//
ci_lint = "tvmai/ci-lint:v0.51"
-ci_gpu = "tvmai/ci-gpu:v0.54"
+ci_gpu = "tvmai/ci-gpu:v0.55"
ci_cpu = "tvmai/ci-cpu:v0.54"
ci_i386 = "tvmai/ci-i386:v0.52"
import torch.nn as nn
import torch.nn.functional as F
import dgl
+import networkx as nx
from dgl.nn.pytorch import GraphConv
class GCN(nn.Module):
# Remove self-loops to avoid duplicate passing of a node's feature to itself
g = data.graph
- g.remove_edges_from(g.selfloop_edges())
+ g.remove_edges_from(nx.selfloop_edges(g))
g.add_edges_from(zip(g.nodes, g.nodes))
return g, data
Parameters
----------
dataset: str
- Name of dataset. You can choose from ['cora', 'citeseer', 'pubmed'].
+ Name of dataset. You can choose from ['cora', 'citeseer', 'pubmed'].
num_layer: int
number of hidden layers
######################################################################
# Prepare the parameters needed in the GraphConv layers
# ------------------
-#
+#
import numpy as np
import networkx as nx