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publications
Out-of-Distribution Detection on Graphs: A Survey
Published in under review, 2025
For the first time, this paper systematically classifies existing out-of-distribution detection (GOOD) methods, reviews representative studies from 2020 to 2025, provides an in-depth analysis of the core principles and common misconceptions in dealing with the task of GOOD detection, and reveals the main challenges facing the field and future research directions.
Recommended citation: Cai, T.; Jiang, Y.; Liu, Y; Li, M.; Huang, C.; and Pan, S. 2025. Out-of-Distribution Detection on Graphs: A Survey. In arXiv: 2502.08105.
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ML-GOOD: Towards Multi-Label Graph Out-Of-Distribution Detection
Published in AAAI, 2025
This paper is the first to address graph OOD detection in multi-label classification by leveraging energy functions, demonstrating improved performance across diverse datasets.
Recommended citation: Cai, T., Jiang, Y., Li, M., Huang, C., Wang, Y., & Huang, Q. (2025). ML-GOOD: Towards Multi-Label Graph Out-Of-Distribution Detection. Proceedings of the AAAI Conference on Artificial Intelligence, 39(15), 15650-15658. https://doi.org/10.1609/aaai.v39i15.33718
talks
Paper Title Number 1
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under review
Recommended citation: Your Name, You. (2009). "Paper Title Number 1." Journal 1. 1(1).
Conference Proceeding talk 3 on Relevant Topic in Your Field
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teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
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Teaching experience 2
Workshop, University 1, Department, 2015
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