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Heterformer

This repository contains the source code and datasets for Heterformer: Transformer-based Deep Node Representation Learning on Heterogeneous Text-Rich Networks, published in KDD 2023.

Links

• Requirements
• Overview
• Data
• Train
• Inference
• Downstream Tasks
• Citations

Requirements

The code is written in Python 3.6. Before running, you need to first install the required packages by typing following commands (Using a virtual environment is recommended):

pip3 install -r requirements.txt

Overview

Heterformer is a Transformer architecture (language model) for representation on heterogeneous text-rich (text-attributed) networks. It can take text data associated with nodes and heterogeneous network structure information into consideration.

Data

1. Download raw data from DBLP, Twitter and Goodreads.
2. Data processing: Run the cells in data/$dataset/data_processing.ipynb for first step data processing.
3. Network Sampling: Run the cells in data/$dataset/sampling.ipynb for ego-network sampling and train/val/test data generation.
4. Pretrain data: Run the cells in data/$dataset/generate_pretrain_data.ipynb for textless node pretraining data generation.

Train

1. Pretrain textless node embeddings. Take Goodreads dataset as an example.

cd pretrain/
bash run.sh

2. Prepare textless node embedding file for Heterformer training.

Run the cells in pretrain/transfer_embed.ipynb

3. Heterformer training.

cd ..
python main.py --data_path data/$dataset --model_type Heterformer --pretrain_embed True --pretrain_dir data/$dataset/pretrain_embed

Test

python main.py --data_path data/$dataset --model_type Heterformer --mode test --load_ckpt_name $load_ckpt_dir

Inference

python main.py --data_path data/$dataset --model_type Heterformer --mode infer --load 1 --load_ckpt_name $load_ckpt_dir

Downstream

Transductive Text-rich node classification

cd downstream/
python classification.py --mode transductive --dataset $dataset --method Heterformer

Inductive Text-rich node classification

python classification.py --mode inductive --dataset $dataset --method Heterformer

Textless node classification

python author_classification.py --dataset $dataset --method Heterformer

Node Clustering

python clustering.py --mode transductive --dataset $dataset --method Heterformer

Retrieval

python retrieval.py --method Heterformer

Citations

Please cite the following paper if you find the code helpful for your research.

@inproceedings{jin2023heterformer,
  title={Heterformer: Transformer-based deep node representation learning on heterogeneous text-rich networks},
  author={Jin, Bowen and Zhang, Yu and Zhu, Qi and Han, Jiawei},
  booktitle={Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining},
  pages={1020--1031},
  year={2023}
}