Question about the sampling code

[chocolate9624]

In line #L517, nodes of the lowest layer are treated as input nodes for GCN. This suggests the lowest layer contains all the nodes in the sampled sub-graph. However, it is not always true.
For example,

---

layer1: 4->2 5 -> 2
layer2: 2->1 3->1
layer3: 1->0

---

the lowest layer contains node (4, 5, 2), the middle layer contain nodes (2,3,1), the top layer contains nodes (1, 0). In your code, the features for nodes 1 and 3 are lost.

[acbull]

Sorry which line are you referring to?

We don't assume the lowest layer contains all the nodes, as we are conducting layer-wise sampling. For each GNN layer, the source nodes and target nodes could be different. (which is stored as a specific adjacency matrix in

LADIES/pytorch_ladies.py

Lines 117 to 120 in 303036e

	adj = row_norm(U[: , after_nodes].multiply(1/p[after_nodes]))
	# Turn the sampled adjacency matrix into a sparse matrix. If implemented by PyG
	# This sparse matrix can also provide index and value.
	adjs += [sparse_mx_to_torch_sparse_tensor(row_normalize(adj))]

)

What you refer to is more like graph-wise sampling, by sampling a subgraph and then apply GNN on the whole sampled graph, which is different from our setting.

[chocolate9624]

Thanks for your quick response!

LADIES/pytorch_ladies.py

Line 157 in c10b526

return adjs, previous_nodes, batch_nodes

This line returns the "previous_nodes". The 'previous_nodes' is the nodes from the lowest layer.

LADIES/pytorch_ladies.py

Lines 241 to 246 in c10b526

	for adjs, input_nodes, output_nodes in train_data:
	adjs = package_mxl(adjs, device)
	optimizer.zero_grad()
	t1 = time.time()
	susage.train()
	output = susage.forward(feat_data[input_nodes], adjs)

In these lines, the 'previous_nodes' is renamed as 'input_nodes'. And these nodes ('input_nodes') are used to slice node features for the sampled subgraph.

I think the 'previous_nodes' from the lowest layer may not contain all the nodes in the sampled subgraph. The node features for some of the nodes are not used.

Thanks!