李翔 - 华东师范大学 - 数据科学与工程学院

个人简介

Xiang Li is currently a research professor and also Ph.D. advisor in the School of Data Science and Engineering at East China Normal University, leading the PLANING lab (graPh mining and LANguage processING ☺). Before that, he received his Ph.D. degree from The University of Hong Kong in 2018, M.Eng. degree from University of Science and Technology of China in 2014 and B.Eng. degree from Nanchang University in 2011, respectively. Previously, he worked as a research scientist in the Data Science Lab at JD.com from Aug 2018 to July 2019 and a postdoc researcher in The University of Hong Kong from July 2019 to Dec 2020. Experience Research Scientist, Data Science Lab, JD.com, Aug. 2018 - July. 2019. Engineering Intern, Artificial Intelligence Group, CaiNiao, Alibaba, July. 2017 - Aug. 2017. Visiting Research Student, Database and Data Mining Lab, supervised by Prof. Martin Ester , Simon Fraser University, May 2016 - Aug. 2016. Research Assistant, Data Mining and Data Management Group, The University of Hong Kong, Sept. 2014 - present. Research Assistant, Information Technology Security Evaluation Center, University of Science and Technology of China, Sept. 2011 - Jun. 2014. Selected Honors HKU Postgraduate Scholarship, HKU, 2014 - 2018 GDC Technology Scholarship (Extraordinary Graduate), USTC, 2013 Excellent Graduate, NCU, 2011 Outstanding Student Scholarship (TOP Level), NCU, 2007-2011

研究领域

His general research interests focus on data mining and applied machine learning, in particular, on graph mining, semi-supervised learning and part of NLP related tasks. Generally speaking, he has spent lots of efforts in classification, clustering, link prediction and other generative tasks in graphs. Moreover, he likes to explore some misc problems of challenge, such as reinforcement learning, crowdsourcing and NLP techniques. Specifically, his research interests include: Machine learning: semi-supervised learning, self-supervised learning, weakly-supervised learning, graph-based learning; Data mining: graph neural networks, heterogeneous graph mining, OOD generalizability; Language processing: large language models (LLMs), code representation learning, prompt tuning; Data privacy: federated learning, data heterogeneity, model heterogeneity.

近期论文

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Self-Pro: Self-Prompt and Tuning Framework for Graph Neural Networks, Chenghua Gong, Xiang Li*, Jianxiang Yu, Yao Cheng, Jiaqi Tan, Chengcheng Yu. In ECML-PKDD 2024, Vilnius, Lithuania. PSP: Pre-Training and Structure Prompt Tuning for Graph Neural Networks, Qingqing Ge, Zeyuan Zhao, Yiding Liu, Anfeng Cheng, Xiang Li*, Shuaiqiang Wang, Dawei Yin. In ECML-PKDD 2024, Vilnius, Lithuania. HetCAN: A Heterogeneous Graph Cascade Attention Network with Dual-Level Awareness, Zeyuan Zhao, Qingqing Ge, Anfeng Cheng, Yiding Liu, Xiang Li*, Shuaiqiang Wang. In ECML-PKDD 2024, Vilnius, Lithuania. Resurrecting Label Propagation for Graphs with Heterophily and Label Noise, Yao Cheng, Caihua Shan, Yifei Shen, Xiang Li*, Siqiang Luo, Dongsheng Li. In KDD 2024, Barcelona, Spain. Boosting Language Models Reasoning with Chain-of-Knowledge Prompting, Jianing Wang, Qiushi Sun, Xiang Li*, Ming Gao. In ACL 2024, Bangkok, Thailand. Heterogeneous Graph Contrastive Learning with Meta-path Contexts and Adaptively Weighted Negative Samples, Jianxiang Yu, Qingqing Ge, Xiang Li*, Aoying Zhou. In TKDE 2024. Conjoin After Decompose: Improving Few-Shot Performance of Named Entity Recognition, Chengcheng Han, Renyu Zhu, Jun Kuang, Fengjiao Chen, Xiang Li*, Ming Gao, Xuezhi Cao, Yunsen Xian. In LREC-COLING 2024, Torino, Italia. Structure-aware Fine-tuning for Code Pre-trained Models, Jiayi Wu, Renyu Zhu, Nuo Chen, Qiushi Sun, Xiang Li, Ming Gao. In LREC-COLING 2024, Torino, Italia. Make Prompt-based Black-Box Tuning Colorful: Boosting Model Generalization from Three Orthogonal Perspectives, Qiushi Sun, Chengcheng Han, Nuo Chen, Renyu Zhu, Jingyang Gong, Xiang Li*, Ming Gao. In LREC-COLING 2024, Torino, Italia. TransCoder: Towards Unified Transferable Code Representation Learning Inspired by Human Skills, Qiushi Sun, Nuo Chen, Jianing Wang, Xiang Li*, Ming Gao. In LREC-COLING 2024, Torino, Italia. BapFL: You can Backdoor Personalized Federated Learning, Tiandi Ye, Cen Chen, Yinggui Wang, Xiang Li, Ming Gao. In TKDD 2024. Training-free Multi-objective Diffusion Model for 3D Molecule Generation, Xu Han, Caihua Shan, Yifei Shen, Can Xu, Han Yang, Xiang Li, Dongsheng Li. In ICLR 2024, Vienna, Austria. UPFL: Unsupervised Personalized Federated Learning towards New Clients, Tiandi Ye, Cen Chen, Yinggui Wang, Xiang Li, Ming Gao. In SDM 2024, Houston, USA Federated Learning via Consensus Mechanism on Heterogeneous Data: A New Perspective on Convergence, Shu Zheng, Tiandi Ye, Xiang Li*, Ming Gao. In ICASSP 2024, Seoul, Korea. Scalable Decoupling Graph Neural Network with Feature-Oriented Optimization, Ningyi Liao, Dingheng Mo, Siqiang Luo, Xiang Li, Pengcheng Yin. In VLDBJ 2023. DialCoT Meets PPO: Decomposing and Exploring Reasoning Paths in Smaller Language Models, Chengcheng Han, Xiaowei Du, Che Zhang, Yixin Lian, Xiang Li, Ming Gao, Baoyuan Wang. In EMNLP 2023, Singapore. Pass-Tuning: Towards Structure-Aware Parameter-Efficient Tuning for Code Representation Learning, Nuo Chen, Qiushi Sun, Jianing Wang, Xiang Li, Ming Gao. In EMNLP Findings 2023, Singapore. Uncertainty-aware Parameter-Efficient Self-training for Semi-supervised Language Understanding, Jianing Wang, Qiushi Sun, Nuo Chen, Chengyu Wang, Xiang Li, Ming Gao, Jun Huang. In EMNLP Findings 2023, Singapore. Evaluating and Enhancing the Robustness of Code Pre-trained Models through Structure-Aware Adversarial Samples Generation, Nuo Chen, Qiushi Sun, Jianing Wang, Xiaoli Li, Xiang Li, Ming Gao. In EMNLP Findings 2023, Singapore. DFRD: Data-Free Robustness Distillation for Heterogeneous Federated Learning, Kangyang Luo, Shuai Wang, Yexuan Fu, Xiang Li*, Yunshi Lan and Ming Gao. In NeurIPS 2023, New Orleans, USA. LD2: Scalable Heterophilous Graph Neural Network with Decoupled Embedding, Ningyi Liao, Siqiang Luo, Xiang Li, and Jieming Shi. In NeurIPS 2023, New Orleans, USA. Graph Self-Contrast Representation Learning, Minjie Chen, Yao Cheng, Ye Wang, Xiang Li*, and Ming Gao. In ICDM 2023, Shanghai, China. (Regular paper, Top 9.37%) DropMix: Better Graph Contrastive Learning with Harder Negative Samples, Yueqi Ma, Minjie Chen and Xiang Li*. In ICDM Workshop 2023, Shanghai, China. MUSE: Multi-view contrastive learning for heterophilic graphs via information reconstruction, Mengyi Yuan, Minjie Chen and Xiang Li*. In CIKM 2023, Birmingham, UK Decentralized Local Updates with Dual-Slow Estimation and Momentum-based Variance-Reduction for Non-Convex Optimization, Kangyang Luo, Kunkun Zhang, Shengbo Zhang, Xiang Li* and Ming Gao. In ECAI 2023, Kraków, Poland When Gradient Descent Meets Derivative-Free Optimization: A Match Made in Black-Box Scenario, Chengcheng Han, Liqing Cui, Renyu Zhu, Jianing Wang, Nuo Chen, Qiushi Sun, Xiang Li and Ming Gao. In ACL Findings 2023, Toronto, Canada. GradMA: A Gradient-Memory-based Accelerated Federated Learning with Alleviated Catastrophic Forgetting, Kangyang Luo, Xiang Li*, Yunshi Lan and Ming Gao. In CVPR 2023, Vancouver, Canada. (Highlight, Top 2.5%) SeeGera: Self-supervised Semi-implicit Graph Variational Auto-encoders with Masking, Xiang Li, Tiandi Ye, Caihua Shan, Dongsheng Li and Ming Gao. In WebConf 2023, Austin, Texas, USA. Explaining Temporal Graph Models through an Explorer-Navigator Framework, Wenwen Xia, Mincai Lai, Caihua Shan, Yao Zhang, Xinnan Dai, Xiang Li, Dongsheng Li. In ICLR 2023, Kigali Rwanda. Meta-Learning Siamese Network for Few-Shot Text Classification, Chengcheng Han, Yuhe Wang, Yingnan Fu, Xiang Li*, Minghui Qiu, Ming Gao and Aoying Zhou. In DASFAA 2023, Tianjin, China. Heterogeneous Graph Contrastive Learning with Meta-path Contexts and Weighted Negative Samples, Jianxiang Yu and Xiang Li*. In SDM 2023, Minneapolis, Minnesota, USA. Knowledge Prompting in Pre-trained Language Model for Natural Language Understanding, Jianing Wang, Wenkang Huang, Minghui Qiu, Qiuhui Shi, Hongbin Wang, Xiang Li* and Ming Gao. In EMNLP 2022, Abu Dhabi, UAE. CAT-probing: A Metric-based Approach to Interpret How Pre-trained Models for Programming Language Attend Code Structure, Nuo Chen, Qiushi Sun, Renyu Zhu, Xiang Li*, Xuesong Lu and Ming Gao. In Findings of EMNLP 2022, Abu Dhabi, UAE. Finding Global Homophily in Graph Neural Networks When Meeting Heterophily, Xiang Li, Renyu Zhu, Yao Cheng, Caihua Shan, Siqiang Luo, Dongsheng Li, Weining Qian. In ICML 2022, Baltimore, USA. [Slices] SCARA: Scalable Graph Neural Networks with Feature-Oriented Optimization, Ningyi Liao, Dingheng Mo, Siqiang Luo, Xiang Li, Pengcheng Yin. In PVLDB 2022, Sydney, Australia. Lexical Knowledge Internalization for Neural Dialog Generation, Zhiyong Wu, Wei Bi, Xiang Li, Lingpeng Kong, Ben Kao. In ACL 2022, Virtual Conference. A Neural Network Architecture for Program Understanding Inspired by Human Behaviors, Renyu Zhu, Lei Yuan, Xiang Li*, Ming Gao, Wenyuan Cai. In ACL 2022, Virtual Conference. Reinforcement Learning Enhanced Explainer for Graph Neural Networks, Caihua Shan, Yifei Shen, Yao Zhang, Xiang Li, Dongsheng Li. In NeurIPS 2021, Virtual Conference. Good for Misconceived Reasons: An Empirical Revisiting on the Need for Visual Context in Multimodal Machine Translation, Zhiyong Wu, Lingpeng Kong, Wei Bi, Xiang Li, Ben Kao. In ACL 2021, Virtual Conference. Leveraging meta-path contexts for classification in heterogeneous information networks, Xiang Li, Danhao Ding, Ben Kao, Yizhou Sun, Nikos Mamoulis. In ICDE 2021, Virtual Conference. [Project Codes] Disentangling user interest and popularity bias for recommendation with causal embedding, Yu Zheng, Chen Gao, Xiang Li, Xiangnan He, Yong Li, Depeng Jin. In WWW 2021, Virtual Conference. SceneRec: Scene-Based Graph Neural Networks for Recommender Systems, Gang Wang, Ziyi Guo, Xiang Li, Dawei Yin, Shuai Ma. In EDBT 2021, Virtual Conference. CAST: A Correlation-based Adaptive Spectral Clustering Algorithm on Multi-scale Data, Xiang Li, Ben Kao, Caihua Shan, Dawei Yin, Martin Ester. In KDD 2020, Virtual Conference. [Project Codes] SCHAIN-IRAM: An Efficient and Effective Semi-supervised Clustering Algorithm for Attributed Heterogeneous Information Networks, Xiang Li, Yao Wu, Martin Ester, Ben Kao, Xin Wang, and Yudian Zheng. In TKDE 2020. An End-to-End Deep RL Framework for Task Arrangement in Crowdsourcing Platforms, Caihua Shan, Nikos Mamoulis, Reynold Cheng, Guoliang Li, Xiang Li and Yuqiu Qian. In ICDE 2020, Virtual Conference. A General Early-Stopping Module for Crowdsourced Ranking, Caihua Shan, Leong Hou U, Nikos Mamoulis, Reynold Cheng and Xiang Li. In DASFAA 2020, Virtual Conference. Spectral clustering in heterogeneous information networks, Xiang Li, Ben Kao, Zhaochun Ren, Dawei Yin. In AAAI 2019, Honolulu, USA. [Project Codes] ROSC: robust spectral clustering on multi-scale data, Xiang Li, Ben Kao, Siqiang Luo, Martin Ester. In WWW 2018, Lyon, France. [Project Codes] Semi-supervised clustering in attributed heterogeneous information networks, Xiang Li, Yao Wu, Martin Ester, Ben Kao, Xin Wang, and Yudian Zheng. In WWW 2017, Perth, Australia. [Project Codes] On transductive classification in heterogeneous information networks, Xiang Li, Ben Kao, Yudian Zheng, and Zhipeng Huang. In CIKM 2016, Indianapolis, USA. Computing relevance in large heterogeneous information networks, Zhipeng Huang, Yudian Zheng, Reynold Cheng, Yizhou Sun, Nikos Mamoulis, and Xiang Li. In KDD 2016, San Francisco, USA. Classification with active learning and meta-paths in heterogeneous information networks, Chang Wan, Xiang Li, Ben Kao, Xiao Yu, Quanquan Gu, David Cheung, and Jiawei Han. In CIKM 2015, Melbourne, Australia. Novel user influence measurement based on user interaction in microblog, Xiang Li, Shaoyin Cheng, Wenlong Chen, and Fan Jiang. In ASONAM 2013, Niagara Falls, Canada.