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OpenDAS
dgl
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b5e90bc3
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b5e90bc3
authored
Jun 14, 2023
by
Hongzhi (Steve), Chen
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GitHub
Jun 14, 2023
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[Misc] Update the comment in examples/sampling/node_classification.py (#5874)
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examples/sampling/node_classification.py
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@@ -5,9 +5,12 @@ large graphs using efficient neighbor sampling.
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@@ -5,9 +5,12 @@ large graphs using efficient neighbor sampling.
Paper: [Inductive Representation Learning on Large Graphs]
Paper: [Inductive Representation Learning on Large Graphs]
(https://arxiv.org/abs/1706.02216)
(https://arxiv.org/abs/1706.02216)
If you want a deeper understanding of node classification. You can
Before reading this example, please familiar yourself with graphsage node
read the example in the `examples/pytorch/graphsage/node_classification.py`
classification by reading the example in the
TODO(#5797): Move `graphsage/node_classification.py` to the `examples/core/`.
`examples/core/graphsage/node_classification.py`
If you want to train graphsage on a large graph in a distributed fashion, read
the example in the `examples/distributed/graphsage/`.
This flowchart describes the main functional sequence of the provided example.
This flowchart describes the main functional sequence of the provided example.
main
main
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