LD²

This is the code for "LD²: Scalable Heterophilous Graph Neural Network with Decoupled Embeddings", NeurIPS 2023.

Proceedings (Paper/Supplemental) | Conference (Poster/Video/Slides) | OpenReview | GitHub

Citation

If you find this work useful, please cite our paper:

Ningyi Liao, Siqiang Luo, Xiang Li, and Jieming Shi.
LD2: Scalable Heterophilous Graph Neural Network with Decoupled Embeddings.
Advances in Neural Information Processing Systems 36: 10197-10209, 2023.

@inproceedings{liao2023ld2,
  title={{LD2}: Scalable Heterophilous Graph Neural Network with Decoupled Embedding},
  author={Liao, Ningyi and Luo, Siqiang and Li, Xiang and Shi, Jieming},
  booktitle={Advances in Neural Information Processing Systems},
  volume={36},
  pages={10197-10209},
  year={2023},
  month={Dec},
  editor = {A. Oh and T. Neumann and A. Globerson and K. Saenko and M. Hardt and S. Levine},
  publisher = {Curran Associates, Inc.},
  url = {https://proceedings.neurips.cc/paper_files/paper/2023/file/206191b9b7349e2743d98d855dec9e58-Paper-Conference.pdf},
}

Experiment

Data Preparation

Use LINKX source to download raw data.
Run command python data_convert.py to generate processed files under path data/[dataset_name] similar to the example folder data/actor:

feats.npy: features in .npy array
labels.npz: node label information
- 'label': labels (number or one-hot)
- 'idx_train/idx_val/idx_test': indices of training/validation/test nodes
adj_el.bin, adj_pl.bin, attribute.txt, deg.npz: graph files for precomputation

Precompute

Environment: CMake 3.16, C++ 14. Dependencies: eigen3, Spectra
Compile Cython: python setup.py build_ext --inplace

Train & Test

Install dependencies: conda create --name [envname] --file requirements.txt
Run minibatch experiment: python run_mini.py -f [seed] -c [config_file] -v [device]

Reference & Links

Baselines: LINKX | GloGNN | ACM | PPRGo | AGP

Datasets: Yelp - GraphSAINT | Reddit - PPRGo | Amazon - Cluster-GCN | tolokers | Others - LINKX

TODOs

NOTE (2023-11-26): the current version has different propagation computation due to some new optimization strategies, which may cause speed differences. We will update the code soon.

Adj propagation parallel and clocking
Upload dataset configs

gdmnl/LD2