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SAGN_with_SLE

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# Scalable and Adaptive Graph Neural Networks with Self-Label-Enhanced training
This repository is an implementation of paper: [Scalable and Adaptive Graph Neural Networks with Self-Label-Enhanced training](https://arxiv.org/abs/2104.09376)

## Requirements
`pytorch dgl ogb pytorch-geometric`

## Statement
Feel free to use any parts from our works, but remember to **cite our paper and repository**. **Introduce our methods** if your work is mostly based on ours. We think these are basic academic ethics.

## Datasets
Inductive datasets (Reddit, Flickr, Yelp) can be loaded via dgl or pyg. PPI should be separately downloaded and saved into `dataset`. More detailed information can be found in [GraphSaint](https://github.com/GraphSAINT/GraphSAINT)

OGB datasets are loaded via OGB library. More detailed information can be found in [Open Graph Benchmark](https://ogb.stanford.edu/)

## Reproduce results
Run shell scripts on folder `scripts`, add device number to indicate GPU:

`bash scripts/{dataset}/{method} {device_id}`

## Results
Test F1-micro with means and standard deviations under 10 runs. We use reported metrics for baselines.
| Method | Reddit | Flickr |  PPI | Yelp |
| ---- | ---- | ---- | ---- | ---- |
|GCN |93.3±0.0\% | 49.2±0.3\% | 51.5±0.6\% | 37.8±0.1\% |
|FastGCN | 92.4±0.1\% | 50.4±0.1\% | 51.3±3.2\% | 26.5±5.3\% |
|Stochastic-GCN | 96.4±0.1\% | 48.2±0.3\% | 96.3±1.0\% | 64.0±0.2\%|
|AS-GCN | 95.8±0.1\% | 50.4±0.2\% | 68.7±1.2\% | — |
|GraphSAGE | 95.3±0.1\% | 50.1±1.3\% | 63.7±0.6\% | 63.4±0.6\%|
|ClusterGCN | 95.4±0.1\% | 48.1±0.5\% | 87.5±0.4\% | 60.9±0.5\% |
|GraphSaint | 96.6±0.1\% | 51.1±0.1\% | **98.1±0.4\%** | 65.3±0.3\%|
|SGC | 94.9±0.0\% | 50.2±0.1\% | 89.2±1.5\% | 35.8±0.6\%|
|SIGN | 96.8±0.0\% | 51.4±0.1\% | 97.0±0.3\% | 63.1±0.3\%|
|SAGN | 96.9±0.0\% | 51.8±0.2\% | 98.0±0.1\% | 65.4±0.1\%|
|SAGN+SLE | **97.1±0.0\%** | **55.1±0.1\%** | **98.1±0.1\%** | **65.6±0.1\%**|

Validation and test accuray with means and standard deviations under 10 runs. We use reported metrics for baselines.
| Method | ogbn-products validation | ogbn-products test | ogbn-papers100M validation | ogbn-papers100M test|
| ---- | ---- | ---- | ----- | ----|
|MLP | 75.54±0.14\% | 61.06±0.08\% | 49.60±0.29\% | 47.24±0.31\%|
| Node2Vec | 90.32±0.06\% | 72.49±0.10\% | 58.07±0.28\% | 55.60±0.23\% |
| GCN | 92.00±0.03\% | 75.64±0.21\% | — | — |
| GraphSAGE | 91.70±0.09\% | 78.70±0.36\% | — | — |
| ClusterGCN | 92.12±0.09\% | 78.97±0.33\% | — | — |
| GraphSaint | — | 80.27±0.26\% | — | — |
| SIGN | 92.86±0.02\% | 80.52±0.13\% | 69.32±0.06\% | 65.68±0.06\% |
| SAGN | 93.11±0.05\% | 81.28±0.12\% | 70.82±0.05\% | 67.24±0.08\% |
| UniMP | 93.08±0.17\% | 82.56±0.31\% | 71.72±0.05\% | 67.36±0.10\% |
| MLP+C\&S | 91.47±0.09\% | 84.18±0.07\% | — | — |
| SAGN+SLE |  93.09±0.07\% | *84.68±0.12\%* | 71.63±0.07\%  |  **68.30±0.08\%** |
| SAGN+SLE+C\&S |  93.02±0.03\% | **84.85±0.10\%** | —  |  — |

SAGN_with_SLE

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