@article{Yan2025, 
author = {Boyuan Yan and Yankun Zhang and Wenwen Gong and Haoyang Wan and Wenwei Wang and Weiyi Zhong and Caixia Bu},
title = {MDGCN-Lt: Fair Web API Classification with Sparse and Heterogeneous Data Based on Deep GCN},
year = {2025},
journal = {Tsinghua Science and Technology},
volume = {30},
number = {3},
pages = {1294-1314},
keywords = {bidirectional encoder representations from transformers, deep graph convolutional networks, logit adjustment, web application programming interface classification, application programming interface correlation graph},
url = {https://www.sciopen.com/article/10.26599/TST.2024.9010026},
doi = {10.26599/TST.2024.9010026},
abstract = {Developers integrate web Application Programming Interfaces (APIs) into edge applications, enabling data expansion to the edge computing area for comprehensive coverage of devices in that region. To develop edge applications, developers search API categories to select APIs that meet specific functionalities. Therefore, the accurate classification of APIs becomes critically important. However, existing approaches, as evident on platforms like programableweb.com, face significant challenges. Firstly, sparsity in API data reduces classification accuracy in works focusing on single-dimensional API information. Secondly, the multidimensional and heterogeneous structure of web APIs adds complexity to data mining tasks, requiring sophisticated techniques for effective integration and analysis of diverse data aspects. Lastly, the long-tailed distribution of API data introduces biases, compromising the fairness of classification efforts. Addressing these challenges, we propose MDGCN-Lt, an API classification approach offering flexibility in using multi-dimensional heterogeneous data. It tackles data sparsity through deep graph convolutional networks, exploring high-order feature interactions among API nodes. MDGCN-Lt employs a loss function with logit adjustment, enhancing efficiency in handling long-tail data scenarios. Empirical results affirm our approach’s superiority over existing methods.}
}