Neighbor2Seq: Deep Learning on Massive Graphs by Transforming Neighbors to Sequences
This repository is an official PyTorch implementation of Neighbor2Seq.
Meng Liu and Shuiwang Ji. Neighbor2Seq: Deep Learning on Massive Graphs by Transforming Neighbors to Sequences [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
. An example on Flickr is as follows.
- Step 1: Precompute Neighbor2Seq
python precompute.py --dataset=Flickr --P=10 --add_self_loop=True
- Step 2: Train and evaluate Neighbor2Seq+Conv or Neighbor2Seq+Attn
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
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
, andYelp
- Results on
ogbn-papers100M
- Results on
ogbn-products