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Hui Yan

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6 papers

2 author rows

EAAI Journal 2026 Journal Article

Causal-stabilized latent regression for robust industrial soft sensors

Kepeng Qiu
Baowei Rong
Yi Zhu
Yu Liu
Hui Yan
Haijun Cao
Weiwei Wang

Soft sensors play an essential role in monitoring and optimizing industrial processes. However, data redundancy and changing operating conditions often limit their reliability and ability to work well in different situations. This paper presents causal-stabilized latent regression (CSLR), a novel framework that combines causal inference with adaptive weighting. This combination helps balance feature importance and makes the model more stable when conditions change. The framework has two main parts working together: First, the causal balance weight optimization module reduces redundancy and aligns data distributions by optimizing both sample and feature weights through a causal weighting approach. Second, the robust weighted partial least squares (PLS) modeling module uses these weights to build a regression model that focuses on causally important features while reducing multicollinearity problems. Our analysis shows that CSLR effectively reduces feature redundancy and improves both generalization and stability when process conditions change. Tests on industrial debutanizer and fermentation processes demonstrate that CSLR achieves significant improvements over existing methods, with prediction error reduced by up to 43. 5%, R 2 increased by 8. 88%, and mean absolute percentage error decreased by 60. 62%, confirming its effectiveness for building accurate and reliable soft sensors.

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AAAI Conference 2025 Conference Paper

Designing Specialized Two-Dimensional Graph Spectral Filters for Spatial-Temporal Graph Modeling

Yuxin Chen
Fangru Lin
Jingyi Huo
Hui Yan

Spatial-temporal graph modeling is challenging due to the diverse node interactions across spatial and temporal dimensions. Recent studies typically adopt Graph Neural Networks (GNNs) to perform node-level aggregation at different time steps, acting as a series of low-pass graph spectral filters, for node interaction modeling. However, these filters, confined to the spatial dimension, are ill-suited for processing signals of nodes with inherent spatial-temporal interdependencies. Moreover, oversimplified low-pass filtering fails to fully exploit information from diverse node interactions. To address these issues, we propose a Spatial-Temporal Spectral Graph Neural Network (STSGNN), which designs specialized two-dimensional (2-D) graph spectral filters for comprehensive spatial-temporal graph modeling. First, based on the normalized Laplacian spectrum of spatial and temporal graphs, we extend the existing graph spectral theory from a univariate spatial dimension to a bivariate spatial-temporal dimension through a 2-D Discrete Graph Fourier Transform (2-D DGFT). Then, we leverage the bivariate Bernstein polynomial approximation, with learned basis coefficients, to design 2-D filters with specialized spectral properties for unified spatial-temporal signal filtering. Finally, the filtered signals, with refined spatial-temporal representations, are fed into well-designed pyramidal gated convolution modules to acquire multiple ranges of spatial-temporal dependencies. Experiments on traffic and meteorological prediction tasks demonstrate that STSGNN achieves state-of-the-art performance. Additionally, we visualize the 2-D filters learned from inputs with distinct spatial-temporal characteristics to enhance the model's interpretability.

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ICML Conference 2025 Conference Paper

GrokFormer: Graph Fourier Kolmogorov-Arnold Transformers

GuoguoAi
Guansong Pang
Hezhe Qiao
Yuan Gao
Hui Yan

Graph Transformers (GTs) have demonstrated remarkable performance in graph representation learning over popular graph neural networks (GNNs). However, self-attention, the core module of GTs, preserves only low-frequency signals in graph features, leading to ineffectiveness in capturing other important signals like high-frequency ones. Some recent GT models help alleviate this issue, but their flexibility and expressiveness are still limited since the filters they learn are fixed on predefined graph spectrum or spectral order. To tackle this challenge, we propose a Graph Fourier Kolmogorov-Arnold Transformer (GrokFormer), a novel GT model that learns highly expressive spectral filters with adaptive graph spectrum and spectral order through a Fourier series modeling over learnable activation functions. We demonstrate theoretically and empirically that the proposed GrokFormer filter offers better expressiveness than other spectral methods. Comprehensive experiments on 10 real-world node classification datasets across various domains, scales, and graph properties, as well as 5 graph classification datasets, show that GrokFormer outperforms state-of-the-art GTs and GNNs. Our code is available at https: //github. com/GGA23/GrokFormer.

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NeurIPS Conference 2025 Conference Paper

One for All: Universal Topological Primitive Transfer for Graph Structure Learning

Yide Qiu
Tong Zhang
Xing Cai
Hui Yan
Zhen Cui

The non-Euclidean geometry inherent in graph structures fundamentally impedes cross-graph knowledge transfer. Drawing inspiration from texture transfer in computer vision, we pioneer topological primitives as transferable semantic units for graph structural knowledge. To address three critical barriers - the absence of specialized benchmarks, aligned semantic representations, and systematic transfer methodologies - we present G²SN-Transfer, a unified framework comprising: (i) TopoGraph-Mapping that transforms non-Euclidean graphs into transferable sequences via topological primitive distribution dictionaries; (ii) G²SN, a dual-stream architecture learning text-topology aligned representations through contrastive alignment; and (iii) AdaCross-Transfer, a data-adaptive knowledge transfer mechanism leveraging cross-attention for both full-parameter and parameter-frozen scenarios. Particularly, G²SN is a dual-stream sequence network driven by ordinary differential equations, and our theoretical analysis establishes the convergence guarantee of G²SN. We construct STA-18, the first large-scale benchmark with aligned topological primitive-text pairs across 18 diverse graph datasets. Comprehensive evaluations demonstrate that G²SN achieves state-of-the-art performance on four structural learning tasks (average 3. 2\% F1-score improvement), while our transfer method yields consistent enhancements across 13 downstream tasks (5. 2\% average gains) including 10 large-scale graph datasets. The datasets and code are available at https: //anonymous. 4open. science/r/UGSKT-C10E/.

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NeurIPS Conference 2025 Conference Paper

Semi-supervised Graph Anomaly Detection via Robust Homophily Learning

GUOGUO AI
Hezhe Qiao
Hui Yan
Guansong Pang

Current semi-supervised graph anomaly detection (GAD) methods utilizes a small set of labeled normal nodes to identify abnormal nodes from a large set of unlabeled nodes in a graph. These methods posit that 1) normal nodes share a similar level of homophily and 2) the labeled normal nodes can well represent the homophily patterns in the entire normal class. However, this assumption often does not hold well since normal nodes in a graph can exhibit diverse homophily in real-world GAD datasets. In this paper, we propose RHO, namely Robust Homophily Learning, to adaptively learn such homophily patterns. RHO consists of two novel modules, adaptive frequency response filters (AdaFreq) and graph normality alignment (GNA). AdaFreq learns a set of adaptive spectral filters that capture different frequency components of the labeled normal nodes with varying homophily in the channel-wise and cross-channel views of node attributes. GNA is introduced to enforce consistency between the channel-wise and cross-channel homophily representations to robustify the normality learned by the filters in the two views. Experiments on eight real-world GAD datasets show that RHO can effectively learn varying, often under-represented, homophily in the small labeled node set and substantially outperforms state-of-the-art competing methods. Code is available at \url{https: //github. com/mala-lab/RHO}.

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JBHI Journal 2018 Journal Article

Aligning Event Logs to Task-Time Matrix Clinical Pathways in BPMN for Variance Analysis

Hui Yan
Pieter Van Gorp
Uzay Kaymak
Xudong Lu
Lei Ji
Choo Chiap Chiau
Hendrikus H. M. Korsten
Huilong Duan

Clinical pathways (CPs) are popular healthcare management tools to standardize care and ensure quality. Analyzing CP compliance levels and variances is known to be useful for training and CP redesign purposes. Flexible semantics of the business process model and notation (BPMN) language has been shown to be useful for the modeling and analysis of complex protocols. However, in practical cases one may want to exploit that CPs often have the form of task-time matrices. This paper presents a new method parsing complex BPMN models and aligning traces to the models heuristically. A case study on variance analysis is undertaken, where a CP from the practice and two large sets of patients data from an electronic medical record (EMR) database are used. The results demonstrate that automated variance analysis between BPMN task-time models and real-life EMR data are feasible, whereas that was not the case for the existing analysis techniques. We also provide meaningful insights for further improvement.

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