dmlc--dgl
Quan (Andy) Gan
69 行
2.3 KiB
Markdown
69 行
2.3 KiB
Markdown
Inductive Representation Learning on Large Graphs (GraphSAGE)
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============
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- Paper link: [http://papers.nips.cc/paper/6703-inductive-representation-learning-on-large-graphs.pdf](http://papers.nips.cc/paper/6703-inductive-representation-learning-on-large-graphs.pdf)
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- Author's code repo: [https://github.com/williamleif/graphsage-simple](https://github.com/williamleif/graphsage-simple). Note that the original code is
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simple reference implementation of GraphSAGE.
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Requirements
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------------
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- requests
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``bash
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pip install requests
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``
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Results
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-------
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### Full graph training
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Run with following (available dataset: "cora", "citeseer", "pubmed")
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```bash
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python3 train_full.py --dataset cora --gpu 0 # full graph
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```
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* cora: ~0.8330
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* citeseer: ~0.7110
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* pubmed: ~0.7830
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### Minibatch training
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Train w/ mini-batch sampling (on the Reddit dataset)
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```bash
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python3 train_sampling.py --num-epochs 30 # neighbor sampling
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python3 train_sampling.py --num-epochs 30 --inductive # inductive learning with neighbor sampling
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python3 train_sampling_multi_gpu.py --num-epochs 30 # neighbor sampling with multi GPU
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python3 train_sampling_multi_gpu.py --num-epochs 30 --inductive # inductive learning with neighbor sampling, multi GPU
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python3 train_cv.py --num-epochs 30 # control variate sampling
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python3 train_cv_multi_gpu.py --num-epochs 30 # control variate sampling with multi GPU
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```
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Accuracy:
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| Model | Accuracy |
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|:---------------------:|:--------:|
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| Full Graph | 0.9504 |
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| Neighbor Sampling | 0.9495 |
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| N.S. (Inductive) | 0.9460 |
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| Control Variate | 0.9490 |
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### Unsupervised training
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Train w/ mini-batch sampling in an unsupervised fashion (on the Reddit dataset)
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```bash
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python3 train_sampling_unsupervised.py
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```
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Notably,
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* The loss function is defined by predicting whether an edge exists between two nodes or not. This matches the official
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implementation, and is equivalent to the loss defined in the paper with 1-hop random walks.
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* When computing the score of `(u, v)`, the connections between node `u` and `v` are removed from neighbor sampling.
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This trick increases the F1-micro score on test set by 0.02.
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* The performance of the learned embeddings are measured by training a softmax regression with scikit-learn, as described
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in the paper.
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Micro F1 score reaches 0.9212 on test set.
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