This repository contains the source code and datasets for Edgeformers: Graph-Empowered Transformers for Representation Learning on Textual-Edge Networks, published in ICLR 2023.
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
Edgeformers contain two transformer-based architecture, namely Edgeformer-E and Edgeformer-N, for edge representation learning and node representation learning respectively.
Download processed data. To reproduce the results in our paper, you need to first download the processed datasets. Then put the dataset folders under Edgeformer-E/ and create a new ckpt/ folder inside each dataset folder for checkpoint saving (e.g., cd Edgeformer-E/Apps, mkdir ckpt).
The format of each processed data sample is as follows:
edge_text\$\$node1_id\$\$node2_id\$\$edge_label\n
For example, a sample in Apps dataset is like this:
Get this game! Beautiful graphics, entertaining story. Challenging yet not unsolvable. One of the best hidden object games out there!\$\$49138\$\$909\$\$4
Raw data & data processing. Raw data can be downloaded from Amazon and Goodreads directly. You can also find our data processing codes here. They might be useful if you want to obtain processed dataset for other networks in Amazon and Goodreads.
Use your own dataset. To run our model on your own data, you need to prepare the following things:
(1) The network config file node_num.pkl. (2) Model training and testing files train.tsv, val.tsv, test.tsv. Please refer to the file in our processed dataset for their format information.
Train
CUDA_VISIBLE_DEVICES=0 python Edgeformer-E/main.py --data_path $data_path
The dataset path is $data_path and the model checkpoints will be saved in $data_path/ckpt. Since tokenizing will take a great amount of time, for the first run on a new dataset, the tokenized data files will be saved in $data_path as cache files for future use. You may change the hyperparameters for your own preference.
Test
CUDA_VISIBLE_DEVICES=0 python Edgeformer-E/main.py --mode test --data_path $data_path --load_ckpt_name $load_ckpt
The dataset path is $data_path. $load_ckpt refers to the checkpoint file you want to test. Macro-f1 and Micro-f1 scores will be shown in the last several lines of the output.
Download processed data. To reproduce the results in our paper, you need to first download the processed datasets. Then put the dataset folders under Edgeformer-N/ and create a new ckpt/ folder inside each dataset folder for checkpoint saving (e.g., cd Edgeformer-N/Apps, mkdir ckpt).
The format of each processed data sample (a node pair) is as follows:
node1_id\*\*node1_edge_text_group1\*\*node1_edge_text_group2\*\*node1_neighbor_node_group1\*\*node1_neighbor_node_group2\$\$node1_id\*\*node1_edge_text_group1\*\*node1_edge_text_group2\*\*node1_neighbor_node_group1\*\*node1_neighbor_node_group2\$\$label\n
It can be simplified to node1_info\$\$node2_info\$\$label\n. Each text (seperated by \t) in {x}_edge_text_group{x} corresponds to a neighbor node (seperated by \t) in node{x}_neighbor_node_group{x} (node{x} is linked to this neighbor node with the text on the edge). The number of text in node{x}_edge_text_group{x} will be same to the number of nodes in node{x}_neighbor_node_group{x}. We have two groups of edge_texts/neighbor_nodes to take both positive reviewed neighbors and negative reviewed neighbors into consideration in ego-graph sampling for Amazon and Goodreads dataset. While for stackoverflow, each query node only have one group of edge_texts/neighbor_nodes (node{x}_edge_text_group2=NULL, node{x}_neighbor_node_group2=NULL).
We will utilize node pairs with label 1 to train the model.
Raw data & data processing. Raw data can be downloaded from Amazon and Goodreads directly. You can also find our data processing codes here. They might be useful if you want to obtain processed dataset for other networks in Amazon, Goodreads and Stackoverflow.
Use your own dataset. To run our model on your own data, you need to prepare the following things:
(1) The network config file node_num.pkl. (2) Model training and testing files train.tsv, val.tsv, test.tsv. (3) The neighbor sampling information file neighbor_sampling.pkl. Please refer to the file in our processed dataset for their format information.
Train
CUDA_VISIBLE_DEVICES=0 python Edgeformer-N/main.py --data_path $data_path
The dataset path is $data_path and the model checkpoints will be saved in $data_path/ckpt. Since tokenizing will take a great amount of time, for the first run on a new dataset, the tokenized data files will be saved in $data_path as cache files for future use. You may change the hyperparameters for your own preference.
Test
CUDA_VISIBLE_DEVICES=0 python Edgeformer-N/main.py --mode test --data_path $data_path --load_ckpt_name $load_ckpt
The dataset path is $data_path. $load_ckpt refers to the checkpoint file you want to test. MRR and NDCG scores will be shown in the last several lines of the output.
Infer
CUDA_VISIBLE_DEVICES=0 python Edgeformer-N/main.py --mode infer --data_path $data_path --load True --load_ckpt_name $load_ckpt
By running this, embeddings will be generated for nodes in $data_path/train.tsv and are saved to $data_path/EdgeformerN.npy.
Please cite the following paper if you find the code helpful for your research.
@inproceedings{jin2023edgeformers,
title={Edgeformers: Graph-Empowered Transformers for Representation Learning on Textual-Edge Networks},
author={Jin, Bowen and Zhang, Yu and Meng, Yu and Han, Jiawei},
booktitle={International Conference on Learning Representations},
year={2023}
}