@inproceedings{9e0b461506a8448c9ce94a9fb3567bbc,
title = "DYGL: A Unified Benchmark and Library for Dynamic Graph",
abstract = "Difficulty in reproducing the code and inconsistent experimental methods hinder the development of the dynamic network field. We present DYGL, a unified, comprehensive, and extensible library for dynamic graph representation learning. The main goal of the library is to make dynamic graph representation learning available for researchers in a unified easy-to-use framework. To accelerate the development of new models, we design unified model interfaces based on unified data formats, which effectively encapsulate the details of the implementation. Experiments demonstrate the predictive performance of the models implemented in the library on node classification and link prediction. Our library will contribute to the standardization and reproducibility in the field of the dynamic graph. The project is released at the link: https://github.com/half-salve/DYGL-lib",
keywords = "Library, Reproducibility, deep learning, dynamic graph",
author = "Teng Ma and Bin Shi and Yiming Xu and Zihan Zhao and Siqi Liang and Bo Dong",
note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.; 7th Asia-Pacific Web (APWeb) and Web-Age Information Management (WAIM) Joint Conference on Web and Big Data, APWeb-WAIM 2023 ; Conference date: 06-10-2023 Through 08-10-2023",
year = "2024",
doi = "10.1007/978-981-97-2387-4\_26",
language = "英语",
isbn = "9789819723867",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "389--401",
editor = "Xiangyu Song and Ruyi Feng and Yunliang Chen and Jianxin Li and Geyong Min",
booktitle = "Web and Big Data - 7th International Joint Conference, APWeb-WAIM 2023, Proceedings",
}