_connected_components(G))
>>> sorted(sorted(component) for component in components)
[['a', 'b', 'e'], ['c', 'd', 'h'], ['f', 'g']]
>>>
>>> C = nx.condensation(G, components)
>>> component_of = C.graph['mapping']
>>> same_component = lambda u, v: component_of[u] == component_of[v]
>>> Q = nx.quotient_graph(G, same_component)
>>> nx.is_isomorphic(C, Q)
True

Node identification can be represented as the quotient of a graph under the equivalence relation that places the two nodes in one block and each other node in its own singleton block:

>>> import networkx as nx
>>> K24 = nx.complete_bipartite_graph(2, 4)
>>> K34 = nx.complete_bipartite_graph(3, 4)
>>> C = nx.contracted_nodes(K34, 1, 2)
>>> nodes = {1, 2}
>>> is_contracted = lambda u, v: u in nodes and v in nodes
>>> Q = nx.quotient_graph(K34, is_contracted)
>>> nx.is_isomorphic(Q, C)
True
>>> nx.is_isomorphic(Q, K24)
True

The blockmodeling technique described in [1] can be implemented as a quotient graph:

>>> G = nx.path_graph(6)
>>> partition = [{0, 1}, {2, 3}, {4, 5}]
>>> M = nx.quotient_graph(G, partition, relabel=True)
>>> list(M.edges())
[(0, 1), (1, 2)]

References

[1]	Patrick Doreian, Vladimir Batagelj, and Anuska Ferligoj. Generalized Blockmodeling. Cambridge University Press, 2004.