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publications.html
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<title>Publications </title>
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<div class="menu-category">Home</div>
<div class="menu-item"><a href="index.html" >About me</a></div>
<div class="menu-item"><a href="research_interests.html">Research Interests</a></div>
<div class="menu-item"><a href="Professional Activities.html">Professional Activities</a></div>
<div class="menu-category">Research</div>
<div class="menu-item"><a href="publications.html" class="current">Publications</a></div>
<div class="menu-item"><a href="patents.html">Patents</a></div>
<div class="menu-item"><a href="RES Project&Founding.html">Projects&Funding</a></div>
<div class="menu-category">My Lab</div>
<div class="menu-item"><a href="graphme.github.io">GraphME</a></div>
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<h1>Publications </h1>
</div>
<ul>
<p><font size="5" color="#FF4500">Representative Papers on GNNs</font></p>
<hr style="border: 3 double #987cb9" size="4" >
<li><p><b>Are graph convolutional networks with random weights feasible?</b> ★[<a href="https://ieeexplore.ieee.org/abstract/document/9796468"
target="_blank">link</a>]<font color=red><b>ESI Highly Cited Paper</b> </font>🏆<br>
C. Huang, <b>M. Li*</b>, F. Cao, H. Fujita, Z. Li, X. Wu<br>
<b>IEEE Transactions on Pattern Analysis and Machine Intelligence</b>, vol. 45, no. 3, pp. 2751-2768, 2023.
</p></li>
<br />
<li><p><b>Multi-view graph convolutional networks with attention mechanism</b> ★ [<a href="https://www.sciencedirect.com/science/article/pii/S0004370222000480">link</a>][<a href="https://sxu-yaokx.github.io/MAGCN/">code</a>]<br>
K. Yao, J. Liang, J. Liang, <b>M. Li</b>, F. Cao<br>
<b>Artificial Intelligence</b>, vol. 307, 103708, 2022.
</p></li>
<br />
<li><p><b> AEGK: Aligned Entropic Graph Kernels through continuous-time quantum walks</b> [<a href="https://ieeexplore.ieee.org/document/10844511">link</a>] <br>
L. Bai, L. Cui, <b>M. Li</b>, P. Ren, Y. Wang, Y. Philip, L. Zhang, E. R. Hancock<br>
<b>IEEE Transactions on Knowledge and Data Engineering</b>, 2025, DOI: 10.1109/TKDE.2024.3512181.
</p></li><br />
<li><p><b> HAQJSK: Hierarchical-aligned quantum Jensen-Shannon kernels for graph classification</b> ★ [<a href="https://arxiv.org/abs/2211.02904">arxiv</a>] [<a href="https://ieeexplore.ieee.org/document/10502132">link</a>]<br>
L. Bai, L. Cui, Y. Wang, <b>M. Li*</b>, J. Li, P. Yu, E. R. Hancock<br>
<b>IEEE Transactions on Knowledge and Data Engineering</b>, vol. 36, no. 11, pp. 6370-6384, 2024.
</p></li><br />
<li><p><b> Collaborative knowledge graph fusion by exploiting the open corpus</b> ★ [<a href="https://ieeexplore.ieee.org/document/10164253">link</a>]<br>
Y. Wang, Y. Wan, L. Bai, L. Cui, Z. Xu, <b>M. Li</b>, P. Yu, E. Hancock<br>
<b>IEEE Transactions on Knowledge and Data Engineering</b>, vol. 36, no. 2, pp. 475-489, 2024.
</p></li><br />
<li><p><b><font color=blue>Guest Editorial</font>: Deep Neural Networks for Graphs: Theory, Models, Algorithms and Applications</b> ★ [<a href="https://ieeexplore.ieee.org/document/10492652">link</a>]<br>
<b>M. Li</b>, A. Micheli, Y. G. Wang, S. Pan, P. Lio, G. Gnecco, M. Sanguineti<br>
<b>IEEE Transactions on Neural Networks and Learning Systems</b>, vol. 35, no. 4, pp. 4367-4372, 2024.
</p></li><br />
<li><p><b> Permutation equivariant graph framelets for
heterophilous graph learning</b> ★ [<a href="https://ieeexplore.ieee.org/document/10466590">link</a>] [<a href="https://github.com/zrgcityu/PEGFAN">code</a>]<font color=red><b>ESI Highly Cited Paper</b> </font>🏆<br>
J. Li, R. Zheng, H. Feng, <b>M. Li*</b>, X. Zhuang*<br>
<b>IEEE Transactions on Neural Networks and Learning Systems</b>, vol. 35, no. 9, pp. 11634-11648, 2024.
</p></li><br />
<li><p><b> Flow2GNN: Flexible two-way flow message passing for enhancing GNNs beyond homophily</b> ★ [<a href="https://ieeexplore.ieee.org/document/10592053">link</a>] <br>
C. Huang, Y. Wang, Y. Jiang, <b>M. Li</b>, X. Huang, S. Wang, S. Pan, C. Zhou <br>
<b>IEEE Transactions on Cybernetics</b>, vol. 54, no. 11, pp. 6607-6618, 2024.
</p></li><br />
<li><p><b>Multimodal graph learning based on 3D Haar semi-tight framelet for student engagement prediction</b> ★ [<a href="https://www.sciencedirect.com/science/article/abs/pii/S1566253524000022">link</a>]<font color=red><br> <b>ESI Highly Cited Paper</b> </font>🏆<br>
<b>M. Li</b>, X. Zhuang, L. Bai, W. Ding<br>
<b>Information Fusion</b>, vol. 105, 102224, 2024.
</p></li><br />
<li><p><b>EduCross: Dual adversarial bipartite hypergraph learning for cross-modal retrieval in multimodal educational slides</b> ★ [<a href="https://www.sciencedirect.com/science/article/abs/pii/S1566253524002069">link</a>]<br>
<b>M. Li</b>, S. Zhou, Y. Chen, C. Huang, Y. Jiang<br>
<b>Information Fusion</b>, vol. 109, 102428, 2024.
</p></li><br />
<li><p><b> EduGraph: Learning path-based hypergraph neural networks for MOOC course recommendation</b> ★ [<a href="https://ieeexplore.ieee.org/document/10663938">link</a>]<br>
<b>M. Li</b>, Z. Li*, C. Huang*, Y. Jiang, X. Wu<br>
<b>IEEE Transactions on Big Data</b>, vol. 10, no. 6, pp. 706-719, 2024.
</p></li><br />
<li><p><b> A simple yet effective framelet-based graph neural network for directed graphs</b>★ [<a href="https://www.sciencedirect.com/science/article/abs/pii/S1566253524000022">link</a>] [<a href="https://github.com/andyjm3/SVD-GCN">code</a>]<br>
C. Zou, A. Han, L. Lin, <b>M. Li</b>, J. Gao<br>
<b>IEEE Transactions on Artificial Intelligence</b>, vol. 5, no. 4, pp. 1647-1657, 2024.
</p></li><br />
<li><p><b> GoLoG: Global-to-local decoupling graph network with joint optimization for hyperspectral image classification</b> ★ [<a href="https://ieeexplore.ieee.org/document/10288272">link</a>]<br>
B. Yang, H. Ye, <b>M. Li</b>*, F. Cao, S. Pan<br>
<b>IEEE Transactions on Geoscience and Remote Sensing</b>, vol. 61, 5528014, 2023.
</p></li><br />
<li><p><b>HC-GAE: The hierarchical cluster-based graph auto-encoder for graph representation learning</b> ★ [<a href="https://arxiv.org/abs/2405.14742">link</a>] [<a href="https://github.com/JonathanGXu/HC-GAE">code</a>]<br>
Z. Xu, L. Bai, L. Cui, <b>M. Li</b>, Y. Wang, E. R. Hancock<br>
<b>NeurIPS</b>, 2024.
</p></li><br />
<li><p><b>Long-range brain graph transformer</b> ★ [<a href="xxx">link</a>] [<a href="https://github.com/yushuowiki/ALTER">code</a>]<br>
S. Yu, S. Jin, <b>M. Li</b>, T. Sarwar, F. Xia<br>
<b>NeurIPS</b>, 2024.
</p></li><br />
<li><p><b> Path integral based convolution and pooling for graph neural networks</b> ★ [<a href="https://arxiv.org/abs/2006.16811">link</a>][<a href="https://github.com/YuGuangWang/PAN">code</a>][<a href="https://pytorch-geometric.readthedocs.io/en/latest/_modules/torch_geometric/nn/conv/pan_conv.html">PyG Implementation</a>]<br>
Z. Ma, J. Xuan, Y. G. Wang, <b>M. Li</b>, P. Lio<br>
<b>NeurIPS</b>, 2020, pp. 16421-16433.
</p></li>
<br />
<li><p><b> How universal polynomial bases enhance spectral graph neural networks: Heterophily, over-smoothing, and over-squashing</b> ★ [<a href="https://proceedings.mlr.press/v235/huang24z.html">link</a>] [<a href="https://github.com/kkhuang81/UniFilter">code</a>] [<a href="https://icml.cc/media/icml-2024/Slides/33747.pdf">poster</a>] [<a href="https://www.youtube.com/watch?v=MGKGR-ULM88">video</a>]<br>
K. Huang*, Y. G. Wang, <b>M. Li</b>*, P. Lio<br>
<b>ICML</b>, 2024, pp. 20310-20330.
</p></li><br />
<li><p><b> QBMK: Quantum-based matching kernels for un-attributed graphs</b> ★ [<a href="https://proceedings.mlr.press/v235/bai24a.html">link</a>]<br>
L. Bai, L. Cui, <b>M. Li</b>, Y. Wang, E. Hancock<br>
<b>ICML</b>, 2024, pp. 2364-2374. (Spotlight Paper: 3.5% acceptance rate)
</p></li><br />
<li><p><b> How powerful are shallow neural networks with bandlimited random weights?</b> ★ [<a href="https://proceedings.mlr.press/v202/li23aa/li23aa.pdf">link</a>]<br>
<b>M. Li</b>, S. Sonoda, F. Cao, Y. G. Wang, J. Liang<br>
<b>ICML</b>, 2023, pp. 19960-19981.
</p></li><br />
<li><p><b> Haar graph pooling</b> ★ [<a href="http://proceedings.mlr.press/v119/wang20m/wang20m.pdf">link</a>][<a href="https://github.com/YuGuangWang/HaarPool">code</a>]<br>
Y. G. Wang, <b>M. Li</b>*, Z. Ma, G. Montufar, X. Zhuang, Y. Fan<br>
<b>ICML</b>, 2020, pp. 9952-9962.
</p></li>
<br />
<li><p><b> How framelets enhance graph neural networks</b> ★[<a href="http://proceedings.mlr.press/v139/zheng21c/zheng21c-supp.pdf">link</a>] [<a href="https://github.com/YuGuangWang/UFG">code</a>]<br>
X. Zheng, B. Zhou, J. Gao, Y. G. Wang, P. Lio, <b>M. Li</b>, G. Montufar<br>
<b>ICML</b>, 2021, pp. 12761-12771. (Spotlight Paper)
</p></li><br />
<li><p><b>When hypergraph meets heterophily: New benchmark datasets and baseline</b> ★ [<a href="https://kellysylvia77.github.io/HHL">repository link</a>] [<a href="https://mingli-ai.github.io/HHL.pdf">appendix</a>]<br>
<b>M. Li</b>, Y. Gu, Y. Wang, Y. Fang, Lu Bai, X. Zhuang, P. Lio<br>
<b>AAAI</b>, 2025.
</p></li>
<br />
<li><p><b>Deep hypergraph neural networks with tight framelets</b> ★ [<a href="">link</a>] [<a href="https://mingli-ai.github.io/FrameHGNN.pdf">appendix</a>]<br>
<b>M. Li</b>, Y. Fang, Y. Wang, H. Feng, Y. Gu, L. Bai, P. Lio<br>
<b>AAAI</b>, 2025.
</p></li>
<br />
<li><p><b>DHAKR: Learning deep hierarchical attention-based kernelized representations for graph classification</b> ★<br>
F. Qian, L. Bai, L. Cui, <b>M. Li</b>*, Z. Lyu, H. Du, E. Hancock<br>
<b>AAAI</b>, 2025.
</p></li>
<br />
<li><p><b>ML-GOOD: Towards multi-label graph out-of-distribution detection</b> ★ [<a href="">link</a>] [<a href="https://github.com/ca1man-2022/ML-GOOD">code</a>] [<a href="https://github.com/ca1man-2022/ML-GOOD/blob/main/Appendix_ML_GOOD.pdf">appendix</a>]<br>
T. Cai, Y. Jiang, <b>M. Li</b>, C. Huang, Y. Wang, Q. Huang<br>
<b>AAAI</b>, 2025.
</p></li>
<br />
<li><p><b>Stability and generalization of ℓp-regularized stochastic learning for GCN</b> ★ [<a href="https://www.ijcai.org/proceedings/2023/631">link</a>]<br>
S. Liu, L. Wei, S. Lv, <b>M. Li</b><br>
<b>IJCAI</b>, 2023, pp. 5685-5693.
</p></li><br />
<li><p><b>FrameERC: Framelet transform based multimodal graph neural networks for emotion recognition in conversation</b>★ [<a href="https://www.sciencedirect.com/science/article/abs/pii/S0031320324010914">link</a>]<br>
<b>M. Li</b>, J. Shi, L. Bai, C. Huang, Y. Jiang, K. Lu, S. Wang, E. Hancock<br>
<b>Pattern Recognition</b>, vol. 161, 111340, 2025.
</p></li><br />
<li><p><b> BLoG: Bootstrapped graph representation learning with local and global regularization for recommendation</b> ★[<a href="https://www.sciencedirect.com/science/article/abs/pii/S0031320323005721">link</a>]<br>
<b>M. Li</b>, L. Zhang, L. Cui, L. Bai, Z. Li, X. Wu<br>
<b>Pattern Recognition</b>, vol. 144, 109874, 2023.
</p></li><br />
<li><p><b> QBER: Quantum-based entropic representations for un-attributed graphs</b> ★ [<a href="https://www.sciencedirect.com/science/article/pii/S0031320323005757">link</a>] <br>
L. Cui, <b>M. Li</b>, L. Bai, Y. Wang, J. Li, Y. Wang, Z. Li, Y. Chen, E. Hancock<br>
<b>Pattern Recognition</b>, vol. 145, 109877, 2024.
</p></li><br />
<li><p><b>Collaborative graph neural networks for augmented graphs: A local-to-global perspective</b>★ [<a href="https://www.sciencedirect.com/science/article/abs/pii/S0031320324007714">link</a>]<br>
Q. Guo, X. Yang, <b>M. Li</b>, Y. Qian<br>
<b>Pattern Recognition</b>, vol. 158, 111020, 2025.
</p></li><br />
<li><p><b> AG-Meta: Adaptive graph meta learning via representation consistency over local subgraphs</b> ★ [<a href="https://www.sciencedirect.com/science/article/abs/pii/S0031320324001389">link</a>] <br>
Y. Wang, C. Huang, <b>M. Li</b>, Q. Huang, X. Wu, J. Wu<br>
<b>Pattern Recognition</b>, vol. 151, 110387, 2024.
</p></li><br />
<li><p><b> Fast Haar transforms for graph neural networks</b> ★ [<a href="https://www.sciencedirect.com/science/article/pii/S0893608020301568?via%3Dihub">link</a>]<br>
<b>M. Li</b>, Z. Ma, Y. G. Wang, X. Zhuang<br>
<b>Neural Networks</b>, vol. 128, pp. 188-198, 2020.
</p></li><br />
<hr style="border: 3 double #987cb9" size="4">
<br />
<p>The full paper list can be retrieved from my <a href="https://scholar.google.com/citations?hl=zh-CN&user=Z7yEoOQAAAAJ&authuser=1" target="_blank" style="target-new: tab;">Google Scholar Profile</a>
and <a href="https://www.researchgate.net/profile/Ming_Li272" target="_blank" style="target-new: tab;">ResearchGate Archieves</a></p>
<font size="3">★: <span style="font-weight: bold">Featured Papers on <span style="font-weight: bold">Graph Neural Networks and Graph Representation Learning</span></span></font><br/>
<font size="3">* : <span style="font-weight: bold">corresponding author</span></font><br/>
<br />
<br />
<font size="5" color="#FF4500">2025-Published Papers</font>
<hr>
<li><p><b> AEGK: Aligned Entropic Graph Kernels through continuous-time quantum walks</b> [<a href="https://ieeexplore.ieee.org/document/10844511">link</a>] <br>
L. Bai, L. Cui, <b>M. Li</b>, P. Ren, Y. Wang, Y. Philip, L. Zhang, E. R. Hancock<br>
<b>IEEE Transactions on Knowledge and Data Engineering</b>, 2025, DOI: 10.1109/TKDE.2024.3512181.
</p></li><br />
<li><p><b>When hypergraph meets heterophily: New benchmark datasets and baseline</b> ★<br>
<b>M. Li</b>, Y. Gu, Y. Wang, Y. Fang, Lu Bai, X. Zhuang, P. Lio<br>
<b>AAAI</b>, 2025.
</p></li>
<br />
<li><p><b>Deep hypergraph neural networks with tight framelets</b> ★ <br>
<b>M. Li</b>, Y. Fang, Y. Wang, H. Feng, Y. Gu, L. Bai, P. Lio<br>
<b>AAAI</b>, 2025.
</p></li>
<br />
<li><p><b>DHAKR: Learning deep hierarchical attention-based kernelized representations for graph classification</b> ★<br>
F. Qian, L. Bai, L. Cui, <b>M. Li</b>*, Z. Lyu, H. Du, E. Hancock<br>
<b>AAAI</b>, 2025.
</p></li>
<br />
<li><p><b>ML-GOOD: Towards multi-label graph out-of-distribution detection</b> ★ [<a href="xxx">link</a>] [<a href="https://github.com/ca1man-2022/ML-GOOD">code</a>]<br>
T. Cai, Y. Jiang, <b>M. Li</b>, C. Huang, Y. Wang, Q. Huang<br>
<b>AAAI</b>, 2025.
</p></li>
<br />
<li><p><b>FrameERC: Framelet transform based multimodal graph neural networks for emotion recognition in conversation</b>★ [<a href="https://www.sciencedirect.com/science/article/abs/pii/S0031320324010914">link</a>]<br>
<b>M. Li</b>, J. Shi, L. Bai, C. Huang, Y. Jiang, K. Lv, S. Wang, E. Hancock<br>
<b>Pattern Recognition</b>, vol. 161, 111340, 2025.
</p></li><br />
<li><p><b>Collaborative graph neural networks for augmented graphs: A local-to-global perspective</b>★ [<a href="https://www.sciencedirect.com/science/article/abs/pii/S0031320324007714">link</a>]<br>
Q. Guo, X. Yang, <b>M. Li</b>, Y. Qian<br>
<b>Pattern Recognition</b>, vol. 158, 111020, 2025.
</p></li><br />
<li><p><b> ReFNet: Rehearsal-based graph lifelong learning with multi-resolution framelet graph neural networks</b> [<a href="https://www.sciencedirect.com/science/article/abs/pii/S0020025524017705">link</a>] <br>
<b>M. Li</b>, X. Yang, Y. Chen, Z. Zhou, Y. Gu, Q. Hu<br>
<b>Information Sciences</b>, vol. 700, 121856, 2025.
</p></li><br />
<li><p><b> Multimodal graph learning with framelet-based stochastic configuration networks for emotion recognition in conversation</b> [<a href="https://www.sciencedirect.com/science/article/abs/pii/S0020025524013070">link</a>] <br>
J. Shi, <b>M. Li</b>*, Y. Chen, L. Cui, L. Bai<br>
<b>Information Sciences</b>, vol. 686, 121393, 2025.
</p></li><br />
<font size="5" color="#FF4500">2024-Submitted Papers</font>
<hr>
<li><p><b>Deeper insights into deep graph convolutional networks: Stability and generalization</b> [<a href="https://arxiv.org/abs/2410.08473">link</a>]<br>
G. Yang, <b>M. Li</b>, H. Feng, X. Zhuang<br>
submitted to <b>IEEE Transactions on Pattern Analysis and Machine Intelligence</b>, 2024.
</p></li><br />
<li><p><b>Bridging smoothness and approximation: Theoretical
insights into over-smoothing in graph neural networks</b> [<a href="https://arxiv.org/abs/2407.01281">link</a>]<br>
G. Yang, J. Li, <b>M. Li</b>, H. Feng, D. X. Zhou<br>
submitted to <b>Journal of Machine Learning Research</b>, 2024.
</p></li><br />
<li><p><b>ENADPool: The Edge-Node Attention-based Differentiable Pooling for graph neural networks</b> ★ [<a href="https://arxiv.org/abs/2405.10218">link</a>]<br>
Z. Zhao, L. Bai, L. Cui, <b>M. Li</b>, Y. Wang, L. Xu, E. Hancock<br>
submitted to <b>WWW</b>, 2024.
</p></li><br />
<li><p><b>AKBR: Learning Adaptive Kernel-based Representations for Graph Classification</b> ★ [<a href="https://arxiv.org/abs/2403.16130">link</a>]<br>
F. Qian, L. Cui, <b>M. Li</b>, Y. Wang, H. Du, L. Xu, L. Bai, P. Yu, E. Hancock<br>
submitted to <b>WWW</b>, 2024.
</p></li><br />
<li><p><b>SSHPool: The Separated Subgraph-based Hierarchical Pooling</b> ★ [<a href="https://arxiv.org/pdf/2403.16133">link</a>]<br>
Z. Xu, L. Cui, <b>M. Li</b>, Y. Wang, Z. Lyu, H. Du, L. Bai, P. Yu, E. Hancock<br>
submitted to <b>WWW</b>, 2024.
</p></li><br />
<li><p><b>Consistency-aware hypergraph fusion network for multimodal emotion recognition in conversations</b> <br>
J. Shi, <b>M. Li</b>*, L. Bai, W. Ding<br>
submitted to <b>Knowledge-Based Systems</b>, 2024.
</p></li><br />
<li><p><b>GloRA: Global item relational awareness based graph representation learning for sequential recommendation</b><br>
<b>M. Li</b>, Z. Zhu, L. Cui, L. Bai, Q. Hu, S. Zhou<br>
submitted to <b>Pattern Recognition</b>, 2024.
</p></li><br />
<li><p><b>Layer-wise feature metric of semantic-pixel matching for few-shot learning</b><br>
H. Tang, J. Lu, G. Huang, <b>M. Li</b>, X. Chen, G. Zhong, C. Pun, Z. Tan, <br>
submitted to <b>Pattern Recognition</b>, 2024.
</p></li><br />
<li><p><b>BMPM-Net: Few-shot medical image segmentation via bias-corrected multiple prototypes mining</b><br>
R. Zhou, G. Huang, <b>M. Li</b>, X. Zhang, Y. Li, X. Yuan, L. Cheng, C. Pun<br>
submitted to <b>Neural Networks</b>, 2024.
</p></li><br />
<li><p><b>ConsistencyDet: Robust object detector with denoising paradigm of consistency models</b> [<a href="https://arxiv.org/abs/2404.07773">link</a>] [<a href="https://github.com/Tankowa/ConsistencyDet">code</a>] <br>
L. Jiang, Z. Wang, C. Wang, X. Guang, <b>M. Li</b>, J. Leng, X. Wu<br>
submitted to <b>CVPR</b>, 2025.
</p></li><br />
<li><p><b> OML-M3IL: Overcoming modality laziness in multi-modal multi-instance learning for immune repertoire classification</b> <br>
Y. Zhang, Z. Zhou, H. Luo, W. Liu, <b>M. Li</b><br>
submitted to <b>CVPR</b>, 2025.
</p></li><br />
<li><p><b> Correlation information enhanced graph anomaly detection via hypergraph transformation</b> <br>
C. Huang, C. Gao, <b>M. Li</b>, X. Wang, Y. Jiang, X. Huang<br>
submitted to <b>IEEE Transactions on Cybernetics</b>, 2024.
</p></li><br />
<li><p><b> Explaining vulnerabilities in deep multi-instance learning: Insights from key instance attacks
and out-of-distribution detection</b> <br>
Y. Zhang, Z. Zhou, <b>M. Li</b>, X. Wu, P. Lio<br>
submitted to <b>IEEE Transactions on Neural Networks and Learning Systems</b>, 2024.
</p></li><br />
<li><p><b> On learning label noise robust networks via regularization: A topological view</b> <br>
C. Zhou, H. Meng, <b>M. Li</b>, Z. Zhou<br>
revision submitted to <b>IEEE Transactions on Neural Networks and Learning Systems</b>, 2024.
</p></li><br />
<li><p><b> Fuzzy clustering with microcluster recombination based on local convex structures</b> <br>
W. Gao, Z. Zhou, <b>M. Li</b>, H. Meng,<br>
submitted to <b>IEEE Transactions on Fuzzy Systems</b>, 2024.
</p></li><br />
<li><p><b> Examining the Fourier spectrum of speech signal from a time-frequency perspective for automatic depression level prediction</b> <br>
M. Niu, J. Tao, Y. He, S. Zhang, <b>M. Li</b><br>
submitted to <b>IEEE Transactions on Affective Computing</b>, 2024.
</p></li><br />
<li><p><b> Opinion dynamics informed neural networks for few-labeled graphs</b> <br>
W. Ye, J. Yang, X. Wei, R. Fan, <b>M. Li</b>, A. Singh<br>
submitted to <b>IEEE Transactions on Network Science and Engineering</b>, 2024.
</p></li><br />
<li><p><b> A unified framework for exploratory learning-aided community detection under topological uncertainty</b> ★ [<a href="https://arxiv.org/abs/2304.04497">link</a>] <br>
Y. Hou, C. Tran, <b>M. Li</b>, W. Y. Shin<br>
revision submitted to <b>IEEE Transactions on Network Science and Engineering</b>, 2024.
</p></li><br />
<li><p><b> AutoSGRL: Automated framework construction for self-supervised graph representation learning</b> <br>
Y. Xie, Y. Chang, <b>M. Li</b>*, A. K. Qin, X. Zhang<br>
submitted to <b>Neural Networks</b>, 2025.
</p></li><br />
<li><p><b> Inconsistency-aware graph convolutional networks for multi-view classification</b> <br>
Q. Teng, K. Liu, X. Yang, <b>M. Li</b><br>
submitted to <b>IEEE Transactions on Emerging Topics in Computational Intelligence</b>, 2024.
</p></li><br />
<li><p><b> Affinity maximization learning for unsupervised deep graph matching</b> <br>
Y. Xie, Z. Li, W. Wang, A. K. Qin, <b>M. Li</b>, M. Gong<br>
submitted to <b>IEEE Transactions on Emerging Topics in Computational Intelligence</b>, 2024.
</p></li><br />
<li><p><b> Propensity scoring on multi-instance partial-label learning</b> <br>
H. Luo, Y. Zhang, Z. Zhou, W. Liu, <b>M. Li</b><br>
submitted to <b>IEEE Transactions on Emerging Topics in Computational Intelligence</b>, 2024.
</p></li><br />
<li><p><b> QIIS: Quaternion inter- and intra-Task interaction strategy for multi-task dense prediction</b> <br>
K. Huang, G. Huang, Y. Li, <b>M. Li</b>, X. Yuan, C. Pun, L. Cheng<br>
submitted to <b>IEEE Transactions on Emerging Topics in Computational Intelligence</b>, 2024.
</p></li><br />
<li><p><b> Uni-Attack: A unified framework for black-box adversarial attacks against large vision-language models in autonomous driving</b> <br>
H. Chen, R. Zhang, Y. Zhang, C. Wang, A. Elazab, <b>M. Li</b><br>
submitted to <b>IEEE Transactions on Intelligent Transportation Systems</b>, 2024.
</p></li><br />
<li><p><b> SFCANet: Channel attention in spatial-frequency domain for infrared small target detection</b><br>
Z. Lin, G. Huang, <b>M. Li</b>, X. Yuan, G. Yue, C. Pun, L. Cheng<br>
submitted to <b>IEEE Transactions on Aerospace and Electronic Systems</b>, 2024.
</p></li><br />
<li><p><b> Multi-topology contrastive graph representation learning</b> <br>
Y. Xie, J. Jia, C. Wen, D. Li, <b>M. Li</b>*<br>
submitted to <b>SCIENCE CHINA Information Sciences</b>, 2024.
</p></li><br />
<li><p><b> eBASE: Real-time battery swap recommendation system for eBike users</b> <br>
S. Zhou, Y. Gu, Z. Li, Y. Li, C. Zhu, X. Liu, J. Huang, <b>M. Li</b>*, X. Zhang, M. Li<br>
submitted to <b>The 30th International Conference on Database Systems for Advanced Applications (DASFAA)-Demo Track</b>, 2024.
</p></li><br />
<li><p><b> A feature reuse framework with texture-adaptive aggregation for reference-based super-resolution</b> [<a href="https://arxiv.org/abs/2306.01500">link</a>] <br>
X. Mei, Y. Yang, <b>M. Li</b>*, C. Huang, K. Zhang, F. Zhang<br>
submitted to <b>Knowledge-Based Systems</b>, 2023.
</p></li><br />
<li><p><b> Fast tensor needlet transforms for tangent vector fields on the sphere</b> ✠[<a href="https://arxiv.org/pdf/1907.13339.pdf">link</a>]<br>
<b>M. Li</b>, J. Chen, P. Broadbridge, A. Olenko, Y. G. Wang<br>
submitted to <b>Applied and Computational Harmonic Analysis</b>, 2023.
</p></li><br />
<font size="5" color="#FF4500">Before 2025-Published Papers</font>
<hr>
<li><p><b><font color=blue>Guest Editorial</font>: Deep Neural Networks for Graphs: Theory, Models, Algorithms and Applications</b> ★ [<a href="https://ieeexplore.ieee.org/document/10492652">link</a>]<br>
<b>M. Li</b>, A. Micheli, Y. G. Wang, S. Pan, P. Lio, G. Gnecco, M. Sanguineti<br>
<b>IEEE Transactions on Neural Networks and Learning Systems</b>, vol. 35, no. 4, pp. 4367-4372, 2024.
</p></li><br />
<li><p><b>EduCross: Dual adversarial bipartite hypergraph learning for cross-modal retrieval in multimodal educational slides</b> ★ [<a href="https://www.sciencedirect.com/science/article/abs/pii/S1566253524002069">link</a>] <br>
<b>M. Li</b>, S. Zhou, Y. Chen, C. Huang, Y. Jiang<br>
<b>Information Fusion</b>, vol. 109, 102428, 2024.
</p></li><br />
<li><p><b>Multimodal graph learning based on 3D Haar semi-tight framelet for student engagement prediction</b> ★ [<a href="https://www.sciencedirect.com/science/article/abs/pii/S1566253524000022">link</a>]<font color=red><b>ESI Highly Cited Paper</b> </font>🏆<br>
<b>M. Li</b>, X. Zhuang, L. Bai, W. Ding<br>
<b>Information Fusion</b>, vol. 105, 102224, 2024.
</p></li><br />
<li><p><b> HAQJSK: Hierarchical-aligned quantum Jensen-Shannon kernels for graph classification</b> ★ [<a href="https://arxiv.org/abs/2211.02904">arxiv</a>] [<a href="https://ieeexplore.ieee.org/document/10502132">link</a>]<br>
L. Bai, L. Cui, Y. Wang, <b>M. Li*</b>, J. Li, P. Yu, E. R. Hancock<br>
<b>IEEE Transactions on Knowledge and Data Engineering</b>, vol. 36, no. 11, pp. 6370-6384, 2024.
</p></li><br />
<li><p><b> Collaborative knowledge graph fusion by exploiting the open corpus</b> ★ [<a href="https://ieeexplore.ieee.org/document/10164253">link</a>]<br>
Y. Wang, Y. Wan, L. Bai, L. Cui, Z. Xu, <b>M. Li</b>, P. Yu, E. Hancock<br>
<b>IEEE Transactions on Knowledge and Data Engineering</b>, vol. 36, no. 2, pp. 475-489, 2024.
</p></li><br />
<li><p><b> XKT: Towards explainable knowledge tracing with multiple knowledge concept annotations</b> [<a href="https://ieeexplore.ieee.org/document/10569045">link</a>] <br>
C. Huang, Q. Huang, X. Huang, H. Wang, <b>M. Li</b>, K. Lin, Y. Chang <br>
<b>IEEE Transactions on Knowledge and Data Engineering</b>, vol. 36, no. 11, pp. 7308-7325, 2024.
</p></li><br />
<li><p><b> Flow2GNN: Flexible two-way flow message passing for enhancing GNNs beyond homophily</b> ★ [<a href="https://ieeexplore.ieee.org/document/10592053">link</a>]<br>
C. Huang, Y. Wang, Y. Jiang, <b>M. Li</b>, X. Huang, S. Wang, S. Pan, C. Zhou <br>
<b>IEEE Transactions on Cybernetics</b>, vol. 54, no. 11, pp. 6607-6618, 2024.
</p></li><br />
<li><p><b>HC-GAE: The hierarchical cluster-based graph auto-encoder for graph representation learning</b> ★ [<a href="https://arxiv.org/abs/2405.14742">link</a>] [<a href="https://github.com/JonathanGXu/HC-GAE">code</a>]<br>
Z. Xu, L. Bai, L. Cui, <b>M. Li</b>, Y. Wang, E. R. Hancock<br>
<b>NeurIPS</b>, 2024.
</p></li><br />
<li><p><b>Long-range brain graph transformer</b> ★ [<a href="https://github.com/yushuowiki/ALTER">code</a>]<br>
S. Yu, S. Jin, <b>M. Li</b>, T. Sarwar, F. Xia<br>
<b>NeurIPS</b>, 2024.
</p></li><br />
<li><p><b> How universal polynomial bases enhance spectral graph neural networks: Heterophily, over-smoothing, and over-squashing</b> ★ [<a href="https://proceedings.mlr.press/v235/huang24z.html">link</a>] [<a href="https://github.com/kkhuang81/UniFilter">code</a>] [<a href="https://icml.cc/media/icml-2024/Slides/33747.pdf">poster</a>] [<a href="https://www.youtube.com/watch?v=MGKGR-ULM88">video</a>]<br>
K. Huang*, Y. G. Wang, <b>M. Li</b>*, P. Lio<br>
<b>ICML</b>, 2024, pp. 20310-20330.
</p></li><br />
<li><p><b> QBMK: Quantum-based matching kernels for un-attributed graphs</b> ★ [<a href="">link</a>]<br>
L. Bai, L. Cui, <b>M. Li</b>, Y. Wang, E. Hancock<br>
<b>ICML</b>, 2024, pp. 2364-2374. (Spotlight Paper: 3.5% acceptance rate)
</p></li><br />
<li><p><b> Permutation equivariant graph framelets for
heterophilous graph learning</b> ★ [<a href="https://ieeexplore.ieee.org/document/10466590">link</a>] [<a href="https://github.com/zrgcityu/PEGFAN">code</a>]<font color=red><b>ESI Highly Cited Paper</b> </font>🏆<br>
J. Li, R. Zheng, H. Feng, <b>M. Li*</b>, X. Zhuang*<br>
<b>IEEE Transactions on Neural Networks and Learning Systems</b>, vol. 35, no. 9, pp. 11634-11648, 2024.
</p></li><br />
<li><p><b>Towards graph self-supervised learning with contrastive adjusted zooming</b> ★ [<a href="https://ieeexplore.ieee.org/document/9945993">link</a>]<br>
Y. Zheng, M. Jin, S. Pan, Y. F. Li, H. Peng, <b>M. Li</b>, Z. Li<br>
<b>IEEE Transactions on Neural Networks and Learning Systems</b>, vol. 35, no. 7, pp. 8882-8896, 2024.
</p></li>
<br />
<li><p><b> EduGraph: Learning path-based hypergraph neural networks for MOOC course recommendation</b> ★ [<a href="https://ieeexplore.ieee.org/document/10663938">link</a>]<br>
<b>M. Li</b>, Z. Li*, C. Huang*, Y. Jiang, X. Wu<br>
<b>IEEE Transactions on Big Data</b>, vol. 10, no. 6, pp. 706-719, 2024.
</p></li><br />
<li><p><b> A simple yet effective framelet-based graph neural network for directed graphs</b>★ [<a href="https://www.sciencedirect.com/science/article/abs/pii/S1566253524000022">link</a>] [<a href="https://github.com/andyjm3/SVD-GCN">code</a>]<br>
C. Zou, A. Han, L. Lin, <b>M. Li</b>, J. Gao<br>
<b>IEEE Transactions on Artificial Intelligence</b>, vol. 5, no. 4, pp. 1647-1657, 2024.
</p></li><br />
<li><p><b> MM-tracker: Visual tracking with a multi-task model integrating detection and differentiating feature extraction</b> [<a href="https://ieeexplore.ieee.org/document/10637320">link</a>] <br>
Z. Wang, M. Li, Z. Li, X. Zhang, <b>M. Li*</b>, Z. Li, W. Ding, X. Wu<br>
<b>IEEE Transactions on Emerging Topics in Computational Intelligence</b>, 2024, DOI: 10.1109/TETCI.2024.3436842.
</p></li><br />
<li><p><b> SODSR: A three-stage small object detection via super-resolution using optimizing combination</b> [<a href="https://ieeexplore.ieee.org/document/10684780">link</a>] <br>
X. Mei, K. Zhang, C. Huang, X. Chen, <b>M. Li</b>, Z. Li, W. Ding, X. Wu<br>
<b>IEEE Transactions on Emerging Topics in Computational Intelligence</b>, 2024, DOI: 10.1109/TETCI.2024.3452749.
</p></li><br />
<li><p><b> Framelet based dual hypergraph neural networks for student engagement prediction</b> ★ [<a href="Poster_AI4ED-AAAI_Paper.pdf">poster</a>] [<a href="AI4Ed-AAAI-Award.pdf">Best Short Paper Award</a>] <br>
<b>M. Li</b>*, J. Shi<br>
<b>AI4ED-AAAI</b>, 2024. Best Short Paper Award 🏆
</p></li><br />
<li><p><b> QBER: Quantum-based entropic representations for un-attributed graphs</b> ★ [<a href="https://www.sciencedirect.com/science/article/pii/S0031320323005757">link</a>] <br>
L. Cui, <b>M. Li</b>, L. Bai, Y. Wang, J. Li, Y. Wang, Z. Li, Y. Chen, E. Hancock<br>
<b>Pattern Recognition</b>, vol. 145, 109877, 2024.
</p></li><br />
<li><p><b> AG-Meta: Adaptive graph meta learning via representation consistency over local subgraphs</b> ★ [<a href="https://www.sciencedirect.com/science/article/abs/pii/S0031320324001389">link</a>] <br>
Y. Wang, C. Huang, <b>M. Li</b>, Q. Huang, X. Wu, J. Wu<br>
<b>Pattern Reocognition</b>, vol. 151, 110387, 2024.
</p></li><br />
<li><p><b> PointTransform networks for automatic depression level prediction via facial keypoints</b> [<a href="https://www.sciencedirect.com/science/article/abs/pii/S0950705124005859">link</a>]<br>
M. Niu, <b>M. Li</b>*, C. Fu<br>
<b>Knowledge-Based Systems</b>, vol. 297, 111951, 2024.
</p></li><br />
<li><p><b> Incorporation of peer-feedback into the pedagogical use of spherical video-based virtual reality in writing education</b> [<a href="https://bera-journals.onlinelibrary.wiley.com/doi/10.1111/bjet.13376">link</a>]<font color=red><b>ESI Highly Cited Paper</b> </font>🏆<br>
Y. Chen, <b>M. Li</b>*, M. Cukurova, M. Jong<br>
<b>British Journal of Educational Technology</b>, vol. 55, no.2, pp. 519-540, 2024.
</p></li><br />
<li><p><b> Unleashing imagination: An effective pedagogical approach to integrate into spherical video-based virtual reality to improve students' creative writing</b> [<a href="https://link.springer.com/article/10.1007/s10639-023-12115-7">link</a>]<br>
Y. Chen, <b>M. Li</b>*, M. Cukurova*<br>
<b>Education and Information Technologies</b>, vol. 29, pp. 6499-6523, 2024.
</p></li><br />
<li><p><b> Understanding the dynamics of motivation and learning behaviors in augmented reality-based writing courses</b> [<a href="https://link.springer.com/article/10.1007/s10639-024-13093-0">link</a>]<br>
Y. Chen, X. Wang, <b>M. Li</b>*, M. Cukurova, M. Jong<br>
<b>Education and Information Technologies</b>, 2024, DOI: 10.1007/s10639-024-13093-0.
</p></li><br />
<li><p><b> A systematic review of research on immersive technology-enhanced writing education: The current state and a research agenda</b> [<a href="https://ieeexplore.ieee.org/document/10354407">link</a>] <br>
Y. Chen, <b>M. Li</b>*, C. Huang, M. Cukurova, Q. Ma<br>
<b>IEEE Transactions on Learning Technologies</b>, vol. 17, pp. 919-938, 2024.
</p></li><br />
<li><p><b> Framelet-based dual hypergraph neural networks for student performance prediction </b> [<a href="https://link.springer.com/article/10.1007/s13042-024-02124-4">link</a>]<br>
Y. Yang, J. Shi, <b>M. Li</b>*, H. Fujita<br>
<b>International Journal of Machine Learning and Cybernetics</b>, vol. 15, pp. 3863–3877, 2024.
</p></li><br />
<li><p><b> Robust graph neural networks with Dirichlet regularization and residual connection </b> [<a href="https://link.springer.com/article/10.1007/s13042-024-02117-3">link</a>][<a href="https://github.com/sxu-yaokx/DRGNN">code</a>]<br>
K. Yao, Z. Du, <b>M. Li</b>, F. Cao, J. Liang<br>
<b>International Journal of Machine Learning and Cybernetics</b>, vol. 15, pp. 3733–3743, 2024.
</p></li><br />
<li><p><b> A joint parcellation and boundary network with multi-rate-shared dilated graph attention for cortical surface parcellation</b> [<a href="https://link.springer.com/article/10.1007/s11517-023-02942-8">link</a>]<br>
S. Liu, H. Ye, B. Yang, <b>M. Li</b>, F. Cao<br>
<b>Medical & Biological Engineering & Computing</b>, vol. 62, pp. 537-549, 2024.
</p></li><br />
<li><p><b>Semantic-spatial feature-fused cortical surface parcellation: a scale-unified spatial learning network with boundary contrastive loss</b> [<a href="https://link.springer.com/article/10.1007/s11517-024-03242-5">link</a>]<br>
H. Ye, S. Liu, <b>M. Li</b>, H. Zhu, F. Cao<br>
<b>Medical & Biological Engineering & Computing</b>, 2024, DOI: 10.1007/s11517-024-03242-5
</p></li><br />
<li><p><b> Real-time E-bike route planning with battery range prediction</b> [<a href="https://dl.acm.org/doi/abs/10.1145/3616855.3635696">Link</a>] [<a href="https://files.atypon.com/acm/65b5ba0c111a9e558da41ebac9d29df7">Demo Video</a>]<br>
Z. Li, G. Ren, Y. Gu, S. Zhou, X. Liu, J. Huang, <b>M. Li</b>*<br>
<b>ACM International Conference on Web Search and Data Mining (WSDM)</b>, 2024, pp. 1070–1073.
</p></li><br />
<li><p><b> GM2RC: Graph-based multitask modality refinement and complement for multimodal sentiment analysis</b> [<a href="https://ieeexplore.ieee.org/abstract/document/10552555">Link</a>] <br>
J. Shi, Y. Chen, S. Zhou, <b>M. Li</b>*<br>
<b> The 7th International Symposium on Autonomous Systems (ISAS)</b>, 2024.
</p></li><br />
<li><p><b>Are graph convolutional networks with random weights feasible?</b> ★[<a href="https://ieeexplore.ieee.org/abstract/document/9796468" target="_blank">link</a>]<font color=red><b>ESI Highly Cited Paper</b> </font>🏆<br>
C. Huang, <b>M. Li*</b>, F. Cao, H. Fujita, Z. Li, X. Wu<br>
<b>IEEE Transactions on Pattern Analysis and Machine Intelligence</b>, vol. 45, no. 3, pp. 2751-2768, 2023.
</p></li>
<br />
<li><p><b>From motivational experience to creative writing:
A motivational AR-based learning approach to promoting Chinese writing performance and
positive writing behaviours</b> [<a href="https://www.sciencedirect.com/science/article/abs/pii/S0360131523001215" target="_blank">link</a>]<br>
<b>M. Li</b>, Y. Chen, C. Huang, G. Hwang, M. Cukurova<br>
<b>Computers and Education</b>, vol. 202, 104844, 2023.
</p></li>
<br />
<li><p><b> How powerful are shallow neural networks with bandlimited random weights?</b> ★ [<a href="https://proceedings.mlr.press/v202/li23aa/li23aa.pdf">link</a>]<br>
<b>M. Li</b>, S. Sonoda, F. Cao, Y. G. Wang, J. Liang<br>
<b>ICML</b>, 2023, pp. 19960-19981.
</p></li><br />
<li><p><b>Multiple pedestrian tracking with graph attention map on urban road scene</b> ★[<a href="https://ieeexplore.ieee.org/document/9847123" target="_blank">link</a>] <br>
Z. Wang, Z. Li, J. Leng, <b>M. Li</b>*, L. Bai<br>
<b>IEEE Transactions on Intelligent Transportation Systems</b>, vol. 24, no. 8, pp. 8567-8579, 2023.</p></li>
<br />
<li><p><b> GoLoG: Global-to-local decoupling graph network with joint optimization for hyperspectral image classification</b> ★ [<a href="https://ieeexplore.ieee.org/document/10288272">link</a>]<br>
B. Yang, H. Ye, <b>M. Li</b>*, F. Cao, S. Pan<br>
<b>IEEE Transactions on Geoscience and Remote Sensing</b>, vol. 61, 5528014, 2023.
</p></li><br />
<li><p><b>Stability and generalization of ℓp-regularized stochastic learning for GCN</b> ★ [<a href="https://www.ijcai.org/proceedings/2023/631">link</a>]<br>
S. Lv, S. Liu, L. Wei, <b>M. Li</b><br>
<b>IJCAI</b>, 2023, pp. 5685-5693.
</p></li><br />
<li><p><b> BLoG: Bootstrapped graph representation learning with local and global regularization for recommendation</b> ★ [<a href="https://www.sciencedirect.com/science/article/abs/pii/S0031320323005721">link</a>]<br>
<b>M. Li</b>, L. Zhang, L. Cui, L. Bai, Z. Li, X. Wu<br>
<b>Pattern Recognition</b>, vol. 144, 109874, 2023.
</p></li><br />
<li><p><b> QBER: Quantum-based entropic representations for un-attributed graphs</b> ★ [<a href="https://www.sciencedirect.com/science/article/pii/S0031320323005757">link</a>] <br>
L. Cui, <b>M. Li</b>, L. Bai, Y. Wang, J. Li, Y. Wang, Z. Li, Y. Chen, E. Hancock<br>
<b>Pattern Recognition</b>, vol. 145, 109877, 2024.
</p></li><br />
<li><p><b>Revisiting graph neural networks from hybrid regularized graph signal reconstruction</b> ★ [<a href="https://www.sciencedirect.com/science/article/pii/S0893608022004439">link</a>]<br>
J. Miao, F. Cao, H. Ye, <b>M. Li</b>, B. Yang<br>
<b>Neural Networks</b>, vol. 157, pp. 444-459, 2023.
</p></li><br />
<li><p><b> Triplet teaching graph contrastive networks with self-evolving adaptive augmentation</b> ★ [<a href="https://www.sciencedirect.com/science/article/pii/S0031320323003850">link</a>][<a href="https://github.com/PaperMiao/T-GCSA">code</a>]<br>
J. Miao, F. Cao, <b>M. Li</b>, B. Yang, H. Ye<br>
<b>Pattern Recognition</b>, vol. 142, 109687, 2023.
</p></li><br />
<li><p><b> A new deep graph attention approach with influence and preference relationship reconstruction for rate prediction recommendation</b> ★ [<a href="https://www.sciencedirect.com/science/article/abs/pii/S0306457323001760">link</a>] <br>
H. Ye, Y. Song, <b>M. Li</b>, F. Cao<br>
<b>Information Processing and Management</b>, vol. 60, no. 5, 103439, 2023.
</p></li><br />
<li><p><b> MATHNET: Haar-Like wavelet multiresolution analysis for graph representation and learning</b> ★ [<a href="https://www.sciencedirect.com/science/article/abs/pii/S0950705123003593">link</a>]<br>
X. Zheng, B. Zhou, <b>M. Li*</b>, Y. G. Wang*, and J. Gao<br>
<b>Knowledge-Based Systems</b>, vol. 273, 110609, 2023.
</p></li><br />
<li><p><b> A disentangled linguistic graph model for explainable aspect-based sentiment analysis</b> ★[<a href="https://www.sciencedirect.com/science/article/abs/pii/S0950705122012461">link</a>]<br>
X. Mei, Y. Zhou, C. Zhu, M. Wu, <b>M. Li</b>*, S. Pan<br>
<b>Knowledge-Based Systems</b>, vol. 260, 110150, 2023.
</p></li><br />
<li><p><b> TeFNA: Text-centered fusion network with crossmodal attention for multimodal sentiment analysis</b> [<a href="https://www.sciencedirect.com/science/article/abs/pii/S0950705123002526">link</a>]<br>
C. Huang, J. Zhang, X. Wu, Y. Wang, <b>M. Li</b>*, X. Huang<br>
<b>Knowledge-Based Systems</b>, vol. 269, 110502, 2023.
</p></li><br />
<li><p><b> Entangled Quantum Neural Network</b> [<a href="https://link.springer.com/chapter/10.1007/978-981-19-9530-9_14">link</a>]<br>
Q Meng, J Zhang, Z Li, <b>M. Li*</b>, L Cui<br>
In: <b>Pandey, R., Srivastava, N., Singh, N.K., Tyagi, K. (eds) Quantum Computing: A Shift from Bits to Qubits. Studies in Computational Intelligence</b>, 245-262, 2023.
</p></li><br />
<li><p><b>Multi-view graph convolutional networks with attention mechanism</b> ★ [<a href="https://www.sciencedirect.com/science/article/pii/S0004370222000480">link</a>][<a href="https://sxu-yaokx.github.io/MAGCN/">code</a>]<br>
K. Yao, J. Liang, J. Liang, <b>M. Li</b>, F. Cao<br>
<b>Artificial Intelligence</b>, vol. 307, 103708, 2022.
</p></li>
<br />
<li><p><b>Embedding graphs on Grassmann manifold</b> ★ [<a href="https://www.sciencedirect.com/science/article/pii/S0893608022001733">link</a>][<a href="https://github.com/YuGuangWang/Egg">code</a>]<br>
B. Zhou, X. Zheng, Y. G. Wang, <b>M. Li</b>, J. Gao<br>
<b>Neural Networks</b>, vol. 152, pp. 322-331, 2022.
</p></li>
<br />
<li><p><b> Deep multi-graph neural networks with attention fusion for recommendation</b> ★[<a href="https://www.sciencedirect.com/science/article/abs/pii/S0957417421015505">link</a>]<br>
Y. Song, H. Ye, <b>M. Li</b>, F. Cao<br>
<b>Expert Systems with Applications</b>, vol. 191, 116240, 2022.
</p></li><br />
<li><p><b> Cell graph neural networks enable the digital staging of tumor microenvironment and precise prediction of patient survival in gastric cancer</b> ★ [<a href="https://www.nature.com/articles/s41698-022-00285-5#citeas">link</a>] [<a href="https://github.com/Docurdt/Cell-Graph_Signature">code</a>]<br>
Y. Wang, Y. G. Wang, C. Hu, <b>M. Li</b>, Y. Fan, N. Otter, et al.<br>
<b>npj Precison Oncology</b>, vol. 6, 45, 2022.
</p></li><br />
<li><p><b> Empowering IoT predictive maintenance solutions with AI: A distributed system for manufacturing plant-wide monitoring</b> [<a href="https://ieeexplore.ieee.org/document/9463585/">link</a>]<br>
Y. Liu, W. Yu, T. Dillon, W. Rahayu, <b>M. Li</b>*<br>
<b>IEEE Transactions on Industrial Informatics</b>, vol. 18, no. 2, pp. 1345-1354, 2022.
</p></li><br />
<li><p><b>Promoting deep writing with immersive technologies: An SVVR‐supported Chinese composition writing approach for primary schools</b>
[<a href="https://bera-journals.onlinelibrary.wiley.com/doi/abs/10.1111/bjet.13247">link</a>]<br>
Y. Chen, <b>M. Li*</b>, C. Huang, Z. Han, G. Hwang, G. Yang<br>
<b>British Journal of Educational Technology</b>, vol. 53, no. 6, pp. 2071-2091, 2022.
</p></li>
<br />
<li><p><b> Deeper insights into neural nets with random weights</b> [<a href="https://link.springer.com/chapter/10.1007/978-3-030-97546-3_11">link</a>]<br>
<b>M. Li</b>, G. Gnecco and M. Sanguineti<br>
in: Proceedings of the Australasian Joint Conference on Artificial Intelligence (<b>AJCAI</b>), 2022, pp. 129-140.
</p></li><br />
<li><p><b> Feedforward neural network reconstructed from high-order quantum systems</b><br>
J. Zhang, Z. Li, X. Wang, H. Peng, <b>M. Li</b><br>
in: Proceedings of the International Joint Conference on Neural Networks (<b>IJCNN</b>), 2022.
</p></li><br />
<li><p><b> Neural network model reconstructed from entangled quantum states</b><br>
J. Zhang, Z. Li, J. X, <b>M. Li</b><br>
in: Proceedings of the International Joint Conference on Neural Networks (<b>IJCNN</b>), 2022.
</p></li><br>
<li><p><b> How framelets enhance graph neural networks</b> ★[<a href="http://proceedings.mlr.press/v139/zheng21c/zheng21c-supp.pdf">link</a>] [<a href="https://github.com/YuGuangWang/UFG">code</a>]<br>
X. Zheng, B. Zhou, J. Gao, Y. G. Wang, P. Lio, <b>M. Li</b>, G. Montufar<br>
<b>ICML</b>, 2021, pp. 12761-12771. (Spotlight Paper)
</p></li><br />
<li><p><b> Effective multiple pedestrian tracking system in video surveillance with monocular stationary camera</b> [<a href="https://www.sciencedirect.com/science/article/abs/pii/S0957417421004334">link</a>]<br>
Z. Wang, <b>M. Li</b>*, Y. Lu, Y. Bao, Z. Li, J. Zhao<br>
<b>Expert Systems with Applications</b>, vol. 178, 114992, 2021.
</p></li><br />
<li><p><b> Grassmann graph embedding</b> ★ [<a href="https://openreview.net/forum?id=ELsrI4NgvS-">link</a>][<a href="https://github.com/YuGuangWang/Egg">code</a>]<br>
B. Zhou, X. Zheng, Y. G. Wang, <b>M. Li</b>, J. Gao<br>
ICLR Workshop on Geometrical and Topological Representation Learning (<b>GTRL</b>), 2021.
</p></li><br />
<li><p><b> Stochastic configuration network ensembles with selective base models</b> [<a href="https://www.sciencedirect.com/science/article/abs/pii/S0893608021000198">link</a>]<br>
C. Huang, <b>M. Li</b>*, D. Wang<br>
<b>Neural Networks</b>, vol. 137, pp. 106-118, 2021.
</p></li><br />
<li><p><b> Algorithm 1018: FaVeST–fast vector spherical harmonic transforms</b> [<a href="https://dl.acm.org/doi/10.1145/3458470">link</a>][<a href="https://github.com/mingli-ai/FaVeST">code</a>]<br>
Q. T. Le Gia, <b>M. Li</b>*, Y. G. Wang<br>
<b>ACM Transactions on Mathematical Softwares</b>, vol. 47, no. 4, pp. 1-24, 2021.
</p></li><br />
<li><p><b> 2-D stochastic configuration networks for image data analytics</b> [<a href="https://ieeexplore.ieee.org/document/8767029">link</a>]<br>
<b>M. Li</b>, D. Wang<br>
<b>IEEE Transactions on Cybernetics</b>, vol. 51, no. 1, pp. 359-372, 2021.
</p></li>
<br />
<li><p><b> Path integral based convolution and pooling for graph neural networks</b> ★ [<a href="https://arxiv.org/abs/2006.16811">link</a>][<a href="https://github.com/YuGuangWang/PAN">code</a>][<a href="https://pytorch-geometric.readthedocs.io/en/latest/_modules/torch_geometric/nn/conv/pan_conv.html">PyG Implementation</a>]<br>
Z. Ma, J. Xuan, Y. G. Wang, <b>M. Li</b>, P. Lio<br>
<b>NeurIPS</b>, 2020, pp. 16421-16433.
</p></li><br />
<li><p><b> Exercise recommendation based on knowledge concept prediction</b> [<a href="https://www.sciencedirect.com/science/article/abs/pii/S0950705120306109">link</a>]<br>
Z. Wu, <b>M. Li</b>, Y. Tang, Q. Liang<br>
<b>Knowledge-Based Systems</b>, vol. 210, 106481, 2020.
</p></li><br />
<li><p><b> rcosmo: R Package for Analysis of Spherical, HEALPix and Cosmological Data</b> [<a href="https://journal.r-project.org/archive/2020/RJ-2020-012/index.html">link</a>][<a href="https://cran.r-project.org/web/packages/rcosmo/index.html">package</a>]<br>
D. Fryer, <b>M. Li</b>, A. Olenko<br>
<b>The R Journal</b>, vol. 12, no. 1, 206-225, 2020.
</p></li><br />
<li><p><b> Haar graph pooling</b> ★ [<a href="http://proceedings.mlr.press/v119/wang20m/wang20m.pdf">link</a>][<a href="https://github.com/YuGuangWang/HaarPool">code</a>]<br>
Y. G. Wang, <b>M. Li</b>*, Z. Ma, G. Montufar, X. Zhuang, Y. Fan<br>
<b>ICML</b>, 2020, pp.9952-9962.
</p></li><br />
<li><p><b> Fast Haar transforms for graph neural networks</b> ★ [<a href="https://www.sciencedirect.com/science/article/pii/S0893608020301568?via%3Dihub">link</a>]<br>
<b>M. Li</b>, Z. Ma, Y. G. Wang, X. Zhuang<br>
<b>Neural Networks</b>, vol. 128, pp. 188-198, 2020.
</p></li>
<br />
<li><p><b> PAN: Path integral based convolution for deep graph neural networks</b> ★ [<a href="https://icml.cc/Conferences/2019/Schedule?showEvent=3519">link</a>]<br>
Z. Ma, <b>M. Li</b>, Y. G. Wang<br>
<b>ICML Workshop on Learning and Reasoning with Graph-Structured Representation</b>, 2019.
</p></li><br />
<li><p><b> Improved randomized learning algorithms for imbalanced and noisy educational data classification</b> [<a href="https://link.springer.com/article/10.1007/s00607-018-00698-w">link</a>]<br>
<b>M. Li</b>, C. Huang, Q. Hu, J. Zhu, Y. Tang<br>
<b>Computing</b>, pp. 1-15, 2019.
</p></li><br />
<li><p><b> Robust stochastic configuration networks with maximum correntropy criterion for uncertain data regression</b> [<a href="https://www.sciencedirect.com/science/article/pii/S0020025518307278">link</a>]<br>
<b>M. Li</b>, C. Huang, and D. Wang<br>
<b>Information Sciences</b>, vol. 473, 73-86, 2019.
</p></li><br />
<li><p><b> Deep stochastic configuration networks with universal approximation property</b> [<a href="https://ieeexplore.ieee.org/document/8489695">link</a>]<br>
D. Wang, <b>M. Li</b><br>
Proceedings of the International Joint Conference on Neural Networks (<b>IJCNN</b>), pp. 1-8, IEEE, 2018.
</p></li><br />
<li><p><b> Stochastic configuration networks: Fundamentals and algorithms</b> [<a href="https://ieeexplore.ieee.org/abstract/document/8013920">link</a>][<a href="https://deepscn.com/software.html">codek</a>][<a href="http://www.deepscn.com/software.php">code</a>]<font color=red><b>ESI Highly Cited Paper</b> </font>🏆<br>
D. Wang, <b>M. Li</b><br>
<b>IEEE Transactions on Cybernetics</b>, vol. 47, no.10, pp. 3466-3479, 2017.
</p></li><br />
<li><p><b> Insights into randomized algorithms for neural networks: Practical issues and common pitfalls</b> [<a href="https://www.sciencedirect.com/science/article/pii/S002002551631917X">link</a>]<br>
<b>M. Li</b>, D. Wang<br>
<b>Information Sciences</b>, vol. 382-383, pp. 170-178, 2017.
</p></li><br />
<li><p><b> Robust stochastic configuration networks with kernel density estimation for uncertain data regression</b> [<a href="https://www.sciencedirect.com/science/article/pii/S0020025517307636">link</a>]<br>
D. Wang, <b>M. Li</b><br>
<b>Information Sciences</b>, vol. 412-413, pp. 210-222, 2017.
</p></li><br />
<li><p><b> Spherical data fitting by multiscale moving least squares</b> [<a href="https://www.sciencedirect.com/science/article/abs/pii/S0307904X14006428">link</a>]<br>
F. Cao, <b>M. Li</b><br>
<b>Applied Mathematical Modelling</b>, vol. 39, no. 12, pp. 3448-3458, 2015.
</p></li><br />
<li><p><b> Scattered data quasi-interpolation on spheres</b> [<a href="https://onlinelibrary.wiley.com/doi/full/10.1002/mma.3239">link</a>]<br>
Z. Chen, F. Cao, <b>M. Li</b><br>
<b>Mathematical Methods in the Applied Sciences</b>, vol. 38, no. 12, pp. 2527-2536, 2015.
</p></li><br />
<li><p><b> Multiscale interpolation on the sphere: Convergence rate and inverse theorem</b> [<a href="https://www.sciencedirect.com/science/article/abs/pii/S0096300315004907">link</a>]<br>
<b>M. Li</b>, F. Cao<br>
<b>Applied Mathematics and Computation</b>, vol. 263, pp. 134-150, 2015.
</p></li><br />
<li><p><b> Approximation by diffuse functional of generalized moving least squares on the sphere</b> [<a href="https://actamath.cjoe.ac.cn/Jwk_sxxb_cn/EN/10.12386/A2014sxxb0057">link</a>]<br>
F. Cao, Y. Zhang, <b>M. Li</b><br>
<b> Acta Mathematica Sinica</b>, vol. 57, no. 3, pp. 607-614, 2014.
</p></li><br />
<li><p><b> Local uniform error estimates for spherical basis functions interpolation</b> [<a href="https://onlinelibrary.wiley.com/doi/abs/10.1002/mma.2898">link</a>]<br>
<b>M. Li</b>, F. Cao<br>
<b>Mathematical Methods in the Applied Sciences</b>, vol. 37, no. 9, pp. 1364-1376, 2014.
</p></li><br />
</ul>
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