dmlc--dgl
Mufei Li
3f6f694159
* Update * Update * Update dependencies * Update * Update * Fix ogbn-products gat * Update * Update * Reformat * Fix typo in node2vec_random_walk * Specify file encoding * Working for 6.7 * Update * Fix subgraph * Fix doc for sample_neighbors_biased * Fix hyperlink * Add example for udf cross reducer * Fix * Add example for slice_batch * Replace dgl.bipartite * Fix GATConv * Fix math rendering * Fix doc Co-authored-by: Ubuntu <ubuntu@ip-172-31-28-17.us-west-2.compute.internal> Co-authored-by: Jinjing Zhou <VoVAllen@users.noreply.github.com> Co-authored-by: Ubuntu <ubuntu@ip-172-31-22-156.us-west-2.compute.internal>
28 行
1.1 KiB
Markdown
28 行
1.1 KiB
Markdown
# Heterogeneous Graph Attention Network (HAN) with DGL
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This is an attempt to implement HAN with DGL's latest APIs for heterogeneous graphs.
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The authors' implementation can be found [here](https://github.com/Jhy1993/HAN).
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## Usage
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`python main.py` for reproducing HAN's work on their dataset.
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`python main.py --hetero` for reproducing HAN's work on DGL's own dataset from
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[here](https://github.com/Jhy1993/HAN/tree/master/data/acm). The dataset is noisy
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because there are same author occurring multiple times as different nodes.
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For sampling-based training, `python train_sampling.py`
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## Performance
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Reference performance numbers for the ACM dataset:
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| | micro f1 score | macro f1 score |
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| ------------------- | -------------- | -------------- |
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| Paper | 89.22 | 89.40 |
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| DGL | 88.99 | 89.02 |
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| Softmax regression (own dataset) | 89.66 | 89.62 |
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| DGL (own dataset) | 91.51 | 91.66 |
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We ran a softmax regression to check the easiness of our own dataset. HAN did show some improvements.
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