Ruofan Wu

Professional Services

Research & Publications

^† denotes equal contribution. Default author ordering is alphabetical.

Preprints

• Toward a unified framework for data-efficient evaluation of large language models
  Lele Liao^†, Qile Zhang^†, Ruofan Wu^†, Guanhua Fang
• Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data
  Wei Guo, Yiyang Duan, Zhaojun Hu, Yiqi Tong, Fuzhen Zhuang, Xiao Zhang, Jin Dong, Ruofan Wu, Tengfei Liu, Yifan Sun
• Are Large Language Models In-Context Graph Learners?
  Jintang Li, Ruofan Wu, Yuchang Zhu, Huizhe Zhang, Liang Chen, Zibin Zheng
• Revisiting and Benchmarking Graph Autoencoders: A Contrastive Learning Perspective
  Jintang Li, Ruofan Wu, Yuchang Zhu, Huizhe Zhang, Xinzhou Jin, Guibin Zhang, Zulun Zhu, Zibin Zheng, Liang Chen
• Ultra-imbalanced classification guided by statistical information
  Yin Jin, Ningtao Wang, Ruofan Wu, Pengfei Shi, Xing Fu, Weiqiang Wang
• LasTGL: An Industrial Framework for Large-Scale Temporal Graph Learning
  Jintang Li, Jiawang Dan, Ruofan Wu, Jing Zhou, Sheng Tian, Yunfei Liu, Baokun Wang, Changhua Meng, Weiqiang Wang, Yuchang Zhu, Liang Chen, Zibin Zheng
• Scaling Up, Scaling Deep: Blockwise Graph Contrastive Learning
  Jintang Li, Wangbin Sun, Ruofan Wu, Yuchang Zhu, Liang Chen, Zibin Zheng
• Less Can Be More: Unsupervised Graph Pruning for Large-scale Dynamic Graphs
  Jintang Li, Sheng Tian, Ruofan Wu, Liang Zhu, Welong Zhao, Changhua Meng, Liang Chen, Zibin Zheng, Hongzhi Yin
• sqSGD: Locally Private and Communication Efficient Federated Learning
  Yan Feng, Tao Xiong, Ruofan Wu, LingJuan Lv, Leilei Shi

Conference & Journal Papers

• Transformers as Unsupervised Learning Algorithms: A study on Gaussian Mixtures
  Zhiheng Chen^†, Ruofan Wu^†, Guanhua Fang
  International Conference on Learning Representations (ICLR), 2026
• Heterophily-aware Representation Learning on Heterogenerous Graphs
  Jintang Li, Zheng Wei, Yuchang Zhu, Ruofan Wu, Huizhe Zhang, Liang Chen, Zibin Zheng
  IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 47(9), 7852-7866, 2025
• Privacy Risks of Federated Knowledge Graph Embedding: New Membership Inference Attacks and Personalized Differential Privacy Defense
  Yuke Hu, Yang Wang, Jian Lou, Wei Liang, Ruofan Wu, Weiqiang Wang, Xiaochen Li, Jinfei Liu, Zhan Qin
  IEEE Transactions on Dependable and Secure Computing, 22(3), 2788-2805, 2025
• Resource-Aware Federated Self-Supervised Learning with Global Class Representations
  Mingyi Li, Xiao Zhang, Qi Wang, Tengfei Liu, Ruofan Wu, Weiqiang Wang, Fuzhen Zhuang, Hui Xiong, Dongxiao Yu
  Neural Information Processing Systems (NeurIPS), 2024
• State Space Models on Temporal Graphs: A First-Principles Study
  Jintang Li^†, Ruofan Wu^†, Xinzhou Jin, Boqun Ma, Liang Chen, Zibin Zheng
  Neural Information Processing Systems (NeurIPS), 2024
• On provable privacy vulnerabilities of graph representations
  Ruofan Wu^†, Guanhua Fang^†, Mingyang Zhang, Qiying Pan, Tengfei Liu, Weiqiang Wang
  Neural Information Processing Systems (NeurIPS), 2024
• Revisiting Modularity Maximization for Graph Clustering: A Contrastive Learning Perspective
  Yunfei Liu, Jintang Li, Yuehe Chen, Ruofan Wu, Baokun Wang, Jing Zhou, Sheng Tian, Shuheng Shen, Xing Fu, Changhua Meng, Weiqiang Wang, Liang Chen
  SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2024
• A Graph is Worth 1-bit Spikes: When Graph Contrastive Learning Meets Spiking Neural Networks
  Jintang Li, Huizhe Zhang, Ruofan Wu, Zulun Zhu, Liang Chen, Zibin Zheng, Baokun Wang, Changhua Meng
  International Conference on Learning Representations (ICLR), 2024
• Neural Frailty Machine: Beyond proportional hazard assumption in neural survival regressions
  Ruofan Wu^†, Jiawei Qiao^†, Mingzhe Wu, Wen Yu, Ming Zheng, Tengfei Liu, Tianyi Zhang, Weiqiang Wang
  Neural Information Processing Systems (NeurIPS), 2023
• A Momentum Loss Reweighting Method for Improving Recall
  Chenzhi Jiang, Yin Jin, Ningtao Wang, Ruofan Wu, Xing Fu, Weiqiang Wang
  International Conference on Information & Knowledge Management (CIKM), 2023
• GUARD: Graph Universal Adversarial Defense
  Jintang Li, Jie Liao, Ruofan Wu, Liang Chen, Jiawang Dan, Changhua Meng, Zibin Zheng, Weiqiang Wang
  International Conference on Information & Knowledge Management (CIKM), 2023
• What's Behind the Mask: Understanding Masked Graph Modeling for Graph Autoencoders
  Jintang Li^†, Ruofan Wu^†, Wangbin Sun, Liang Chen, Sheng Tian, Liang Zhu, Changhua Meng, Zibin Zheng, Weiqiang Wang
  SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2023
• Quantifying and Defending against Privacy Threats on Federated Knowledge Graph Embedding
  Yuke Hu, Wei Liang, Ruofan Wu, Kai Xiao, Weiqiang Wang, Xiaochen Li, Jingfei Liu, Zhan Qin
  International World Wide Web Conference (WWW), 2023 (Spotlight)
• Scaling Up Dynamic Graph Representation Learning via Spiking Neural Networks
  Jintang Li, Zhouxin Yu, Zulun Zhu, Liang Chen, Qi Yu, Zibin Zheng, Sheng Tian, Ruofan Wu, Changhua Meng
  AAAI Conference on Artificial Intelligence (AAAI), 2023
• GRANDE: a neural model over directed multigraphs with application to anti-money laundering
  Ruofan Wu^†, Boqun Ma^†, Hong Jin, Wenlong Zhao, Weiqiang Wang, Tianyi Zhang
  International Conference on Data Mining (ICDM), 2022
• Design Domain Specific Neural Network via Symbolic Testing
  Hui Li^†, Xing Fu^†, Ruofan Wu^†, Kai Xiao, Xiaofu Chang, Weiqiang Wang, Shuai Chen, Leilei Shi, Tao Xiong, Yuan Qi
  SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2022
• Self-supervised Representation Learning on Dynamic Graphs
  Sheng Tian^†, Ruofan Wu^†, Leilei Shi, Liang Zhu, Tao Xiong
  International Conference on Information & Knowledge Management (CIKM), 2021
• Can Social Notifications Help to Mitigate Payment Delinquency in Online Peer-to-Peer Lending?
  Xianghua Lu, Tian Lu, Chong(Alex) Wang, Ruofan Wu
  Production and Operations Management, 30(8), 2564-2585, 2021
• Estimation and variable selection for semiparametric transformation models under a more efficient cohort sampling design
  Mingzhe Wu, Ming Zheng, Wen Yu, Ruofan Wu
  Test, 27, 570-596, 2018
• Subgroup analysis with time‐to‐event data under a logistic‐Cox mixture model
  Ruofan Wu, Ming Zheng, Wen Yu
  Scandinavian Journal of Statistics, 43(3), 863-878, 2016

Workshop Presentations

• Privacy-preserving design of graph neural networks with applications to vertical federated learning
  Ruofan Wu, Mingyang Zhang, Lingjuan Lyu, Xiaolong Xu, Xiuquan Hao, Xinyi Fu, Tengfei Liu, Tianyi Zhang, Weiqiang Wang
  NeurIPS 2023 New Frontiers in Graph Learning Workshop (NeurIPS GLFrontiers 2023)
• FedGKD: Unleashing the Power of Collaboration in Federated Graph Neural Networks
  Qiying Pan, Ruofan Wu, Tengfei Liu, Tianyi Zhang, Yifei Zhu, Weiqiang Wang
  NeurIPS 2023 New Frontiers in Graph Learning Workshop (NeurIPS GLFrontiers 2023)
• Self-supervision meets kernel graph neural models: From architecture to augmentations
  Jiawang Dan^†, Ruofan Wu^†, Yunpeng Liu, Baokun Wang, Changhua Meng, Tengfei Liu, Tianyi Zhang, Ningtao Wang, Xing Fu, Qi Li, Weiqiang Wang
  ICDM 2023 Machine Learning on Graph Workshop (ICDM MLoG 2023)