Accumulate values of "neigborhood" from edgelist with numpy


I have a undirected network where each node can be one of k types. For each node i, I need to calculate the number of neighbors that node i has of each type.

Right now I am representing the edges with an edgelist where the columns are indexes of the nodes. The nodes are represented as a n x k matrix, where each column represents a node type. If a node is of type k then the kth column's value is 1, 0 otherwise.

Here's my current code, which is correct, but too slow.

# example nodes and edges, both typically much longer
nodes = np.array([[0, 0, 1], 
                  [0, 1, 0],                       
                  [1, 0, 0]])
edges = np.array([[0, 1],
                  [1, 2]])

neighbors = np.zeros_like(nodes)

for i, j in edges:
   neighbors[i] += nodes[j]
   neighbors[j] += nodes[i]

Is there some clever numpy that would allow me to avoid this for loop? If the best way to do this is with an adjacency matrix, that is also acceptable.

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| numpy   | python   | vectorization   | graph-algorithm   | numpy-broadcasting   2016-09-30 23:09 2 Answers

Answers to Accumulate values of "neigborhood" from edgelist with numpy ( 2 )

  1. 2016-10-01 13:10

    If I understand your question correctly, the numpy_indexed package (disclaimer: I am its author) has a fast and elegant solution to this:

    # generate a random example graph
    n_edges = 50
    n_nodes = 10
    n_types = 3
    edges = np.random.randint(0, n_nodes, size=(n_edges, 2))
    node_types = np.random.randint(0, 2, size=(n_nodes, n_types)).astype(np.bool)
    # Note; this is for a directed graph
    s, e = edges.T
    # for undirected, add reversed edges
    s, e = np.concatenate([edges, edges[:,::-1]], axis=0).T
    import numpy_indexed as npi
    node_idx, neighbor_type_count = npi.group_by(s).sum(node_types[e])

    In general, operations on graphs, or algorithms involving jagged-arrays, can often be efficiently and elegantly expressed using grouping-operations.

  2. 2016-10-03 23:10

    You can simply use -

    out = np.zeros_like(nodes), edges[:,0],nodes[edges[:,1]]), edges[:,1],nodes[edges[:,0]])

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