World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
40
Citations
6970
World Ranking
9294
National Ranking
1178

Overview

Mingsheng Shang is affiliated with the Chinese Academy of Sciences in China and has contributed extensively to the field of computer science, with a primary focus on artificial intelligence and its various subfields. Their research encompasses multiple specialized areas including artificial intelligence, computer vision and pattern recognition, information systems, control and systems engineering, and statistical and nonlinear physics.

The scientist has published papers in a range of topics that highlight their expertise in both theoretical and applied aspects of machine learning and complex networks. Key research themes include recommender systems and techniques, neural networks and applications, complex network analysis techniques, advanced neural network applications, advanced graph neural networks, face and expression recognition, and machine learning methodologies such as extreme learning machines (ELM).

Mingsheng Shang's frequent publication venues underline their engagement with leading journals and conferences. These include:

  • IEEE Transactions on Neural Networks and Learning Systems
  • Information Sciences
  • Neurocomputing
  • arXiv (Cornell University)
  • SSRN Electronic Journal

The scientist has collaborated extensively with a set of frequent co-authors, indicating ongoing research partnerships. These co-authors include Xiaoyu Shi, Mei Liu, Long Jin, Xin Luo, and Hong Xie.

Among recent publications, the following works are notable for their topics and citation volume:

  • "Activated Gradients for Deep Neural Networks," 2021, IEEE Transactions on Neural Networks and Learning Systems
  • "A Data-Characteristic-Aware Latent Factor Model for Web Services QoS Prediction," 2020, IEEE Transactions on Knowledge and Data Engineering
  • "An L1-and-L2-Norm-Oriented Latent Factor Model for Recommender Systems," 2021, IEEE Transactions on Neural Networks and Learning Systems
  • "Epidemic spreading on higher-order networks," 2024, Physics Reports
  • "Highly-Accurate Community Detection via Pointwise Mutual Information-Incorporated Symmetric Non-Negative Matrix Factorization," 2020, IEEE Transactions on Network Science and Engineering

Mingsheng Shang's body of work demonstrates a sustained investigation into both neural network methodologies and complex network structures applied across various computational challenges. This includes contributions to recommender systems and the analysis of higher-order network dynamics.

Best Publications

  • Identifying influential nodes in complex networks

    Duanbing Chen;Linyuan Lü;Ming-Sheng Shang;Yi-Cheng Zhang;Yi-Cheng Zhang

  • User-Based Collaborative-Filtering Recommendation Algorithms on Hadoop

    Zhi-Dan Zhao;Ming-sheng Shang

  • Activated Gradients for Deep Neural Networks.

    Mei Liu;Liangming Chen;Xiaohao Du;Long Jin

  • An Inherently Nonnegative Latent Factor Model for High-Dimensional and Sparse Matrices from Industrial Applications

    Xin Luo;MengChu Zhou;Shuai Li;MingSheng Shang

  • A Data-Characteristic-Aware Latent Factor Model for Web Services QoS Prediction

    Di Wu;Xin Luo;Mingsheng Shang;Yi He

  • Detecting overlapping communities of weighted networks via a local algorithm

    Duanbing Chen;Mingsheng Shang;Zehua Lv;Yan Fu

  • Empirical analysis of web-based user-object bipartite networks

    Ming-Sheng Shang;Ming-Sheng Shang;Linyuan Lü;Yi-Cheng Zhang;Yi-Cheng Zhang;Tao Zhou;Tao Zhou

  • A Deep Latent Factor Model for High-Dimensional and Sparse Matrices in Recommender Systems

    Di Wu;Xin Luo;Mingsheng Shang;Yi He

  • Symmetric and Nonnegative Latent Factor Models for Undirected, High-Dimensional, and Sparse Networks in Industrial Applications

    Xin Luo;Jianpei Sun;Zidong Wang;Shuai Li

  • A Fast Non-Negative Latent Factor Model Based on Generalized Momentum Method

    Xin Luo;Zhigang Liu;Shuai Li;Mingsheng Shang

  • An L₁-and-L₂-Norm-Oriented Latent Factor Model for Recommender Systems

    Di Wu;Mingsheng Shang;Xin Luo;Zidong Wang

  • Collaborative filtering with diffusion-based similarity on tripartite graphs

    Ming-Sheng Shang;Zi-Ke Zhang;Tao Zhou;Tao Zhou;Yi-Cheng Zhang;Yi-Cheng Zhang

  • Self-training semi-supervised classification based on density peaks of data

    Di Wu;Ming sheng Shang;Xin Luo;Ji Xu;Ji Xu

  • Non-Negativity Constrained Missing Data Estimation for High-Dimensional and Sparse Matrices from Industrial Applications

    Xin Luo;Mengchu Zhou;Shuai Li;Lun Hu

  • Algorithms of Unconstrained Non-Negative Latent Factor Analysis for Recommender Systems

    Xin Luo;MengChu Zhou;Shuai Li;Di Wu

  • Randomized latent factor model for high-dimensional and sparse matrices from industrial applications

    Mingsheng Shang;Xin Luo;Zhigang Liu;Jia Chen

  • A Posterior-neighborhood-regularized Latent Factor Model for Highly Accurate Web Service QoS Prediction

    Di Wu;Qiang He;Xin Luo;Mingsheng Shang

  • Highly-Accurate Community Detection via Pointwise Mutual Information-Incorporated Symmetric Non-Negative Matrix Factorization

    Xin Luo;Zhigang Liu;Mingsheng Shang;Jungang Lou

  • A Highly Accurate Framework for Self-Labeled Semisupervised Classification in Industrial Applications

    Di Wu;Xin Luo;Guoyin Wang;Mingsheng Shang

  • A fast and efficient heuristic algorithm for detecting community structures in complex networks

    Duanbing Chen;Yan Fu;Mingsheng Shang

  • An Instance-Frequency-Weighted Regularization Scheme for Non-Negative Latent Factor Analysis on High-Dimensional and Sparse Data

    Xin Luo;Zidong Wang;Mingsheng Shang

  • Large-scale and Scalable Latent Factor Analysis via Distributed Alternative Stochastic Gradient Descent for Recommender Systems

    Xiaoyu Shi;Qiang He;Xin Luo;Yannai Bai

Frequent Co-Authors

Xin Luo
Xin Luo Chinese Academy of Sciences
Yi-Cheng Zhang
Yi-Cheng Zhang University of Fribourg
Tao Zhou
Tao Zhou University of Electronic Science and Technology of China
Shuai Li
Shuai Li University of Oulu
MengChu Zhou
MengChu Zhou New Jersey Institute of Technology
Linyuan Lü
Linyuan Lü University of Electronic Science and Technology of China
Long Jin
Long Jin Lanzhou University
Zidong Wang
Zidong Wang Brunel University London
Qiang He
Qiang He Swinburne University of Technology
Xindong Wu
Xindong Wu Hefei University of Technology

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