@inproceedings{93b80c247c8f45b3addf939ba689110f,
title = "GraphCBAL-Sys: A Class-Balanced Active Learning System for Graphs",
abstract = "Active learning for Graph Neural Networks (GNNs) aims to select valuable unlabeled samples for annotation with a limited budget to maximize the GNNs{\textquoteright} performance at a low cost. However, most methods often result in imbalanced class distributions, leading to a bias toward majority classes, which undermines minority class performance and overall model effectiveness. To tackle this issue, we develop the Class-Balanced Active Learning System for Graphs GraphCBAL-Sys. It learns an optimal policy through reinforcement learning to acquire class-balanced and informative nodes for annotation. Additionally, GraphCBAL-Sys is capable of visualizing the internal processes and results during our model{\textquoteright}s training and testing phases. Our demonstration video can be found here: https://b23.tv/yCLOIPw.",
keywords = "Class balance, Graph Active learning, Reinforcement learning, Visualization",
author = "Chengcheng Yu and Wenqian Zhou and Jiahui Wang and Fangshu Chen and Jiapeng Zhu and Xiang Li",
note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026.; 30th International Conference on Database Systems for Advanced Applications, DASFAA 2025 ; Conference date: 26-05-2025 Through 29-05-2025",
year = "2026",
doi = "10.1007/978-981-95-4158-4\_34",
language = "英语",
isbn = "9789819541577",
series = "Lecture Notes in Computer Science",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "473--476",
editor = "Feida Zhu and Ee-Peng Lim and Yu, \{Philip S.\} and Akiyo Nadamoto and Kyuseok Shim and Wei Ding and Bingxue Zhang",
booktitle = "Database Systems for Advanced Applications - 30th International Conference, DASFAA 2025, Proceedings",
address = "德国",
}