wehub-resource-sync
69 lines
2.8 KiB
Markdown
69 lines
2.8 KiB
Markdown
Metapath2vec
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============
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- Paper link: [metapath2vec: Scalable Representation Learning for Heterogeneous Networks](https://ericdongyx.github.io/papers/KDD17-dong-chawla-swami-metapath2vec.pdf)
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- Author's code repo: [https://ericdongyx.github.io/metapath2vec/m2v.html](https://ericdongyx.github.io/metapath2vec/m2v.html).
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Dependencies
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------------
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- PyTorch 1.0.1+
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How to run the code
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-----
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Run with either of the following procedures:
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* Running with default AMiner dataset:
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1. Directly run the following command:
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```bash
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python metapath2vec.py --aminer --path "where/you/want/to/download" --output_file "your_model_output_path"
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```
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* Running with another AMiner-like dataset
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1. Prepare the data in the same format as the ones of AMiner and DBIS in Section B of [Author's code repo](https://ericdongyx.github.io/metapath2vec/m2v.html).
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2. Run `sampler.py` on your graph dataset with, for instance,
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```bash
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python sampler.py net_dbis
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```
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3. Run the following command:
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```bash
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python metapath2vec.py --path net_dbis/output_path.txt --output_file "your_model_output_path"
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```
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Tips: Change num_workers based on your GPU instances; Running 3 or 4 epochs is actually enough.
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Tricks included in the implementation:
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-------
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1, Sub-sampling;
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2, Negative Sampling without repeatedly calling numpy random choices;
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Performance and Explanations:
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-------
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Venue Classification Results for Metapath2vec:
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| Metric | 5% | 10% | 20% | 30% | 40% | 50% | 60% | 70% | 80% | 90% |
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| ------ | -- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
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| Macro-F1 | 0.3033 | 0.5247 | 0.8033 | 0.8971 | 0.9406 | 0.9532 | 0.9529 | 0.9701 | 0.9683 | 0.9670 |
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| Micro-F1 | 0.4173 | 0.5975 | 0.8327 | 0.9011 | 0.9400 | 0.9522 | 0.9537 | 0.9725 | 0.9815 | 0.9857 |
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Author Classfication Results for Metapath2vec:
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| Metric | 5% | 10% | 20% | 30% | 40% | 50% | 60% | 70% | 80% | 90% |
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| ------ | -- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
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| Macro-F1 | 0.9216 | 0.9262 | 0.9292 | 0.9303 | 0.9309 | 0.9314 | 0.9315 | 0.9316 | 0.9319 | 0.9320 |
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| Micro-F1 | 0.9279 | 0.9319 | 0.9346 | 0.9356 | 0.9361 | 0.9365 | 0.9365 | 0.9365 | 0.9367 | 0.9369 |
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Note that:
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Testing files are available in "label 2" file;
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The above are results listed in the paper, in real experiments, exact numbers might be slightly different:
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1, For venue node classification results, when the size of the training dataset is small (e.g. 5%), the variance of the performance is large since the number of available labeled venues is small.
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2, For author node classification results, the performance is stable since the number of available labeled authors is huge, so even 5% training data would be sufficient.
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3, In the test.py, you could change experiment times you want, especially it is very slow to test author classification so you could only do 1 or 2 times.
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