Lightgcn fix#602
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lthoang merged 6 commits intoPreferredAI:masterfrom Mar 18, 2024
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Removed normalization for layers, not used for lgcn. Fixed sum weight constant to num layers instead of cur layer index. Allow lgcn to take blocks. Fixed requirement error caused by newer dgl versions. Moved edge normalization to graph for easier use.
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@tqtg as per #600 (comment), I've created a pull request for LightGCN only. |
qtuantruong
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Mar 14, 2024
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thanks @theisjendal for clarifying all the changes. They all make sense to me after revising the authors' implementation of LightGCN. Could you also try to compare the updated version and the old one using this example with the same hyper-parameters? I'm curious about the changes in accuracy and speed of convergence. |
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@tqtg, here are the results:
NEW
VALIDATION: TEST: |
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LGTM. Feel free to merge this PR. |
qtuantruong
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Mar 15, 2024
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Oct 25, 2025
* Add git ignore * Lightgcn fix Removed normalization for layers, not used for lgcn. Fixed sum weight constant to num layers instead of cur layer index. Allow lgcn to take blocks. Fixed requirement error caused by newer dgl versions. Moved edge normalization to graph for easier use. * Lightgcn debug error fix * Simplified layer normalization and readability * Easier support of rcuda
qtuantruong
pushed a commit
to qtuantruong/cornac
that referenced
this pull request
Oct 25, 2025
* Add git ignore * Lightgcn fix Removed normalization for layers, not used for lgcn. Fixed sum weight constant to num layers instead of cur layer index. Allow lgcn to take blocks. Fixed requirement error caused by newer dgl versions. Moved edge normalization to graph for easier use. * Lightgcn debug error fix * Simplified layer normalization and readability * Easier support of rcuda
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Description
LightGCN is a simplified version of NGCF, however, some of the simplifications made in LightGCN where not included.
Therefore:
1/(L+1)and instead of1/(l+1)whereLis the number of layers andlis the current layer index.Checklist:
README.md(if you are adding a new model).examples/README.md(if you are adding a new example).datasets/README.md(if you are adding a new dataset).