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How to pronounce my name?Zhaoxuan -> jow sh-yen. Hi there, thanks for visiting my website! I'm a first-year CSE PhD student at the University of Notre Dame, where I am fortunate to be advised by Prof. Meng Jiang and affiliated with the DM2 lab. Prior to that, I obtained my bachelor's degree in computer science and technology at Xi'an Jiaotong University (2019-2023) and had wonderful time conducting research at the LUD lab advised by Prof. Minnan Luo. My research generally focuses on knowledge base, social network analysis, natural language processing, and graph neural networks. In 2022, I spent a summer at KEG @ THU to work with Prof. Yuxiao Dong. Please feel free to drop me an Email for any form of communication or collaboration!☘️
Email: CV / Google Scholar / Semantic Scholar / Twitter / Github / LinkedIn |
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My primary research interest lies at the intersection of graph mining (especially knowledge graphs and social networks) and natural language processing, with a particular focus on computation for social good and LLM+Graph. |
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Zijian Cai, Zhaoxuan Tan, Zhenyu Lei, Zifeng Zhu, Hongrui Wang, Qinghua Zheng, Minnan Luo arXiv preprint 2023 We propose LMBot, which utilizes a language model with graph-aware knowledge distillation to act as a proxy for graph-less Twitter bot detection inference. This approach effectively resolves graph data dependency and sampling bias issues. |
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Sen Ye, Zhaoxuan Tan, Zhenyu Lei, Ruijie He, Hongrui Wang, Qinghua Zheng, Minnan Luo arXiv preprint 2023 We identify the heterophilous disguise challenge in Twitter bot detection and proposed HOFA, a novel framework equipped with Homophily-Oriented Augmentation and Frequency Adaptive Attention to demystify the heterophilous disguise challenge. |
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Heng Wang*, Shangbin Feng*, Tianxing He, Zhaoxuan Tan, Xiaochuang Han, Yulia Tsvetkov Proceedings of NeurIPS 2023 (Spotlight) code Are language models graph reasoners? We propose the NLGraph benchmark, a test bed for graph-based reasoning designed for language models in natural language. We find that LLMs are preliminary graph thinkers while the most advanced graph reasoning tasks remain an open research question. |
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Shangbin Feng, Zhaoxuan Tan, Wenqian Zhang, Zhenyu Lei, Yulia Tsvetkov Proceedings of ACL 2023 We propose KALM, a Knowledge-Aware Language Model that jointly incorporates external local, document-level, and global context knowledge. |
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Heng Wang, Wenqian Zhang, Yuyang Bai, Zhaoxuan Tan, Shangbin Feng, Qinghua Zheng, Minnan Luo arXiv preprint, 2023. We curate a large-scale network-based spoiler detection dataset (LCS), a movie knowledge base (UKM), and propose MVSD, a Multi-View Spoiler Detection framework that takes into account external knowledge and user interaction networks. |
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Yuhan Liu, Zhaoxuan Tan, Heng Wang, Shangbin Feng, Qinghua Zheng, Minnan Luo Proceedings of SIGIR 2023. We propose community-aware mixture-of-experts to address two challenges in detecting advanced Twitter bots: manipulated features and diverse communities. |
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Zhaoxuan Tan*, Shangbin Feng*, Melanie Sclar, Herun Wan, Minnan Luo, Yejin Choi, Yulia Tsvetkov arXiv preprint, 2023. demo / tweet We introduce the concept of community-level Twitter bot detection and develope BotPercent, a multi-dataset, multi-model Twitter bot detection pipeline. Utilizing BotPercent, we investigate the presence of bots in various Twitter communities and discovered that bot distribution is heterogeneous in both space and time. |
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Zhaoxuan Tan, Zilong Chen, Shangbin Feng, Qingyue Zhang, Qinghua Zheng, Jundong Li, Minnan Luo Proceedings of The Web Conference (WWW), 2023. code / talk We adopt contrastive learning and knowledge relational attention network to alleviate the widespread sparsity problem in knowledge graphs. |
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Shangbin Feng*, Zhaoxuan Tan*, Herun Wan*, Ningnan Wang*, Zilong Chen*, Binchi Zhang*, Qinghua Zheng, Wenqian Zhang, Zhenyu Lei, Shujie Yang, Xinshun Feng, Qingyue Zhang, Hongrui Wang, Yuhan Liu, Yuyang Bai, Heng Wang, Zijian Cai, Yanbo Wang, Lijing Zheng, Zihan Ma, Jundong Li, Minnan Luo Proceedings of NeurIPS, Datasets and Benchmarks Track, 2022. website / GitHub / bibtex / poster We present TwiBot-22, the largest graph-based Twitter bot detection benchmark to date, which provides diversified entities and relations in Twittersphere and has considerably better annotation quality. |
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Shangbin Feng, Zhaoxuan Tan, Zilong Chen, Peisheng Yu, Qinghua Zheng, Xiaojun Chang, Minnan Luo Proceedings of EMNLP 2022. code / bibtex / poster We propose to learn representations of political actors with social context and expert knowledge, while applying learned representations in computational political science. |
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Shangbin Feng, Zhaoxuan Tan, Rui Li, Minnan Luo Proceedings of AAAI 2022. slides / code / bibtex We propose the relational graph transformers GNN architecture to leverage the intrinsic relation heterogeneity and influence heterogeneity in Twitter network. |
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University of Notre Dame
2023.08 - present Ph.D. in Computer Science and Engineering Advisor: Prof. Meng Jiang |
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Xi'an Jiaotong University
2019.08 - 2023.07 B.E. in Computer Science and Technology GPA: 89.1 (+3) / 100.0 [top 5%] Advisor: Prof. Minnan Luo |
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Template courtesy: Jon Barron. |