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
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Acknowledgements
This research was partially supported by the National Key Research and Development Project of China No. 2021ZD0110700, the Key Research and Development Project in Shaanxi Province No. 2022GXLH-01-03, the National Science Foundation of China under Grant Nos. 62002282, 62037001, 62250009 and 61721002, the Major Technological Innovation Project of Hangzhou No. 2022AIZD0113, the “Pioneer” and “Leading Goose” R &D Program of Zhejiang No. 2022C01107, the China Postdoctoral Science Foundation No. 2020M683492, the MOE Innovation Research Team No. IRT_17R86, and Project of XJTU-SERVYOU Joint Tax-AI Lab.
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Ma, T., Shi, B., Xu, Y., Zhao, Z., Liang, S., Dong, B. (2024). DYGL: A Unified Benchmark and Library for Dynamic Graph. In: Song, X., Feng, R., Chen, Y., Li, J., Min, G. (eds) Web and Big Data. APWeb-WAIM 2023. Lecture Notes in Computer Science, vol 14333. Springer, Singapore. https://doi.org/10.1007/978-981-97-2387-4_26
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DOI: https://doi.org/10.1007/978-981-97-2387-4_26
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