def nodeFeatures(g, types):
#g = dgl.add_self_loop(g)
#graph = dgl.DGLGraph.to_networkx(g)
if (types == "simple"):
return g.in_degrees()
elif (types == "weight"):
return dgl.khop_adj(g, 1) #g.ndata['w']
elif (types == "multifractal"):
return multifractal.multifractal(g)
def test_khop_adj():
N = 20
feat = F.randn((N, 5))
g = dgl.DGLGraph(nx.erdos_renyi_graph(N, 0.3))
for k in range(3):
adj = F.tensor(dgl.khop_adj(g, k))
# use original graph to do message passing for k times.
g.ndata['h'] = feat
for _ in range(k):
g.update_all(fn.copy_u('h', 'm'), fn.sum('m', 'h'))
h_0 = g.ndata.pop('h')
# use k-hop adj to do message passing for one time.
h_1 = F.matmul(adj, feat)
assert F.allclose(h_0, h_1, rtol=1e-3, atol=1e-3)
