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generic_weighted_projected_graph¶

generic_weighted_projected_graph
(B, nodes, weight_function=None)[source]¶ Weighted projection of B with a userspecified weight function.
The bipartite network B is projected on to the specified nodes with weights computed by a userspecified function. This function must accept as a parameter the neighborhood sets of two nodes and return an integer or a float.
The nodes retain their attributes and are connected in the resulting graph if they have an edge to a common node in the original graph.
Parameters:  B (NetworkX graph) – The input graph should be bipartite.
 nodes (list or iterable) – Nodes to project onto (the “bottom” nodes).
 weight_function (function) – This function must accept as parameters the same input graph that this function, and two nodes; and return an integer or a float. The default function computes the number of shared neighbors.
Returns: Graph – A graph that is the projection onto the given nodes.
Return type: NetworkX graph
Examples
>>> from networkx.algorithms import bipartite >>> # Define some custom weight functions >>> def jaccard(G, u, v): ... unbrs = set(G[u]) ... vnbrs = set(G[v]) ... return float(len(unbrs & vnbrs)) / len(unbrs  vnbrs) ... >>> def my_weight(G, u, v, weight='weight'): ... w = 0 ... for nbr in set(G[u]) & set(G[v]): ... w += G.edge[u][nbr].get(weight, 1) + G.edge[v][nbr].get(weight, 1) ... return w ... >>> # A complete bipartite graph with 4 nodes and 4 edges >>> B = nx.complete_bipartite_graph(2,2) >>> # Add some arbitrary weight to the edges >>> for i,(u,v) in enumerate(B.edges()): ... B.edge[u][v]['weight'] = i + 1 ... >>> for edge in B.edges(data=True): ... print(edge) ... (0, 2, {'weight': 1}) (0, 3, {'weight': 2}) (1, 2, {'weight': 3}) (1, 3, {'weight': 4}) >>> # Without specifying a function, the weight is equal to # shared partners >>> G = bipartite.generic_weighted_projected_graph(B, [0, 1]) >>> print(G.edges(data=True)) [(0, 1, {'weight': 2})] >>> # To specify a custom weight function use the weight_function parameter >>> G = bipartite.generic_weighted_projected_graph(B, [0, 1], weight_function=jaccard) >>> print(G.edges(data=True)) [(0, 1, {'weight': 1.0})] >>> G = bipartite.generic_weighted_projected_graph(B, [0, 1], weight_function=my_weight) >>> print(G.edges(data=True)) [(0, 1, {'weight': 10})]
No attempt is made to verify that the input graph B is bipartite. The graph and node properties are (shallow) copied to the projected graph.
See also
is_bipartite()
,is_bipartite_node_set()
,sets()
,weighted_projected_graph()
,collaboration_weighted_projected_graph()
,overlap_weighted_projected_graph()
,projected_graph()