Well-conditioned Spectral Transforms for Dynamic Graph Representation

Introduction

This work establishes a fully-spectral framework to capture informative long-range temporal interactions in a dynamic system. We connect the spectral transform to the low-rank self-attention mechanisms and investigate its energy-balancing effect and computational efficiency. Based on the observations, we leverage the adaptive power method SVD and global graph framelet convolution to encode time-dependent features and graph structure for continuous-time dynamic graph representation learning. The former serves as an efficient high-order linear self-attention with determined propagation rules, and the latter establishes scalable and transferable geometric characterization for property prediction. Empirically, the proposed model learns well-conditioned hidden representations on a variety of online learning tasks, and it achieves top performance with a reduced number of learnable parameters and faster propagation speed.

Paper link: Well-conditioned Spectral Transforms for Dynamic Graph Representation

Requirements

To install requirements:

pip install -r requirements.txt

Dataset and Preprocessing

Download the public data

Download the sample datasets (eg. wikipedia and reddit) from here and store their csv files in a folder named data/.

Preprocess the data

We use the dense npy format to save the features in binary format. If edge features or nodes features are absent, they will be replaced by a vector of zeros.

python utils/preprocess_data.py --data wikipedia --bipartite
python utils/preprocess_data.py --data reddit --bipartite

Running the experiments

Self-supervised learning using the link prediction task:

# Transductive learning on the wikipedia dataset
python  link_prediction_transductive.py --data wikipedia --drop_out 0.3 --num_modes 70 --memory_dim 150 --n_runs 1 

# Inductive learning on the wikipedia dataset
python link_prediction_inductive.py  --data wikipedia --drop_out 0.3  --memory_dim 150 --gpu 0 --early_stopper 10 --n_runs 1 --num_modes 70

Supervised learning on dynamic node classification

(this requires a trained model from the self-supervised task, by eg. running the commands above):

# Node classification
python node_classification.py --drop_out 0.3 --num_modes 70 --memory_dim 150 --n_runs 1

Citation

If you find this repository helpful, please cite our paper with the bibtex below:

@inproceedings{zhou2022spedgnn,
title={Well-conditioned Spectral Transforms for Dynamic Graph Representation},
author={Zhou, Bingxin and Liu, Xinliang and Liu, Yuehua and Huang, Yunying and Lio, Pietro and Wang, YuGuang},
booktitle={Learning on Graphs (LOG) Conference},
year={2022}
}

bzho3923 / GNN_SpedGNN