李明，教授，博士生导师，入选浙江省第二批高校“院士专家结对培育青年英才计划”（同年全省仅56人）人才、高校高层次拔尖人才 (第二批“浙江省高校领军人才计划”培养人选，即“5246人才工程”)、 “钱江人才计划”紧缺急需人才，入选国际理论物理中心ICTP Regular Associate协联成员。现任浙江省智能教育技术与应用重点实验室副主任，图机器学习与智能教育实验室 (GraphME Lab)负责人，中国人工智能学会、中国计算机学会会员，中国人工智能学会­知识工程与分布智能专委会委员，中国人工智能学会机器学习专业委员会通讯委员，CCF-AI专委会图机器学习学组首届秘书处成员。担任Pattern Recognition、Neural Networks、Machine Learning在内的多个国际权威期刊副主编/编委，曾任神经网络方向国际权威期刊IEEE TNNLS专刊首席特邀副主编。在IEEE TPAMI、Artificial Intelligence、IEEE TKDE、SCIENCE CHINA-Information Sciences等期刊及ICML/NeurIPS/AAAI等国际会议上发表论文100余篇，其中第一或通讯作者发表的CCF/CAA/CAAI-A类论文40多篇（含6篇ESI高被引、2篇ESI热点论文、7篇Oral 会议论文），第一作者的论文成果荣获AAAI 2026大会杰出论文奖（Outstanding Paper Award）。谷歌学术引用近6000次（h-index: 40），入选全球前2%顶尖科学家榜单（2024-2025）。担任ICML/NeurIPS/IJCAI/KDD/ICLR等国际会议的AC/SPC/PC Member。主持国家自然科学基金面上项目1项（已结题）、青年科学基金项目（C类）1项（已结题）、浙江省 “领雁”研发攻关计划项目（即省重点研发计划项目）1项（在研）；作为合作单位项目负责人联合承担国家自然科学基金区域创新发展联合基金重点项目1项（已结题）；作为核心成员参与国家科技创新2030—“新一代人工智能”重大项目课题1项、浙江省重点研发计划项目2 项（已结题）。 授权发明专利10余项，相关图神经网络技术成果已在知识追踪、学习行为分析等智能教育场景中实现技术落地，并成功应用于科大讯飞多个智能教育平台服务中，产生了良好的经济效益与示范效应。相关成果获浙江省科学技术进步奖一等奖1项，第二十五届中国专利优秀奖1项。获第三届全国高校教师教学创新大赛浙江省二等奖 (新工科正高组)。近年来指导硕博士研究生获批浙江省大学生科技创新活动计划（新苗人才计划）项目3项、浙江省教育厅一般项目4项；指导学生获得“华为杯”第五届中国研究生人工智能创新大赛三等奖1项；指导的学生中已有6人获得研究生国家奖学金。

         其他科研成果及学术任职情况详见个人科研主页：https://mingli-ai.github.io/

🔆 图神经网络、图表示学习、超图表示学习等方向的代表作（含CCF/CAA/CAAI推荐A类期刊/会议38篇, ESI高被引/热点🏆11篇）：

● Deeper insights into deep graph convolutional networks: Stability and generalization (CCF/CAAI-A类, CAA-A⁺期刊)

   G. Yang, M. Li*, H. Feng, X. Zhuang
   IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 48, no. 2, pp. 1707-1719, 2026.

● Are graph convolutional networks with random weights feasible? ESI Highly Cited Paper 🏆 (CCF/CAAI-A类, CAA-A⁺期刊)

   C. Huang, M. Li*, F. Cao, H. Fujita, Z. Li, X. Wu
   IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 45, no. 3, pp. 2751-2768, 2023.

 ● Multi-view graph convolutional networks with attention mechanism  [code]  (CCF/CAA/CAAI-A类期刊)

   K. Yao, J. Liang, J. Liang, M. Li, F. Cao
   Artificial Intelligence, vol. 307, 103708, 2022.

● AEGK: Aligned Entropic Graph Kernels through continuous-time quantum walks   (CCF/CAA/CAAI-A类期刊)

   L. Bai, L. Cui, M. Li, P. Ren, Y. Wang, Y. Philip, L. Zhang, E. Hancock 

   IEEE Transactions on Knowledge and Data Engineering, vol. 37, no. 3, pp. 1064-1078, 2025.

● HAQJSK: Hierarchical-aligned quantum Jensen-Shannon kernels for graph classification  ESI Hot Paper 🏆 (CCF/CAA-CAAI-A类期刊)

   L. Bai, L. Cui, Y. Wang, M. Li*, J. Li, P. Yu, E. Hancock 

   IEEE Transactions on Knowledge and Data Engineering, vol. 36, no. 11, pp. 6370-6384, 2024.

 ● Collaborative knowledge graph fusion by exploiting the open corpus   (CCF/CAA/CAAI-A类期刊)

    Y. Wang, Y. Wan, L. Bai, L. Cui, Z. Xu, M. Li, P. Yu, E. Hancock 

   IEEE Transactions on Knowledge and Data Engineering, vol. 36, no. 2, pp. 475-489, 2024.

● Multi-topology contrastive graph representation learning   (CCF/CAA/CAAI-A类期刊)

    Y. Xie, J. Jia, C. Wen, D. Li, M. Li*

   SCIENCE CHINA Information Sciences, 2025.

● EduCross: Dual adversarial bipartite hypergraph learning for cross-modal retrieval in multimodal educational slides ESI Highly Cited Paper 🏆  (CAA-A⁺类期刊, CAAI A类期刊)

   M. Li, S. Zhou, Y. Chen, C. Huang, Y. Jiang*

   Information Fusion, vol. 109, 102428, 2024.

● Multimodal graph learning based on 3D Haar semi-tight framelet for student engagement prediction ESI Highly Cited Paper 🏆  (CAA-A⁺类期刊, CAAI-A类期刊)

   M. Li, X. Zhuang, L. Bai, W. Ding

   Information Fusion, vol. 105, 102224, 2024.

● High-pass matters: Theoretical insights and sheaflet-based design for hypergraph neural networks (CCF/CAA/CAAI-A类会议)

   M. Li, Y. Fang, D. Shen, H. Feng, X. Zhuang, K. Xia, P. Lio
   AAAI, 2026, pp. 23039-23046. (Oral Paper, Outstanding Paper Award🏆 )

● HyperNoRA: Hyperedge prediction via node-level relation-aware self-supervised hypergraph learning  (CCF/CAA/CAAI-A类会议)

   M. Li, Z. Zhu, X. Li, L. Bai, L. Cui, F. Cao, K. Lu
   AAAI, 2026, pp. 23047-23054.  (Oral Paper)

● Multi-granular graph learning with fine-grained behavioral pattern awareness for session-based recommendation  (CCF/CAA/CAAI-A类会议)

   M. Li, Z. Yan, Y. Chen, L. Bai, L. Cui, F. Cao, Z. Li, K. Lu
   AAAI, 2026, pp. 23030-23038.  (Oral Paper)

● Permutation equivariant framelet-based hypergraph neural networks (CCF/CAA/CAAI-A类会议)

   M. Li, Y. Wang, C. Gao, Y. Fang, L. Bai, X. Zhuang, P. Lio
   AAAI, 2026, pp. 23079-23086. (Oral Paper)

● HyperAim: Hypergraph contrastive learning with adaptive multi-frequency filters (CCF/CAA/CAAI-A类会议)

   M. Li, R. Zhao, Z. Yan, L. Bai, L. Cui, F. Cao
   AAAI, 2026, pp. 23063-23070.  (Oral Paper)

● Self-supervised hypergraph Learning with substructure awareness for hyperedge prediction (CCF/CAA/CAAI-A类会议)

   M. Li, H. Wang, Y. Chen, L. Bai, L. Cui, F. Cao, K. Lu
   AAAI, 2026, pp. 23055-23062.  (Oral Paper)

● Heterophily-aware contrastive learning for heterophilic hypergraphs (CCF/CAA/CAAI-A类会议)

   M. Li, Y. Li, Y. Chen, F. Cao, K. Lu
   AAAI, 2026, pp. 23071-23078.

● When hypergraph meets heterophily: New benchmark datasets and baseline (CCF/CAA/CAAI-A类会议)

   M. Li, Y. Gu, Y. Wang, Y. Fang, Lu Bai, X. Zhuang, P. Lio

   AAAI, 2025, pp. 18377-18384. 

● Deep hypergraph neural networks with tight framelets (CCF/CAA/CAAI-A类会议)

   M. Li, Y. Fang, Y. Wang, H. Feng, Y. Gu, L. Bai, P. Lio

   AAAI, 2025, pp. 18385-18392. (Oral Paper)

● DHAKR: Learning deep hierarchical attention-based kernelized representations for graph classification (CCF/CAA/CAAI-A类会议)

   F. Qian, L. Bai*, L. Cui, M. Li*, Z. Lyu, H. Du, E. Hancock

   AAAI, 2025, pp. 19995-20003.

● ML-GOOD: Towards multi-label graph out-of-distribution detection (CCF/CAA/CAAI-A类会议)

   T. Cai, Y. Jiang, M. Li, C. Huang, Y. Wang, Q. Huang

   AAAI, 2025, pp. 15650-15658.

● Framelet based dual hypergraph neural networks for student engagement prediction [poster] [Best Short Paper Award]

   M. Li*, J. Shi

   AI4ED-AAAI, 2024. Best Short Paper Award 🏆

● Path integral based convolution and pooling for graph neural networks [code] [PyG Implementation]  (CCF/CAA/CAAI-A类会议)

   Z. Ma, J. Xuan, Y. G. Wang, M. Li, P. Lio
   NeurIPS, 2020, pp. 16421-16433.

● HC-GAE: The hierarchical cluster-based graph auto-encoder for graph representation learning (CCF/CAA/CAAI-A类会议)

   Z. Xu, L. Bai, L. Cui, M. Li, Y. Wang, E. R. Hancock

   NeurIPS, 2024, pp. 127968-127986.

● Long-range brain graph transformer (CCF/CAA/CAAI-A类会议)

   S. Yu, S. Jin, M. Li, T. Sarwar, F. Xia
   NeurIPS, 2024, pp. 24472-24495.

●  EduLLM: Leveraging large language models and framelet-based signed hypergraph neural networks for student performance prediction(CCF/CAA/CAAI-A类会议)

   M. Li, Y. Cheng, L. Bai*, F. Cao, K. Lu, J. Liang, P. Lio

   ICML, 2025, pp. 34119-3414.

● How universal polynomial bases enhance spectral graph neural networks: Heterophily, over-smoothing, and over-squashing [link][code] (CCF/CAA/CAAI-A类会议)

   K. Huang*, Y. G. Wang, M. Li*, P. Lio
   ICML, 2024, pp. 20310-20330.

● QBMK: Quantum-based matching kernels for un-attributed graphs [link] (CCF/CAA/CAAI-A类会议)

   L. Bai, L. Cui, M. Li, Y. Wang, E. Hancock

   ICML, 2024, pp. 2364-2374. (Spotlight Paper: 3.5% acceptance rate)

● Haar graph pooling [link][code] (CCF/CAA/CAAI-A类会议)

   Y. G. Wang, M. Li*, Z. Ma, G. Montufar, X. Zhuang, Y. Fan
   ICML, 2020, pp. 9952-9962.

● How framelets enhance graph neural networks [link] [code] (CCF/CAA/CAAI-A类会议)

   X. Zheng, B. Zhou, J. Gao, Y. G. Wang, P. Lio, M. Li, G. Montufar
   ICML, 2021, pp. 12761-12771. (Spotlight Paper)

● How powerful are shallow neural networks with bandlimited random weights? (CCF/CAA/CAAI-A类会议)

   M. Li, S. Sonoda, F. Cao, Y. G. Wang, J. Liang
   ICML, 2023, pp. 19960-19981.

● MATCH: Modality-calibrated hypergraph fusion network for conversational emotion recognition (CCF/CAA/CAAI-A类会议)

   J. Shi, M. Li*, L. Bai, F. Cao, K. Lu, J. Liang

   IJCAI, 2025, pp. 6164-6172.

● DHTAGK: Deep hierarchical transitive-aligned graph kernels for graph classification (CCF/CAA/CAAI-A类会议)

   X. Qin, L. Bai, L. Cui, M. Li*, Z. Lyu, H. Du, E. Hancock

   IJCAI, 2025, pp. 3254-3262.

● Stability and generalization of ℓp-regularized stochastic learning for GCN  (CCF/CAA/CAAI-A类会议)

   S. Lv, S. Liu, L. Wei, M. Li
   IJCAI, 2023, pp. 5685-5693.

● Guest Editorial: Deep Neural Networks for Graphs: Theory, Models, Algorithms and Applications [link] ESI Hot Paper 🏆 (CAA/CAAI-A类期刊, CCF-B类期刊)

   M. Li*, A. Micheli, Y. G. Wang, S. Pan, P. Lio, G. Gnecco, M. Sanguineti

   IEEE Transactions on Neural Networks and Learning Systems, vol. 35, no. 4, pp. 4367-4372, 2024.

● Permutation equivariant graph framelets for heterophilous graph learning [code] ESI Highly Cited Paper 🏆(CAA/CAAI-A类期刊, CCF-B类期刊)

   J. Li, R. Zheng, F. Han, M. Li*, X. Zhuang*

   IEEE Transactions on Neural Networks and Learning Systems, vol. 35, no. 9, pp. 11634-11648, 2024. 

● Towards graph self-supervised learning with contrastive adjusted zooming (CAA/CAAI-A类期刊, CCF-B类期刊)

   Y. Zheng, M. Jin, S. Pan, Y. F. Li, H. Peng, M. Li, Z. Li

   IEEE Transactions on Neural Networks and Learning Systems, vol. 35, no. 7, pp. 8882-8896, 2024.

● Flow2GNN: Flexible two-way flow message passing for enhancing GNNs beyond homophily (CAA/CAAI-A类期刊, CCF-B类期刊)

   C. Huang, Y. Wang, Y. Jiang, M. Li, X. Huang, S. Wang, S. Pan, C. Zhou 

   IEEE Transactions on Cybernetics, vol. 54, no. 11, pp. 6607-6618, 2024.

● Correlation information enhanced graph anomaly detection via hypergraph transformation (CAA/CAAI-A类期刊, CCF-B类期刊)

   C. Huang, C. Gao, M. Li, Y. Li, X. Wang, Y. Jiang, X. Huang

   IEEE Transactions on Cybernetics, 2025.

● A unified framework for exploratory learning-aided community detection under topological uncertainty 

   Y. Hou, C. Tran, M. Li, W. Shin

   IEEE Transactions on Network Science and Engineering, 2025. 

● A simple yet effective framelet-based graph neural network for directed graphs [code] (CAAI-B类期刊)

   C. Zou, A. Han, L. Lin, M. Li, J. Gao

   IEEE Transactions on Artificial Intelligence, vol. 5, no. 4, pp. 1647-1657, 2024. 

● Multiple pedestrian tracking with graph attention map on urban road scene (CAA-A类期刊, CCF/CAAI-B类期刊)

   Z. Wang, Z. Li, J. Leng, M. Li*, L. Bai

   IEEE Transactions on Intelligent Transportation Systems, vol. 24, no. 8, pp. 8567-8579, 2023.

● GoLoG: Global-to-local decoupling graph network with joint optimization for hyperspectral image classification (CAA/CAAI-A类期刊, CCF-B类期刊)

   B. Yang, H. Ye, M. Li*, F. Cao, S. Pan

   IEEE Transactions on Geoscience and Remote Sensing, vol. 61, 5528014, 2023.

●  EduGraph: Learning path-based hypergraph neural networks for MOOC course recommendation  

   M. Li, Z. Li*, C. Huang*, Y. Jiang, X. Wu

   IEEE Transactions on Big Data, vol. 10, no. 6, pp. 706-719, 2024.

● Fast Haar transforms for graph neural networks  (CAA-A类期刊, CCF/CAAI-B类期刊)

   M. Li, Z. Ma, Y. G. Wang, X. Zhuang
   Neural Networks, vol. 128, pp. 188-198, 2020.

● Embedding graphs on Grassmann manifold [code] (CAA/CAAI-A类期刊, CCF/CAAI-B类期刊)

   B. Zhou, X. Zheng, Y. G. Wang, M. Li, J. Gao

   Neural Networks, vol. 152, pp. 322-331, 2022.

● Revisiting graph neural networks from hybrid regularized graph signal reconstruction   (CAA/CAAI-A类期刊, CCF/CAAI-B类期刊)

   J. Miao, F. Cao, H. Ye, M. Li, B. Yang

   Neural Networks, vol. 157, pp. 444-459, 2023.

● FrameERC: Framelet transform based multimodal graph neural networks for emotion recognition in conversation ESI Highly Cited Paper 🏆 (CAA/CAAI-A类期刊, CCF/CAAI-B类期刊)

  M. Li, J. Shi, L. Bai, C. Huang, Y. Jiang, K. Lv, S. Wang, E. Hancock

   Pattern Recognition, 2025.

● Collaborative graph neural networks for augmented graphs: A local-to-global perspective  (CAA/CAAI-A类期刊, CCF/CAAI-B类期刊)

  Q. Guo, X. Yang, M. Li, Y. Qian

   Pattern Recognition, vol. 158, 111020, 2025.

● QBER: Quantum-based entropic representations for un-attributed graphs   (CAA/CAAI-A类期刊, CCF/CAAI-B类期刊)

   L. Cui,  M. Li, L. Bai, Y. Wang, J. Li, Y. Wang,  Z. Li, Y. Chen,  E. Hancock

   Pattern Recognition, vol. 145, 109877, 2024.

● AG-Meta: Adaptive graph meta learning via representation consistency over local subgraphs  (CAA/CAAI-A类期刊, CCF/CAAI-B类期刊)

   Y. Wang, C. Huang, M. Li, Q. Huang, X. Wu, J. Wu

   Pattern Recognition, vol. 151, 110387, 2024.

● BLoG: Bootstrapped graph representation learning with local and global regularization for recommendation  (CAA/CAAI-A类期刊, CCF/CAAI-B类期刊)

   M. Li, L. Zhang, L. Cui, L. Bai, Z. Li, X. Wu

   Pattern Recognition, vol. 144, 109874, 2023.

● Triplet teaching graph contrastive networks with self-evolving adaptive augmentation [code]  (CAA/CAAI-A类期刊, CCF/CAAI-B类期刊)

   J. Miao, F. Cao, M. Li, B. Yang, H. Ye

   Pattern Recognition, vol. 142, 109687, 2023.

🔆 学术活动及相关服务

● 国际权威期刊副主编(Associate Editor)、编委(Editorial Board Member)：

    ▪  Pattern Recognition 副主编

    ▪   Neural Networks 副主编

    ▪   Machine Learning 编委

● 中国计算机协会 (China Computer Federation，CCF) 会员

● 中国人工智能学会 (Chinese Association for Artificial Intelligence， CAAI) 会员

● 中国人工智能学会——机器学习专委会通讯委员

● 中国人工智能学会——知识工程与分布智能专委会委员

● The Institute of Electrical and Electronics Engineers (IEEE) 会员

● Australian Mathematical Society (AusMS), 澳大利亚数学会会员

●  AC for KDD 2026 (Research Track Cycle 2)

●  SPC for WWW 2026, AAAI 2026, IJCAI 2025

●  PC member for NeurIPS (2020-2025)、ICML (2020-2026)、ICLR (2021-2026)、AAAI (2022-2024)、 IJCAI (2023-2026)、CVPR 2024、ACMMM (2024-2026)、 ICME (2022-2026)、KDD (2022/2024/2025; recognized as an Excellent Reviewer in KDD 2025)、WSDM (2023-2026)、AISTATS (2025-2026)、 BMVC 2026

● 期刊审稿人(已审稿篇数600+)

• IEEE Transactions on Pattern Analysis and Machine Intelligence (Impact Factor: 20.8)

• Nature Communications (Impact Factor: 15.7)

• IEEE Transactions on Neural Networks and Learning Systems (Impact Factor: 10.2)

• IEEE Transactions on Cybernetics (Outstanding Reviewer in 2016 and 2017) (Impact Factor: 9.4)

• IEEE Transactions on Knowledge and Data Engineering (Impact Factor: 8.9)

• IEEE Transactions on Image Processing (Impact Factor: 13.7)

• IEEE Transactions on Circuits and Systems for Video Technology (Impact Factor: 8.3)

• IEEE Transactions on Intelligent Transportation Systems (Impact Factor: 7.9)

• IEEE Transactions on Cognitive Communications and Networking (Impact Factor: 7.4)

• IEEE Journal on Selected Areas in Communications (Impact Factor: 13.8)

• IEEE Transactions on Big Data (Impact Factor: 7.5)

• IEEE Journal of Selected Topics in Signal Processing (Impact Factor: 8.7)

• IEEE Transactions on Affective Computing (Impact Factor: 9.6)

• IEEE Wireless Communications (Impact Factor: 10.9)

• IEEE Computational Intelligence Magazine (Impact Factor: 10.3)

• IEEE Transactions on Systems, Man, and Cybernetics: Systems (Impact Factor: 8.6)

• IEEE Transactions on Emerging Topics in Computing (Impact Factor: 5.4)

• IEEE Transactions on Emerging Topics in Computational Intelligence (Impact Factor: 5.3)

• IEEE Transactions on Network Science and Engineering (Impact Factor: 6.7)

• IEEE Transactions on Parallel and Distributed Systems (Impact Factor: 5.6)

• IEEE Transactions on Network and Service Management (Impact Factor: 4.7)

• IEEE Transactions on Neural Systems & Rehabilitation Engineering (Impact Factor: 4.8)

• IEEE Journal of Biomedical and Health Informatics (Impact Factor: 6.7)

• IEEE Transactions on Dependable and Secure Computing (Impact Factor: 7.0)

• IEEE Transactions on Cognitive and Developmental Systems (Impact Factor: 5.0)

• IEEE Transactions Services Computing (Impact Factor: 5.5)

• IEEE Transactions on Software Engineering (Impact Factor: 6.5)

• IEEE Transactions on Mobile Computing (Impact Factor: 7.7)

• IEEE Transactions on Computational Imaging (Impact Factor: 4.2)

• IEEE Transactions on Audio, Speech and Language Processing

• IEEE Network (Impact Factor: 6.8)

• IEEE Intelligent Systems (Impact Factor: 5.6)

• IEEE Transactions on Signal and Information Processing over Networks (Impact Factor: 3.0)

• IEEE Signal Processing Letters (Impact Factor: 3.2)

• IEEE Transactions on Artificial Intelligence

• IEEE Transactions on Automation Science and Engineering

• IEEE Transactions on Consumer Electronics (Impact Factor: 4.3)

• IEEE Transactions on Learning Technologies (Impact Factor: 2.9)

• IEEE/CAA Journal of Automatica Sinica (Impact Factor: 15.3)

• IEEE/ACM Transactions on Networking (Impact Factor: 3.0)

• ACM Transactions on Information Systems (Impact Factor: 5.4)

• ACM Transactions on Multimedia Computing Communications and Applications (Impact Factor: 5.2)

• ACM Transactions on Knowledge Discovery from Data (Impact Factor: 4.4)

• ACM Transactions on Software Engineering and Methodology (Impact Factor: 6.6)

• ACM Transactions on the Web (Impact Factor: 2.6)

• ACM Transactions on Autonomous and Adaptive Systems (Impact Factor: 5.9)

• ACM Transactions on Asian and Low-Resource Language Information Processing (Impact Factor: 1.8)

• Artificial Intelligence Review (Impact Factor: 10.7)

• SCIENCE CHINA Information Sciences (Impact Factor: 7.3)

• Neural Networks (Impact Factor: 6.0)

• Pattern Recognition (Impact Factor: 7.5)

• Neurocomputing (Impact Factor: 5.5)

• Information Sciences (Impact Factor: 8.1)

• Expert Systems with Applications (Impact Factor: 7.5)

• Knowledge-Based Systems (Impact Factor: 7.2)

• Applied Intelligence (Impact Factor: 3.4)

• Applied Soft Computing (Impact Factor: 7.2)

• Soft Computing (Impact Factor: 3.1)

• Network: Computation in Neural Systems (Impact Factor: 1.1)

• International Journal of Machine Learning and Cybernetics (Impact Factor: 3.1)

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