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# incidence_matrix

incidence_matrix(G, nodelist=None, edgelist=None, oriented=False, weight=None, *, dtype=None)

  Returns incidence matrix of G.

  The incidence matrix assigns each row to a node and each column to an edge. For a standard incidence matrix a 1 appears wherever a row’s node is incident on the column’s edge. For an oriented incidence matrix each edge is assigned an orientation (arbitrarily for undirected and aligning to direction for directed). A -1 appears for the source (tail) of an edge and 1 for the destination (head) of the edge. The elements are zero otherwise.

  Parameters:
    G graph

      A NetworkX graph

    nodelist list, optional (default= all nodes in G)

      The rows are ordered according to the nodes in nodelist. If nodelist is None, then the ordering is produced by G.nodes().

    edgelist list, optional (default= all edges in G)

      The columns are ordered according to the edges in edgelist. If edgelist is None, then the ordering is produced by G.edges().

    oriented: bool, optional (default=False)

      If True, matrix elements are +1 or -1 for the head or tail node respectively of each edge. If False, +1 occurs at both nodes.

    weight string or None, optional (default=None)

      The edge data key used to provide each value in the matrix. If None, then each edge has weight 1. Edge weights, if used, should be positive so that the orientation can provide the sign.

    dtype a NumPy dtype or None (default=None)

      The dtype of the output sparse array. This type should be a compatible type of the weight argument, eg. if weight would return a float this argument should also be a float. If None, then the default for SciPy is used.

  Returns:
    A SciPy sparse array

      The incidence matrix of G.

  Notes

  For MultiGraph/MultiDiGraph, the edges in edgelist should be (u,v,key) 3-tuples.

  “Networks are the best discrete model for so many problems in applied mathematics” [1].

  References

  [1]

  Gil Strang, Network applications: A = incidence matrix, http://videolectures.net/mit18085f07_strang_lec03/