code: def edge_load_centrality(G, cutoff=False):
      """Compute edge load.
  
      WARNING: This concept of edge load has not been analysed
      or discussed outside of NetworkX that we know of.
      It is based loosely on load_centrality in the sense that
      it counts the number of shortest paths which cross each edge.
      This function is for demonstration and testing purposes.
  
      Parameters
      ----------
      G : graph
          A networkx graph
  
      cutoff : bool, optional (default=False)
          If specified, only consider paths of length <= cutoff.
  
      Returns
      -------
      A dict keyed by edge 2-tuple to the number of shortest paths
      which use that edge. Where more than one path is shortest
      the count is divided equally among paths.
      """
      betweenness = {}
      for u, v in G.edges():
          betweenness[(u, v)] = 0.0
          betweenness[(v, u)] = 0.0
  
      for source in G:
          ubetween = _edge_betweenness(G, source, cutoff=cutoff)
          for e, ubetweenv in ubetween.items():
              betweenness[e] += ubetweenv  # cumulative total
      return betweenness
  
imports: from operator import itemgetter
  import networkx
label_desc: Use image node_id 3 for calling a global function with example usage: edge_load_centrality(G, cutoff) and returns: betweenness