Publications

Are graph convolutional networks with random weights feasible? ★[link]ESI Highly Cited Paper 🏆
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 ★ [link][Code]
K. Yao, J. Liang, J. Liang, M. Li, F. Cao
Artificial Intelligence, vol. 307, 103708, 2022.

Collaborative knowledge graph fusion by exploiting the open corpus ★ [link]
Y. Wang, Y. Wan, L. Bai, L. Cui, Z. Xu, M. Li, P. Yu, E. Hancock
IEEE Transactions on Knowledge and Data Engineering, 2023, DOI: 10.1109/TKDE.2023.3289949.

A simple yet effective framelet-based graph neural network for directed graphs★
C. Zou, A. Han, L. Lin, M. Li, J. Gao
IEEE Transactions on Artificial Intelligence, 2023, DOI: 10.1109/TAI.2023.3316628.

GoLoG: Global-to-local decoupling graph network with joint optimization for hyperspectral image classification ★ [link]
B. Yang, H. Ye, M. Li*, F. Cao, S. Pan
IEEE Transactions on Geoscience and Remote Sensing, vol. 61, 5528014, 2023.

Path integral based convolution and pooling for graph neural networks ★ [link]
Z. Ma, J. Xuan, Y. G. Wang, M. Li, P. Lio
NeurIPS, 2020, pp. 16421-16433.

How powerful are shallow neural networks with bandlimited random weights? ★ [link]
M. Li, S. Sonoda, F. Cao, Y. G. Wang, J. Liang
ICML, 2023, pp. 19960-19981.

Haar graph pooling ★ [link][code]
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]
X. Zheng, B. Zhou, J. Gao, Y. G. Wang, P. Lio, M. Li, G. Montufar
ICML, 2021, pp. 12761-12771.

Stability and generalization of ℓp-regularized stochastic learning for GCN ★ [link]
S. Liu, L. Wei, S. Lv, M. Li
IJCAI, 2023, pp. 5685-5693.

BLoG: Bootstrapped graph representation learning with local and global regularization for recommendation ★[link]
M. Li, L. Zhang, L. Cui, L. Bai, Z. Li, X. Wu
Pattern Recognition, vol. 144, 109874, 2023.

QBER: Quantum-based entropic representations for un-attributed graphs ★ [link]
L. Cui, M. Li, L. Bai, Y. Wang, J. Li, Y. Wang, Z. Li, Y. Chen, E. Hancock
Pattern Recognition, vol. 145, 109877, 2024.

Fast Haar transforms for graph neural networks ★ [link]
M. Li, Z. Ma, Y. G. Wang, X. Zhuang
Neural Networks, vol. 128, pp. 188-198, 2020.

A unified framework for exploratory learning-aided community detection in networks with unknown topology ★ [link]
Y. Hou, C. Tran, M. Li, W. Y. Shin
submitted to IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023.

Key instance attack for multi-instance learning
Y. Zhang, Z. Zhou, M. Li, R. Shen
submitted to IEEE Transactions on Image Processing, 2023.

EduGraph: Learning path-based hypergraph neural networks for MOOC course recommendation ★ [link]
M. Li, Z. Li, X. Wu
submitted to IEEE Transactions on Big Data, 2023.

Graph representation learning for interactive biomolecule systems ★
X. Xiong, B. Zhou, P. Lio, Y. G. Wang, M. Li
to be submitted to Nature Machine Intelligence, 2023.

HAQJSK: Hierarchical-aligned quantum Jensen-Shannon kernels for graph classification ★ [link]
L. Bai, L. Cui, Y. Wang, M. Li, J. Li, P. Yu, E. R. Hancock
submitted to IEEE Transactions on Knowledge and Data Engineering, 2023.

Multimodal graph learning based on 3D Haar semi-tight framelet for student engagement prediction ★
M. Li, X. Zhuang, L. Bai, W. Ding
revision submitted to Information Fusion, 2023.

Permutaion equivariant graph framelets for heterophilous graph learning ★ [link]
J. Li, R. Zheng, H. Feng, M. Li*, X. Zhuang*
revision submitted to IEEE Transactions on Neural Networks and Learning Systems, 2023.

Towards understanding the stability and generalization of deep graph convolutional networks: A theoretical study ★ [link]
G. Yang, M. Li*, H. Feng, X. Zhuang
submitted to Artificial Intelligence, 2023.

Flow2GNN: Flexible two-way flow message passing for enhancing GNNs beyond homophily ★
C. Huang, Y. Wang, Y. Jiang, M. Li, X. Huang, S. Pan, C. Zhou
submitted to IEEE Transactions on Cybernetics, 2023.

XKT: Towards explainable knowledge tracing with multiple knowledge concept annotations
C. Huang, Q. Huang, X. Huang, H. Wang, M. Li, K. Lin, Y. Chang
revision submitted to IEEE Transactions on Knowledge and Data Engineering, 2023.

A feature reuse framework with texture-adaptive aggregation for reference-based super-resolution [link]
X. Mei, Y. Yang, M. Li*, C. Huang, K. Zhang, P. Lio
revision submitted to IEEE Transactions on Image Processing, 2023.

AERK: Aligned entropic reproducing kernels through continuous-time quantum walks [link]
L. Cui, M. Li, Y. Wang, L. Bai, E. R. Hancock
submitted to IEEE Transactions on Neural Networks and Learning Systems, 2023.

Small object detection super-resolution: A novel three-stage architecture for improved object detection and texture restoration in educational settings
X. Mei, K. Zhang, C. Huang, X. Chen, M. Li*, Z. Li, W. Ding, X. Wu
submitted to IEEE Transactions on Emerging Topics in Computational Intelligence, 2023.

MM-tracker: Visual tracking with a multi-task model integrating detection and differentiating feature extraction
Z. Wang, M. Li, Z. Li, X. Zhang, M. Li*, Z. Li, W. Ding, X. Wu
revision submitted to IEEE Transactions on Emerging Topics in Computational Intelligence, 2023.

A pointtransform network for automatic depression level prediction via facial keypoints
M. Niu, M. Li, C. Fu
submitted to Knowledge-Based Systems, 2023.

Mask-based residual graph neural networks ★
K. Yao, Z. Du, M. Li, J. Liang
submitted to International Journal of Machine Learning and Cybernetics, 2023.

Fast tensor needlet transforms for tangent vector fields on the sphere ✠[link]
M. Li, J. Chen, P. Broadbridge, A. Olenko, Y. G. Wang
submitted to Applied and Computational Harmonic Analysis, 2023.

AG-Meta: Adaptive graph meta learning via representation consistency over local subgraphs ★
Y. Wang, C. Huang, M. Li, Q. Huang, X. Wu, J. Wu
revision submitted to Pattern Reocognition, 2023.

Are graph convolutional networks with random weights feasible? ★[link]ESI Highly Cited Paper 🏆
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.

From motivational experience to creative writing: A motivational AR-based learning approach to promoting Chinese writing performance and positive writing behaviours [link]
M. Li, Y. Chen, C. Huang, G. Hwang, M. Cukurova
Computers and Education, vol. 202, 104844, 2023.

How powerful are shallow neural networks with bandlimited random weights? ★ [link]
M. Li, S. Sonoda, F. Cao, Y. G. Wang, J. Liang
ICML, 2023, pp. 19960-19981.

Collaborative knowledge graph fusion by exploiting the open corpus ★ [link]
Y. Wang, Y. Wan, L. Bai, L. Cui, Z. Xu, M. Li, P. Yu, E. Hancock
IEEE Transactions on Knowledge and Data Engineering, 2023, DOI: 10.1109/TKDE.2023.3289949.

Multiple pedestrian tracking with graph attention map on urban road scene ★[link]
Z. Wang, Z. Li, J. Leng, M. Li*, L. Bai
IEEE Transactions on Intelligent Transportation Systems, vol. 24, no. 8, pp. 8567-8579, 2023.

A simple yet effective framelet-based graph neural network for directed graphs★
C. Zou, A. Han, L. Lin, M. Li, J. Gao
IEEE Transactions on Artificial Intelligence, 2023, DOI: 10.1109/TAI.2023.3316628.

GoLoG: Global-to-local decoupling graph network with joint optimization for hyperspectral image classification ★ [link]
B. Yang, H. Ye, M. Li*, F. Cao, S. Pan
IEEE Transactions on Geoscience and Remote Sensing, vol. 61, 5528014, 2023.

Stability and generalization of ℓp-regularized stochastic learning for GCN ★ [link]
S. Lv, S. Liu, L. Wei, M. Li
IJCAI, 2023, pp. 5685-5693.

BLoG: Bootstrapped graph representation learning with local and global regularization for recommendation ★ [link]
M. Li, L. Zhang, L. Cui, L. Bai, Z. Li, X. Wu
Pattern Recognition, vol. 144, 109874, 2023.

QBER: Quantum-based entropic representations for un-attributed graphs ★ [link]
L. Cui, M. Li, L. Bai, Y. Wang, J. Li, Y. Wang, Z. Li, Y. Chen, E. Hancock
Pattern Recognition, vol. 145, 109877, 2024.

Revisiting graph neural networks from hybrid regularized graph signal reconstruction ★ [link]
J. Miao, F. Cao, H. Ye, M. Li, B. Yang
Neural Networks, vol. 157, pp. 444-459, 2023.

Triplet teaching graph contrastive networks with self-evolving adaptive augmentation ★ [link]
J. Miao, F. Cao, M. Li, B. Yang, H. Ye
Pattern Recognition, vol. 142, 109687, 2023.

A new deep graph attention approach with influence and preference relationship reconstruction for rate prediction recommendation ★ [link]
H. Ye, Y. Song, M. Li, F. Cao
Information Processing and Management, vol. 60, no. 5, 103439, 2023.

MATHNET: Haar-Like wavelet multiresolution analysis for graph representation and learning ★ [link]
X. Zheng, B. Zhou, M. Li*, Y. G. Wang*, and J. Gao
Knowledge-Based Systems, vol. 273, 110609, 2023.

A disentangled linguistic graph model for explainable aspect-based sentiment analysis ★[link]
X. Mei, Y. Zhou, C. Zhu, M. Wu, M. Li*, S. Pan
Knowledge-based Systems, vol. 260, 110150, 2023.

TeFNA: Text-centered fusion network with crossmodal attention for multimodal sentiment analysis [link]
C. Huang, J. Zhang, X. Wu, Y. Wang, M. Li*, X. Huang
Knowledge-Based Systems, vol. 269, 110502, 2023.

A joint parcellation and boundary network with multi-rate-shared dilated graph attention for cortical surface parcellation
S. Liu, H. Ye, B. Yang, M. Li, F. Cao
Medical & Biological Engineering & Computing, 2023, DOI: 10.1007/s11517-023-02942-8.

A systematic review of research on immersive technology-enhanced writing education: The current state and a research agenda
Y. Chen, M. Li*, C. Huang, M. Cukurova, Q. Ma
IEEE Transactions on Learning Technologies, 2023, accepted.

Incorporation of peer-feedback into the pedagogical use of spherical video-based virtual reality in writing education[link]
Y. Chen, M. Li*, M. Cukurova, M. Jong
British Journal of Educational Technology, 2023, DOI: 10.1111/bjet.13376.

Unleashing imagination: An effective pedagogical approach to integrate into spherical video-based virtual reality to improve students' creative writing [link]
Y. Chen, M. Li*, M. Cukurova
Education and Information Technologies, 2023, DOI: 10.1007/s10639-023-12115-7.

Real-time E-bike route planning with battery range prediction
Z. Li, G. Ren, Y. Gu, S. Zhou, X. Liu, J. Huang, M. Li*
WSDM Demonstrations, 2024, DOI: 10.1145/3616855.3635696.

Entangled Quantum Neural Network [link]
Q Meng, J Zhang, Z Li, M. Li*, L Cui
In: Pandey, R., Srivastava, N., Singh, N.K., Tyagi, K. (eds) Quantum Computing: A Shift from Bits to Qubits. Studies in Computational Intelligence, 245-262, 2023.

Multi-view graph convolutional networks with attention mechanism ★ [link][code]
K. Yao, J. Liang, J. Liang, M. Li, F. Cao
Artificial Intelligence, vol. 307, 103708, 2022.

Towards graph self-supervised learning with contrastive adjusted zooming ★ [link]
Y. Zheng, M. Jin, S. Pan, Y. F. Li, H. Peng, M. Li, Z. Li
IEEE Transactions on Neural Networks and Learning Systems, 2022, DOI: 10.1109/TNNLS.2022.3216630.

Embedding graphs on Grassmann manifold ★[link]
B. Zhou, X. Zheng, Y. G. Wang, M. Li, J. Gao
Neural Networks, vol. 152, pp. 322-331, 2022.

Deep multi-graph neural networks with attention fusion for recommendation ★[link]
Y. Song, H. Ye, M. Li, F. Cao
Expert Systems with Applications, vol. 191, 116240, 2022.

Cell graph neural networks enable the digital staging of tumor microenvironment and precise prediction of patient survival in gastric cancer ★ [link]
Y. Wang, Y. G. Wang, C. Hu, M. Li, Y. Fan, N. Otter, et al.
npj Precison Oncology, vol. 6, 45, 2022.

Empowering IoT predictive maintenance solutions with AI: A distributed system for manufacturing plant-wide monitoring [link]
Y. Liu, W. Yu, T. Dillon, W. Rahayu, M. Li*
IEEE Transactions on Industrial Informatics, vol. 18, no. 2, pp. 1345-1354, 2022.

Promoting deep writing with immersive technologies: An SVVR‐supported Chinese composition writing approach for primary schools [link]
Y. Chen, M. Li*, C. Huang, Z. Han, G. Hwang, G. Yang
British Journal of Educational Technology, vol. 53, no. 6, pp. 2071-2091, 2022.

Deeper insights into neural nets with random weights [link]
M. Li, G. Gnecco and M. Sanguineti
in: Proceedings of the Australasian Joint Conference on Artificial Intelligence (AJCAI), 2022, pp. 129-140.

Feedforward neural network reconstructed from high-order quantum systems
J. Zhang, Z. Li, X. Wang, H. Peng, M. Li
in: Proceedings of the International Joint Conference on Neural Networks (IJCNN), 2022.

Neural network model reconstructed from entangled quantum states
J. Zhang, Z. Li, J. X, M. Li
in: Proceedings of the International Joint Conference on Neural Networks (IJCNN), 2022.

How framelets enhance graph neural networks ★[link] [code]
X. Zheng, B. Zhou, J. Gao, Y. G. Wang, P. Lio, M. Li, G. Montufar
ICML, 2021, pp. 12761-12771.

Effective multiple pedestrian tracking system in video surveillance with monocular stationary camera [link]
Z. Wang, M. Li*, Y. Lu, Y. Bao, Z. Li, J. Zhao
Expert Systems with Applications, vol. 178, 114992, 2021.

Grassmann graph embedding ★[link]
B. Zhou, X. Zheng, Y. G. Wang, M. Li, J. Gao
ICLR Workshop on Geometrical and Topological Representation Learning (GTRL), 2021.

Stochastic configuration network ensembles with selective base models [link]
C. Huang, M. Li*, D. Wang
Neural Networks, vol. 137, pp. 106-118, 2021.

Algorithm 1018: FaVeST–fast vector spherical harmonic transforms [link][code]
Q. T. Le Gia, M. Li*, Y. G. Wang
ACM Transactions on Mathematical Softwares, vol. 47, no. 4, pp. 1-24, 2021.

2-D stochastic configuration networks for image data analytics [link]
M. Li, D. Wang
IEEE Transactions on Cybernetics, vol. 51, no. 1, pp. 359-372, 2021.

Path integral based convolution and pooling for graph neural networks ★ [link]
Z. Ma, J. Xuan, Y. G. Wang, M. Li, P. Lio
NeurIPS, 2020, pp. 16421-16433.

Exercise recommendation based on knowledge concept prediction [link]
Z. Wu, M. Li, Y. Tang, Q. Liang
Knowledge-Based Systems, vol. 210, 106481, 2020.

rcosmo: R Package for Analysis of Spherical, HEALPix and Cosmological Data [link][package]
D. Fryer, M. Li, A. Olenko
The R Journal, vol. 12, no. 1, 206-225, 2020.

Haar graph pooling ★ [link][code]
Y. G. Wang, M. Li*, Z. Ma, G. Montufar, X. Zhuang, Y. Fan
ICML, 2020, pp.9952-9962.

Fast Haar transforms for graph neural networks ★ [link]
M. Li, Z. Ma, Y. G. Wang, X. Zhuang
Neural Networks, vol. 128, pp. 188-198, 2020.

PAN: Path integral based convolution for deep graph neural networks ★ [link]
Z. Ma, M. Li, Y. G. Wang
ICML Workshop on Learning and Reasoning with Graph-Structured Representation, 2019.

Improved randomized learning algorithms for imbalanced and noisy educational data classification [link]
M. Li, C. Huang, Q. Hu, J. Zhu, Y. Tang
Computing, pp. 1-15, 2019.

Robust stochastic configuration networks with maximum correntropy criterion for uncertain data regression [link]
M. Li, C. Huang, and D. Wang
Information Sciences, vol. 473, 73-86, 2019.

Deep stochastic configuration networks with universal approximation property [link]
D. Wang, M. Li
Proceedings of the International Joint Conference on Neural Networks (IJCNN), pp. 1-8, IEEE, 2018.

Stochastic configuration networks: Fundamentals and algorithms [link][code]ESI Highly Cited Paper 🏆
D. Wang, M. Li
IEEE Transactions on Cybernetics, vol. 47, no.10, pp. 3466-3479, 2017.

Insights into randomized algorithms for neural networks: Practical issues and common pitfalls [link]
M. Li, D. Wang
Information Sciences, vol. 382-383, pp. 170-178, 2017.

Robust stochastic configuration networks with kernel density estimation for uncertain data regression [link]
D. Wang, M. Li
Information Sciences, vol. 412-413, pp. 210-222, 2017.

Spherical data fitting by multiscale moving least squares [link]
F. Cao, M. Li
Applied Mathematical Modelling, vol. 39, no. 12, pp. 3448-3458, 2015.

Scattered data quasi-interpolation on spheres [link]
Z. Chen, F. Cao, M. Li
Mathematical Methods in the Applied Sciences, vol. 38, no. 12, pp. 2527-2536, 2015.

Multiscale interpolation on the sphere: Convergence rate and inverse theorem [link]
M. Li, F. Cao
Applied Mathematics and Computation, vol. 263, pp. 134-150, 2015.

Approximation by diffuse functional of generalized moving least squares on the sphere
F. Cao, Y. Zhang, M. Li
Acta Mathematica Sinica, vol. 57, no. 3, pp. 607-614, 2014.

Local uniform error estimates for spherical basis functions interpolation [link]
M. Li, F. Cao
Mathematical Methods in the Applied Sciences, vol. 37, no. 9, pp. 1364-1376, 2014.

The equivalent theorem for Jackson-type operators on spherical cap
M. Li, F. Cao, Z. Chen
Journal of Computational Analysis and Applications, vol. 15, no. 4, pp. 778-787, 2013.

Pointwise error estimates for spherical hybrid interpolation
C. Ding, M. Li, F. Cao
Journal of Computational Analysis and Applications, vol. 26, no. 1, pp. 77-84, 2019.

Lp approximation errors for hybrid interpolation on the unit sphere
C. Ding, M. Li, F. Cao
Journal of Computational Analysis and Applications, vol. 26, no. 1, pp. 77-84, 2019.