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I am a third-year phd candidate in the Knowledge Engineering Group (KEG) at Tsinghua, supervised by Jie Tang. I recieved my Master’s Degree from School of Software, Tsinghua University in 2021, advised by Ping Luo. My research focus on scientific language model and graph representaion learning. I am now a visiting student researcher at Caltech in the Computing + Mathematical Sciences (CMS) Department, currently working with Yisong Yue. 张丹 / zd18 [MASK] tsinghua [MASK] org [MASK] cn / Bio / Google Scholar / GitHub |
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Dan Zhang, Ziniu Hu, Sining Zhoubian, Zhengxiao Du, Kaiyu Yang, Zihan Wang, Yisong Yue, Yuxiao Dong, and Jie Tang In submission, 2024 arXiv / code & data / model SciGLM is a suite of scientific language models able to conduct college-level scientific reasoning. Central to our approach is a novel self-reflective instruction annotation framework to address the data scarcity challenge in the science domain. Applying this framework, we curated SciInstruct, a diverse and high-quality dataset encompassing physics, chemistry, math, and formal proofs. |
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Fanjin Zhang, Shijie Shi, Yifan Zhu, Bo Chen, Yukuo Cen, Jifan Yu, Yelin Chen, Lulu Wang, Qingfei Zhao, Yuqing Cheng, Tianyi Han, Yuwei An, Dan Zhang, Weng Lam Tam, Kun Cao, Yunhe Pang, Xinyu Guan, Huihui Yuan, Jian Song, Xiaoyan Li, Yuxiao Dong, and Jie Tang In submission, 2024 arXiv / code & data / OAG-Challenge @ KDD Cup 2024 We present OAG-Bench, a comprehensive, multi-aspect, and fine-grained human-curated benchmark based on the Open Academic Graph (OAG). OAG-Bench covers 10 tasks, 20 datasets, 70+ baselines, and 120+ experimental results to date. |
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Dan Zhang, Yangliao Geng, Wenwen Gong, Zhongang Qi, Zhiyu Chen, Xing Tang, Ying Shan, Yuxiao Dong, and Jie Tang WWW, 2024, Oral presentation arXiv / code & data RecDCL is a dual contrastive learning recommendation framework. In this work, we investigate how to employ both batch-wise CL (BCL) and feature-wise CL (FCL) for recommendation. We theoretically analyze the relation between BCL and FCL, and find that combining BCL and FCL helps eliminate redundant solutions but never misses an optimal solution. |
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Dan Zhang, Yifan Zhu, Yuxiao Dong, Yuandong Wang, Wenzheng Feng, Evgeny Kharlamov, and Jie Tang WWW, 2023 PDF / code & data / slides_pdf ApeGNN develops a node-wise adaptive diffusion mechanism for information aggregation, in which each node is enabled to adaptively decide its diffusion weights based on the local structure (e.g., degree). |
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Dan Zhang, Wenzheng Feng, Yuandong Wang, Zhongang Qi, Ying Shan, and Jie Tang TKDE, 2024 PDF / code & data DropConn is an adaptive data augmentation strategy, which better leverages edge features and assigns weights for chemical bonds to emphasize their importance, and generates robust representations for molecule graph property prediction. |
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Yifan Zhu, Fangpeng Cong, Dan Zhang, Wenwen Gong, Qika Lin, Wenzheng Feng, Yuxiao Dong, and Jie Tang KDD, 2023 PDF / code & data WinGNN models dynamic graphs by combining a simple graph neural network model with meta-learning strategies and implementing a time-encoder-free dynamic graph coding process through a stochastic gradient aggregation mechanism. |
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Ming Zhou, Dan Zhang, Yuandong Wang, Yangliao Geng, and Jie Tang CIKM, 2023 CBD is characterized by a two-stage model learning strategy: a contrastive pre-training stage to mine generalization patterns from massive unlabeled social graphs, followed by a semi-supervised fine-tuning stage to model task-specific knowledge latent in social graphs with a few annotations. |
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Ming Zhou, Wenzheng Feng, Yifan Zhu, Dan Zhang, Yuxiao Dong, and Jie Tang ECML-PKDD, 2023, Best Student Paper We analyze human-bot networks and propose SIRAN, which combines relation attention with initial residual connection to reduce and prevent the noise aggregated from neighbors to improve the capability of distinguishing different kinds of nodes on social graphs with heterophily. Then we use a consistency loss to boost the detection performance of the model for limited annotated data. |
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