Auto-ibDLM is proposed, a network-driven deep learning framework that represents events as dynamic interaction networks and predicts public event evolution through participant growth forecasting and consistently outperforms representative state-of-the-art methods in both forecasting accuracy and generalization capabili...
Jie Wei, Yue Liu, Xiao-Chuan Tang et al.· 0 citations
SaDGAE, an unsupervised deep graph autoencoder (GAE) framework that jointly models gene expression patterns and cell–cell relationships, achieves strong and competitive clustering performance, yielding biologically interpretable clusters and accurately recovering known marker gene patterns.
Xiang-Hui Liu, Yanmei Hu, Sheng-Lin Yang et al.· International Journal of Dat...· 0 citations
A systematic literature review of ML applications in credit risk assessment (CRA), covering publications from January 2016 to May 2026, synthesise prevailing methodologies into a unified end-to-end credit risk modelling framework spanning data preprocessing, feature engineering, model training, evaluation, and operatio...
Bo-Lun Zhang, Jun Luo, Ruobing Wu et al.· Journal of Risk and Financia...· 1 citation
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