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OpenDAS
dgl
Commits
8b5f4f5b
Unverified
Commit
8b5f4f5b
authored
Aug 03, 2021
by
Jinjing Zhou
Committed by
GitHub
Aug 03, 2021
Browse files
Fix typos (#3214)
parent
4ae13bd2
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.github/PULL_REQUEST_TEMPLATE.md
.github/PULL_REQUEST_TEMPLATE.md
+1
-1
tutorials/models/1_gnn/9_gat.py
tutorials/models/1_gnn/9_gat.py
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-1
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.github/PULL_REQUEST_TEMPLATE.md
View file @
8b5f4f5b
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@@ -8,7 +8,7 @@ Please feel free to remove inapplicable items for your PR.
...
@@ -8,7 +8,7 @@ Please feel free to remove inapplicable items for your PR.
-
[ ] Changes are complete (i.e. I finished coding on this PR)
-
[ ] Changes are complete (i.e. I finished coding on this PR)
-
[ ] All changes have test coverage
-
[ ] All changes have test coverage
-
[ ] Code is well-documented
-
[ ] Code is well-documented
-
[ ] To the
my
best knowledge, examples are either not affected by this change,
-
[ ] To the best
of my
knowledge, examples are either not affected by this change,
or have been fixed to be compatible with this change
or have been fixed to be compatible with this change
-
[ ] Related issue is referred in this PR
-
[ ] Related issue is referred in this PR
-
[
] If the PR is for a new model/paper, I've updated the example index [here
](
../examples/README.md
)
.
-
[
] If the PR is for a new model/paper, I've updated the example index [here
](
../examples/README.md
)
.
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tutorials/models/1_gnn/9_gat.py
View file @
8b5f4f5b
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@@ -29,7 +29,7 @@ One workaround is to simply average over all neighbor node features as described
...
@@ -29,7 +29,7 @@ One workaround is to simply average over all neighbor node features as described
the research paper `GraphSAGE
the research paper `GraphSAGE
<https://www-cs-faculty.stanford.edu/people/jure/pubs/graphsage-nips17.pdf>`_.
<https://www-cs-faculty.stanford.edu/people/jure/pubs/graphsage-nips17.pdf>`_.
However, `Graph Attention Network <https://arxiv.org/abs/1710.10903>`_ proposes a
However, `Graph Attention Network <https://arxiv.org/abs/1710.10903>`_ proposes a
different type of aggregation. GA
N
uses weighting neighbor features with feature dependent and
different type of aggregation. GA
T
uses weighting neighbor features with feature dependent and
structure-free normalization, in the style of attention.
structure-free normalization, in the style of attention.
"""
"""
###############################################################
###############################################################
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