Add the unsupervised bipartite GraphSAGE model on the Taobao dataset #6144
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@@ Coverage Diff @@
## master #6144 +/- ##
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Coverage 85.09% 85.09%
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Files 402 402
Lines 21672 21672
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Hits 18442 18442
Misses 3230 3230
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rusty1s
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Jan 19, 2023
#6431 created a `pyproject.toml` which aimed to create a single configuration file for `yapf`, `isort`, `coverage`, `pyright` among others. All the configuration for `yapf` now lies within the `pyproject.toml` file. This PR aims to delete the redundant `.style.yapf` possibly added back after (#6144). Co-authored-by: rusty1s <matthias.fey@tu-dortmund.de>
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This PR adds an implementation of unsupervised bipartite GraphSAGE on the Taobao User Behaviors dataset offered by Alibaba. The Taobao dataset contains a heterogeneous graph, where nodes represent users and items, and edges represent different types of behaviors between users and items.
We use the i2i co-occurrence matrix to construct the i2i-graph. When applying GraphSAGE, the model follows the i-i-i pattern to encode the item embedding and the i-i-u pattern to encode the user embedding. As the task is unsupervised, we use negative sampling and
binary_cross_entropy_with_logitsto compute the loss in the model.