test_line_graph.py

import torch as th
import networkx as nx
import numpy as np
import dgl

D = 5

def test_line_graph():
    N = 5
    G = dgl.DGLGraph(nx.star_graph(N))
    G.edata['h'] = th.randn((2 * N, D))
    n_edges = G.number_of_edges()
    L = G.line_graph(shared=True)
    assert L.number_of_nodes() == 2 * N
    L.ndata['h'] = th.randn((2 * N, D))
    # update node features on line graph should reflect to edge features on
    # original graph.
    u = [0, 0, 2, 3]
    v = [1, 2, 0, 0]
    eid = G.edge_ids(u, v)
    L.nodes[eid].data['h'] = th.zeros((4, D))
    assert th.allclose(G.edges[u, v].data['h'], th.zeros((4, D)))

    # adding a new node feature on line graph should also reflect to a new
    # edge feature on original graph
    data = th.randn(n_edges, D)
    L.ndata['w'] = data
    assert th.allclose(G.edata['w'], data)

def test_no_backtracking():
    N = 5
    G = dgl.DGLGraph(nx.star_graph(N))
    L = G.line_graph(backtracking=False)
    assert L.number_of_nodes() == 2 * N
    for i in range(1, N):
        e1 = G.edge_id(0, i)
        e2 = G.edge_id(i, 0)
        assert not L.has_edge_between(e1, e2)
        assert not L.has_edge_between(e2, e1)

if __name__ == '__main__':
    test_line_graph()
    test_no_backtracking()