All eight dynamic text-attributed graphs provided by DTGB can be downloaded from here.

Each graph is preserved through three files.

• edge_list.csv: stores each edge in DyTAG as a tuple. i.e., (u, r, i, ts, label). u is the id of the source entity, i is the id of the target entity, r is the id of the relation between them, ts is the occurring timestamp of this edge, label is the label of this edge.
• entity_text.csv: stores the mapping from entity ids (e.g., u and i) to the text descriptions of entities.
• relation_text.csv: stores the mapping from relation ids (e.g., r) to the text descriptions of relations.

• After downloading the datasets, they should be uncompressed into the DyLink_Datasets folder.
• Run get_pretrained_embeddings.py to obtain the Bert-based node and edge text embeddings. They will be saved as e_feat.npy and r_feat.npy respectively.
• Run get_LLM_data.ipynb to get the train and test set for the textual relation generation task. They will be saved as LLM_train.pkl and LLM_test.pkl respectively.

• Example of training DyGFormer on GDELT dataset without text attributes:

python train_link_prediction.py --dataset_name GDELT --model_name DyGFormer --patch_size 2 --max_input_sequence_length 64 --num_runs 5 --gpu 0 --use_feature no

• Example of training DyGFormer on GDELT dataset with text attributes:

python train_link_prediction.py --dataset_name GDELT --model_name DyGFormer --patch_size 2 --max_input_sequence_length 64 --num_runs 5 --gpu 0 --use_feature Bert

• The AP and AUC-ROC metrics on the test set (both transductive setting and inductive setting) will be automatically saved in saved_resuts/DyGFormer/GDELT/DyGFormer_seed0no.json
• The best checkpoint will be saved in saved_resuts/DyGFormer/GDELT/ folder, and the checkpoint will be used to reproduce the performance on the node retrieval task.

After obtaining the best checkpoint on the Future Link Prediction Task. The Hits@k metrics of the Destination Node Retrieval Task can be reproduced by running:

python evaluate_node_retrieval.py --dataset_name GDELT --model_name DyGFormer --patch_size 2 --max_input_sequence_length 64 --negative_sample_strategy random --num_runs 5 --gpu 0  --use_feature no

• The negative_sample_strategy hyper-parameter is used to control the candidate sampling strategies, which can be random and historical.
• The use_feature hyper-parameter is used to control whether to use Bert-based embeddings, which can be no and Bert.

• Example of training DyGFormer on GDELT dataset without text attributes:

python train_edge_classification.py --dataset_name GDELT --model_name DyGFormer --patch_size 2 --max_input_sequence_length 64 --num_runs 5 --gpu 0 --use_feature no

• The Precision, Recall, and F1-score metrics on the test set will be automatically saved in saved_resuts/DyGFormer/GDELT/edge_classification_DyGFormer_seed0no.json

After obtaining the LLM_train.pkl and LLM_test.pkl files. You can directly reproduce the performance of original LLMs by running

python LLM_eval.py -config_path=LLM_configs/vicuna_7b_qlora_uncensored.yaml -model=raw

• You can change the LLMs through the config_path hyper-parameter.
• The generated text will be saved in s_his_o_des_his_result_vicuna7b.pkl.

And then to get the Bert_score metrics, you should change the file path in LLM_metric.py and run:

python LLM_metric.py

If you want to fine-tune the LLMs, you should run:

python LLM_train.py LLM_configs/vicuna_7b_qlora_uncensored.yaml

and then reproduce the performance of the fine-tunned LLMs by running

python LLM_eval.py -config_path=LLM_configs/vicuna_7b_qlora_uncensored.yaml -model=lora

For any questions or suggestions, you can use the issues section or contact us at ([email protected]).

Codes and model implementations are referred to DyGLib project. Thanks for their great contributions!

@article{zhang2024dtgb,
  title={DTGB: A Comprehensive Benchmark for Dynamic Text-Attributed Graphs},
  author={Zhang, Jiasheng and Chen, Jialin and Yang, Menglin and Feng, Aosong and Liang, Shuang and Shao, Jie and Ying, Rex},
  journal={arXiv preprint arXiv:2406.12072},
  year={2024}
}