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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
Pages
Posts
Future Blog Post
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Blog Post number 4
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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
Blog Post number 3
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Blog Post number 2
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Blog Post number 1
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portfolio
Portfolio item number 1
Short description of portfolio item number 1
Portfolio item number 2
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publications
Out-of-Distribution Detection on Graphs: A Survey
Published in arXiv, 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
In this paper, we tackle the emerging issue of multi-label graph out-of-distribution (GOOD) detection. We propose a label-specific energy function that effectively integrates multi-label information to compute GOOD energy scores, supported by theoretical analysis. Through extensive experimentation and discussion, we verify the efficacy of our approach, i.e., ML-GOOD. We aim to inspire further research into OOD detection for multi-label graph classification.
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.
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HyperNear: Unnoticeable Node Injection Attacks on Hypergraph Neural Networks
Published in ICML, 2025
In this work, we explore adversarial attacks on hypergraph-based models, revealing their structural vulner abilities. We introduce HyperNear, the first node injection attack framework for HNNs, leveraging homophily constraints for stealth. Experiments demonstrate its high attack efficacy, transferability, and unnoticeability. Our analysis of unnoticeability metrics deepens the understanding of hypergraph structure and model robustness, paving the way for future work on strengthening hypergraph-based models.
Recommended citation: Cai T, Jiang Y, Li M, et al. HyperNear: Unnoticeable Node Injection Attacks on Hypergraph Neural Networks[C]//Forty-second International Conference on Machine Learning.
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HyperGOOD: Towards Out-of-Distribution Detection in Hypergraphs
Published in AAAI, 2026
Out-of-distribution (OOD) detection plays a critical role in ensuring the robustness of machine learning models in open-world settings. While extensive efforts have been made in vision, language, and graph domains, the challenge of OOD detection in hypergraph-structured data remains unexplored. In this work, we formalize the problem of hypergraph out-of-distribution (HOOD) detection, which aims to identify nodes or hyperedges whose high-order relational contexts differ significantly from those seen during training. We propose HyperGOOD, a unified energy-based detection framework that integrates multi-scale spectral decomposition with structure-aware uncertainty propagation. By preserving both low- and high-frequency signals and diffusing uncertainty across the hypergraph, HyperGOOD effectively captures subtle and relationally entangled anomalies. Experimental results on nine hypergraph datasets demonstrate the effectiveness of our approach, establishing a new foundation for robust hypergraph learning under distributional shifts.
Recommended citation: Cai, T., Jiang, Y., Li, M., Huang, C., Fang, Y., Gao, C., & Zheng, Z. (2026). HyperGOOD: Towards Out-of-Distribution Detection in Hypergraphs. Proceedings of the AAAI Conference on Artificial Intelligence, 40(24), 19817–19825.
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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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This is a description of your conference proceedings talk, note the different field in type. You can put anything in this field.
teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
This is a description of a teaching experience. You can use markdown like any other post.
Teaching experience 2
Workshop, University 1, Department, 2015
This is a description of a teaching experience. You can use markdown like any other post.