https://github.com/divelab/neighbor2seq

Official implementation of "Neighbor2Seq: Deep Learning on Massive Graphs by Transforming Neighbors to Sequences" [SDM2022]
https://github.com/divelab/neighbor2seq

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Official implementation of "Neighbor2Seq: Deep Learning on Massive Graphs by Transforming Neighbors to Sequences" [SDM2022]

• Host: GitHub
• URL: https://github.com/divelab/neighbor2seq
• Owner: divelab
• License: gpl-3.0
• Created: 2022-01-20T16:16:38.000Z (almost 3 years ago)
• Default Branch: main
• Last Pushed: 2022-02-08T03:20:32.000Z (almost 3 years ago)
• Last Synced: 2023-03-06T20:42:14.960Z (over 1 year ago)
• Language: Python
• Size: 201 KB
• Stars: 8
• Watchers: 4
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Neighbor2Seq: Deep Learning on Massive Graphs by Transforming Neighbors to Sequences

This repository is an official PyTorch implementation of Neighbor2Seq.

[Meng Liu](https://mengliu1998.github.io) and [Shuiwang Ji](http://people.tamu.edu/~sji/). [Neighbor2Seq: Deep Learning on Massive Graphs by Transforming Neighbors to Sequences](https://arxiv.org/abs/2202.03341) [SDM2022].

## Requirements

* PyTorch

* PyTorch Geometric (with 1.6.1-1.7.2 recommended)

* OGB

## Reference

```

@inproceedings{liu2022neighbor2seq,

  title={{Neighbor2Seq}: Deep Learning on Massive Graphs by Transforming Neighbors to Sequences},

  author={Liu, Meng and Ji, Shuiwang},

  booktitle={Proceedings of the 2022 SIAM International Conference on Data Mining},

  year={2022},

  organization={SIAM}

}

```

## Run

All of our running scripts are included in [`run_ours.sh`](https://github.com/divelab/Neighbor2Seq/blob/main/Neighbor2Seq/run_ours.sh). An example on Flickr is as follows.

* Step 1: Precompute Neighbor2Seq

```linux

python precompute.py --dataset=Flickr --P=10 --add_self_loop=True

```

* Step 2: Train and evaluate Neighbor2Seq+Conv or Neighbor2Seq+Attn 

```linux

CUDA_VISIBLE_DEVICES=0 python main_inductive.py --model=conv --lr=0.0008 --K=10 --weight_decay=0.00005 --hidden=256 --dropout=0.5 --batch_size=24576 --epochs=400 --kernel_size=7 --runs=10 --log_step=1 

```

```linux

CUDA_VISIBLE_DEVICES=0 python main_inductive.py --model=posattn --lr=0.002 --K=10 --weight_decay=0.00005 --hidden=256 --dropout=0.5 --batch_size=256 --epochs=200 --pe_drop=0.25 --runs=10 --log_step=1

```

## Results

* Results on inductive tasks: `Reddit`, `Flickr`, and `Yelp`



* Results on `ogbn-papers100M`



* Results on `ogbn-products`