dmlc--dgl
Lingfan Yu
b8cc26e34a
* sign for ogbn products, arxiv, mag * texts * fix * update ogb folder readme * use dgl nightly build Co-authored-by: Mufei Li <mufeili1996@gmail.com>
51 行
1.7 KiB
Markdown
51 行
1.7 KiB
Markdown
SIGN: Scalable Inception Graph Neural Network
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==========================
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Paper: [https://arxiv.org/abs/2004.11198](https://arxiv.org/abs/2004.11198)
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Dependencies
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------------
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- pytorch 1.5
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- dgl 0.5 nightly build
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- `pip install --pre dgl`
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- ogb 1.2.3
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How to run
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-------------
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### ogbn-products
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```python
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python3 sign.py --dataset ogbn-products --eval-ev 10 --R 5 --input-d 0.3 --num-h 512 \
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--dr 0.4 --lr 0.001 --batch-size 50000 --num-runs 10
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```
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### ogbn-arxiv
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```python
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python3 sign.py --dataset ogbn-arxiv --eval-ev 10 --R 5 --input-d 0.1 --num-h 512 \
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--dr 0.5 --lr 0.001 --eval-b 100000 --num-runs 10
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```
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### ogbn-mag
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ogbn-mag is a heterogeneous graph and the task is to predict publishing venue
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of papers. Since SIGN model is designed for homogeneous graph, we simply ignore
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heterogeneous information (i.e. node and edge types) and treat the graph as a
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homogeneous one. For node types that don't have input feature, we featurize them
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with the average of their neighbors' features.
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```python
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python3 sign.py --dataset ogbn-mag --eval-ev 10 --R 5 --input-d 0 --num-h 512 \
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--dr 0.5 --lr 0.001 --batch-size 50000 --num-runs 10
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```
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Results
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----------
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Table below shows the average and standard deviation (over 10 times) of
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accuracy. Experiments were performed on Tesla T4 (15GB) GPU on Oct 29.
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| Dataset | Test Accuracy | Validation Accuracy | # Params |
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| :-------------: | :-------------: | :-------------------: | :---------: |
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| ogbn-products | 0.8052±0.0016 | 0.9299±0.0004 | 3,483,703 |
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| ogbn-arxiv | 0.7195±0.0011 | 0.7323±0.0006 | 3,566,128 |
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| ogbn-mag | 0.4046±0.0012 | 0.4068±0.0010 | 3,724,645 |
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