I am currently an associate professor at Department of Information Management and Business Intelligence, Fudan University. I obtained my Ph.D. degree from Zhejiang University in September 2021, co-advised by Yang Yang and Jiangang Lu. I have been visiting UCLA (working with Yizhou Sun) from Nov, 2019 to Apr, 2020.

My research interests lie in developing effective and reliable artificial intelligence methods to address real-world problems, particularly in finance and broader business and societal domains. Specifically, I am interested in large language models, graph and time-series data mining.

jiarongxu {at} fudan [dot] edu [dot] cn

Motivated students with backgrounds in machine learning are welcome for brainstorming! Contact

Selected Research Work

Financial Foundation Models

We develop financial foundation models that integrate temporal user behavior sequences, textual information, and graph-structured data to support financial services such as financial risk assessment.

01Risk Assessment with Graph-Structured Data

We leverage relational patterns in financial networks to detect abnormal and high-risk behavior.

Wild-GAD, KDD'25 DGraph, NeurIPS'22 AMNet, IJCAI'22

02Risk Assessment with Time-Series Data

We model time-series data, such as user behavior sequences, to support reliable financial risk assessment.

Think-like-LSTM, KDD'26 SSH-T3, CIKM'25 Zhang et al., NeurIPS'25 LeTE, ICML'25

Graph Foundation Models

We investigate the full pre-training and fine-tuning lifecycle of graph foundation models. Our works aim to answer four questions:

01When to Pre-Train

Under what conditions should the graph pre-training and fine-tuning paradigm be adopted?

W2PGNN, KDD'23

02What to Pre-Train

Is a massive amount of input data always necessary, or even beneficial, for graph pre-training?

APT, NeurIPS'23

03How to Fine-Tune

How can a pre-trained graph model be efficiently adapted while reducing the gap between pre-training and downstream tasks?

Bridge-Tune, AAAI'24

04How to Collaborate with LLMs

We explore the power of language models for text-attributed graphs and unified graph pre-training.

Kuang et al., EMNLP'23 Ma et al., EMNLP'23

Data Privacy & Security

We advance trustworthy machine learning along two research directions:

01Privacy Risks & Protection

Privacy-preserving data collaboration and fair federated learning.

Xu et al., INFORMS Journal on Computing FairGraphFL, AAAI'24

Privacy risk assessment and mitigation strategies.

Yuan et al., KDD'24 GPIA, NeurIPS'24 Molextract, NeurIPS'24 Yang et al., TDSC'25

02Adversarial Threats & Robust Models

We investigate the vulnerabilities of machine learning models to adversarial attacks and develop defense mechanisms.

STACK, AAAI'22 Xu et al., AAAI'22 RisKeeper, AAAI'24

Applied Artificial Intelligence for Business & Society

We design novel machine learning or LLM-based methods for real-world business and societal problems.

01 Recommendation

We model purchase motivations and diversified user preferences to improve product recommendation.

Xu et al., Information Systems Research Tree of Preferences, NeurIPS'25

02Platform Governance

We build LLM-based multi-agent simulation systems for proactive policy evaluation and optimization for platforms.

PolicySim, WWW'26

We detect automated and abnormal user behavior to help platforms manage operational risks.

NGUARD+, TKDD'20 NGUARD, KDD'18

Selected Publication List

2026
  • Jiarong Xu, Jiaan Wang, Hongzhe Zhang and Tian Lu. A User Purchase Motivation-Aware Product Recommender System. Information Systems Research, forthcoming. [PDF]
  • Jiarong Xu#, Jiaan Wang, Zenan Zhou and Tian Lu. Toward Graph Data Collaboration in a Data-Sharing-Free Manner: A Novel Privacy-Preserving Graph Pre-training Model. INFORMS Journal on Computing, forthcoming. [PDF] (#: corresponding author)
  • Renhong Huang, Ning Tang, Jiarong Xu#, Yuxuan Cao, Qingqian Tu, Sheng Guo, Bo Zheng, Huiyuan Liu and Yang Yang. PolicySim: An LLM-Based Agent Social Simulation Sandbox for Proactive Policy Optimization. In Proceedings of the 35th Web Conference (WWW'26). [PDF] [Code] (#: corresponding author)
  • Siwei Zhang, Yun Xiong, Xi Chen, Yateng Tang, Zian Jia, Xuehao Zheng, and Jiarong Xu. Think-like-LSTM: Memory-Augmented Large Language Models via Dynamic Fine-Tuning for Financial Risk Assessment. In Proceedings of the 32th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'26).
  • Siwei Zhang, Yun Xiong, Xi Chen, Zian Jia, Renhong Huang, Jiarong Xu, and Jiawei Zhang. RAPO: Expanding Exploration for LLM Agents via Retrieval-Augmented Policy Optimization. In Proceedings of the 32th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'26).
    • 2025
  • Yuxuan Cao, Jiarong Xu#, Chen Zhao, Jiaan Wang, Carl Yang, Chunping Wang and Yang Yang. How to use Graph Data in the Wild to Help Graph Anomaly Detection? In Proceedings of the 31th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'25). [PDF] [Code] (#: corresponding author)
  • Hanyang Yuan, Ning Tang, Tongya Zheng#, Jiarong Xu#, Xintong Hu, Renhong Huang, Shunyu Liu, Jiacong Hu, Jiawei Chen and Mingli Song. Tree of Preferences for Diversified Recommendation. In Proceedings of the 39th Conference on Neural Information Processing System (NeurIPS’25). (#: corresponding author) [PDF]
  • Siwei Zhang, Yun Xiong, Yateng Tang, Jiarong Xu, Xi Chen, Zehao Gu, Xuehao Zheng, Zian Jia and Jiawei Zhang. Unifying Text Semantics and Graph Structures for Temporal Text-attributed Graphs with Large Language Models. In Proceedings of the 39th Conference on Neural Information Processing System (NeurIPS’25). [PDF]
  • Xi Chen, Yateng Tang, Jiarong Xu, Jiawei Zhang, Siwei Zhang, Sijia Peng, Xuehao Zheng and Yun Xiong. Rethinking Time Encoding via Learnable Transformation Functions. In Proceedings of the 42nd International Conference on Machine Learning (ICML'25). [PDF] [Code]
  • Zehao Gu, Yateng Tang, Jiarong Xu, Siwei Zhang, Xuehao Zheng, Xi Chen and Yun Xiong. SSH-T^3 : A Hierarchical Pre-training Framework for Multi-Scenario Financial Risk Assessment. In Proceedings of the 34th ACM International Conference on Information and Knowledge Management (CIKM'25). [PDF]
  • Jiaan Wang, Fandong Meng, Zengkui Sun, Yunlong Liang, Yuxuan Cao, Jiarong Xu, Haoxiang Shi and Jie Zhou. An Empirical Study of Many-to-Many Summarization with Large Language Models. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (ACL'25). [PDF]
  • Yiran Qiao, Xiang Ao, Yang Liu, Jiarong Xu, Xiaoqian Sun and Qing He. LOGIN: A Large Language Model Consulted Graph Neural Network Training Framework. In Proceedings of the 18th International Conference on Web Search and Data Mining (WSDM'25). [PDF]
  • Ziqi Yang, Yiran Zhu, Jie Wan, ChuXiao Xiang, Tong Tang, Yilin Wang, Ruite Xu, Lijin Wang, Fan Zhang, Jiarong Xu and Zhan Qin. Defending Data Inference Attacks Against Machine Learning Models by Mitigating Prediction Distinguishability. In IEEE Transactions on Dependable and Secure Computing (TDSC), 2025. [PDF]
    • 2024
  • Hanyang Yuan, Jiarong Xu#, Renhong Huang, Mingli Song, Chunping Wang and Yang Yang. Can Graph Neural Networks Expose Training Data Properties? An Efficient Risk Assessment Approach. In Proceedings of the 38th Conference on Neural Information Processing System (NeurIPS’24). [PDF][Code] (#: corresponding author)
  • Renhong Huang, Jiarong Xu#, Zhiming Yang, Xiang Si, Xin Jiang, Hanyang Yuan, Chunping Wang and Yang Yang. Extracting Training Data from Molecular Pre-trained Models. In Proceedings of the 38th Conference on Neural Information Processing System (NeurIPS’24). [PDF][Code] (#: corresponding author)
  • Zhou Zhang*, Dongzeng Tan*, Jiaan Wang, Yilong Chen and Jiarong Xu#. Unleashing the Power of Emojis in Texts via Self-supervised Graph Pre-Training. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing (EMNLP’24). [PDF] (*: equal controbution, #: corresponding author)
  • Renhong Huang, Jiarong Xu#, Xin Jiang, Ruichuan An, and Yang Yang. Can Modifying Data Address Graph Domain Adaptation? In Proceedings of the 30th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'24). [PDF][Code] (#: corresponding author)
  • Hanyang Yuan, Jiarong Xu#, Cong Wang, Ziqi Yang, Chunping Wang, Keting Yin and Yang Yang. Unveiling Privacy Vulnerabilities: Investigating the Role of Structure in Graph Data. In Proceedings of the 30th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'24). [PDF][Code] (#: corresponding author)
  • Renhong Huang*, Jiarong Xu*#, Xin Jiang, Chenglu Pan, Zhiming Yang, Chunping Wang, and Yang Yang. Measuring Task Similarity and Its Implication in Fine-Tuning Graph Neural Networks. In Proceedings of the 38th Conference on Artificial Intelligence (AAAI'24). [PDF][Code] (*: equal contribution, #: corresponding author)
  • Chenglu Pan*, Jiarong Xu*#, Yue Yu, Ziqi Yang, Qingbiao Wu, Chunping Wang, Lei Chen, and Yang Yang. Towards Fair Graph Federated Learning via Incentive Mechanisms. In Proceedings of the 38th Conference on Artificial Intelligence (AAAI'24). [PDF] [Code] (*: equal contribution, #: corresponding author)
  • Junlong Liao*, Wenda Fu*, Cong Wang, Zhongyu Wei, and Jiarong Xu#. Value at Adversarial Risk: A Graph Defense Strategy Against Cost-Aware Attacks. In Proceedings of the 38th Conference on Artificial Intelligence (AAAI'24). [PDF] [Code] (*: equal contribution, #: corresponding author)
  • Jiaan Wang, Yunlong Liang, Zengkui Sun, Yuxuan Cao, Jiarong Xu# and Fandong Meng. Cross-Lingual Knowledge Editing in Large Language Models. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (ACL’24). [PDF] (#: corresponding author)
    • 2023
  • Jiarong Xu, Renhong Huang, Xin Jiang, Yuxuan Cao, Carl Yang, Chunping Wang, and Yang Yang. Better with Less: A Data-Active Perspective on Pre-Training Graph Neural Networks. In Proceedings of the 37th Conference on Neural Information Processing System (NeurIPS’23). [PDF] [Code]
  • Yuxuan Cao*, Jiarong Xu*#, Carl Yang, Jiaan Wang, Yunchao Mercer Zhang, Chunping Wang, Lei Chen, and Yang Yang. When to Pre-Train Graph Neural Networks? From Data Generation Perspective! In Proceedings of the 29th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'23). [PDF] [Code] [Poster] (*: equal contribution, #: corresponding author)
  • Haoyu Kuang, Jiarong Xu#, Haozhe Zhang, Zuyu Zhao, Qi Zhang, Xuanjing Huang, and Zhongyu Wei#. Unleashing the Power of Language Models in Text-Attributed Graph. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP’23 Findings). [PDF] (#: corresponding author)
  • Ruoxue Ma, Jiarong Xu#, Xinnong Zhang, Haozhe Zhang, Zuyu Zhao, Qi Zhang, Xuanjing Huang, and Zhongyu Wei#. One-Model-Connects-All: A Unified Graph Pre-Training Model for Online Community Modeling. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP’23 Findings). [PDF] (#: corresponding author)
  • Taoran Fang, Zhiqing Xiao, Chunping Wang, Jiarong Xu, Xuan Yang, and Yang Yang. DropMessage: Unifying Random Dropping for Graph Neural Networks. In Proceedings of the 37th Conference on Artificial Intelligence (AAAI'23). [PDF] (Distinguished Paper Award)
  • Xinke Jiang, Zidi Qin, Jiarong Xu, and Xiang Ao. Incomplete Graph Learning via Attribute-Structure Decoupled Variational Auto-Encoder. In Proceedings of the 17th ACM International Conference on Web Search and Data Mining (WSDM'23).
  • Siyuan Wang, Zhongyu Wei, Jiarong Xu, Taishan Li, and Zhihao Fan. Unifying Structure Reasoning and Language Pre-training for Complex Reasoning Tasks. In IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP), 2023. [PDF]
  • Jiaan Wang, Fandong Meng, Yunlong Liang, Tingyi Zhang, Jiarong Xu, Zhixu Li, Jie Zhou. Understanding Translationese in Cross-Lingual Summarization. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP’23 Findings). [PDF]
    • 2022
  • Jiarong Xu, Yang Yang, Junru Chen, Xin Jiang, Chunping Wang, Jiangang Lu and Yizhou Sun. Unsupervised Adversarially Robust Representation Learning on Graphs. In Proceedings of the 36th Conference on Artificial Intelligence (AAAI’22). [PDF] [Code]
  • Jiarong Xu, Yizhou Sun, Xin Jiang, Yanhao Wang, Chunping Wang, Jiangang Lu and Yang Yang. Blindfolded Attackers Still Threatening: Strict Black-Box Adversarial Attacks on Graphs. In Proceedings of the 36th Conference on Artificial Intelligence (AAAI’22). [PDF] [Code]
  • Jiarong Xu, Yang Yang, Chunping Wang, Zongtao Liu, Jing Zhang, Lei Chen and Jiangang Lu. Robust Network Enhancement from Flawed Networks. In IEEE Transactions on Knowledge and Data Engineering (TKDE), 2022. [PDF] [Code]
  • Jiarong Xu, Yang Yang, Shiliang Pu, Yao Fu, Jun Feng, Weihao Jiang, Jiangang Lu, and Cunping Wang. NetRL: Task-aware Network Denoising via Deep Reinforcement Learning. In IEEE Transactions on Knowledge and Data Engineering (TKDE), 2023. [PDF] [Code]
  • Xuanwen Huang, Yang Yang, Yang Wang, Chunping Wang, Zhisheng Zhang, Jiarong Xu, and Lei Chen. DGraph: A Large-Scale Financial Dataset for Graph Anomaly Detection. In Proceedings of the 36th Conference on Neural Information Processing System Track on Datasets and Benchmarks (NeurIPS’22 D&B). [PDF] [DGraph Data]
  • Ziwei Chai, Siqi You, Yang Yang, Shiliang Pu, Jiarong Xu, Haoyang Cai, and Weihao Jiang. Can Abnormality be Detected by Graph Neural Networks? In Proceedings of the 31st International Joint Conference on Artificial Intelligence (IJCAI'22). [PDF] [Code]
  • Yifei Sun, Haoran Deng, Yang Yang, Chunping Wang, Jiarong Xu, Renhong Huang, Linfeng Cao, Yang Wang and Lei Chen. Beyond Homophily: Structure-aware Path Aggregation Graph Neural Network. In Proceedings of the 31st International Joint Conference on Artificial Intelligence (IJCAI'22). [PDF] [Code]
    • 2021 and prior
  • Jiarong Xu, Yifan Luo, Jianrong Tao, Changjie Fan, Zhou Zhao and Jiangang Lu. NGUARD+: A Attention-based Game Bot Detection Framework via Player Behavior Sequences. In ACM Transactions on Knowledge Discovery from Data (TKDD), 2020. [PDF]
  • Jianrong Tao*, Jiarong Xu*, Linxia Gong, Yifu Li, Changjie Fan and Zhou Zhao. NGUARD: A Game Bot Detection Framework for NetEase MMORPGs. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'18). [PDF] (*: equal contribution)
  • Qinkai Zheng, Xu Zou, Yuxiao Dong, Yukuo Cen, Da Yin, Jiarong Xu, Yang Yang, and Jie Tang. Graph Robustness Benchmark: Benchmarking the Adversarial Robustness of Graph Machine Learning. In Proceedings of the 35th Conference on Neural Information Processing Systems Track on Datasets and Benchmarks (NeurIPS’21 D&B). [PDF] [Code]
  • Yiren Shen, Xiaoxian Ou, Jiarong Xu, Guanglin Zhang, Lin Wang and Dapeng Li. Optimal energy storage management for microgrids with ON/OFF co-generator: A two-time-scale approach. In Proceedings of the 3rd IEEE Conference on Signal and Information Processing (GlobalSIP'15). [PDF]
  • Chen Chen, Xueyuan Li, Ye Yang, Jiarong Xu, Zuwei Liao, Xinggao Liu and Jinshui Chen and Jiangang Lu. Parameter Self-Tuning of SISO Compact-Form Model-Free Adaptive Controller based on Neural Network with System Error Set as Input. In Proceedings of the 12th Asian Control Conference (ASCC'19). [PDF]
  • Ye Yang, Jinhou Han, Chen Chen, Jiarong Xu, Zuwei Liao, Xinggao Liu, Jinshui Chen and Jiangang Lu. A PSO-LP Cooperative Algorithm for Mixed Integer Nonlinear Programming. In Proceedings of the 12th Asian Control Conference (ASCC'19). [PDF]

    • Contact