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学者姓名：檀彦超
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Abstract ：
Recently, heterogeneous graphs have attracted widespread attention as a powerful and practical superclass of traditional homogeneous graphs, which reflect the multi-type node entities and edge relations in the real world. Most existing methods adopt meta-path construction as the mainstream to learn long-range heterogeneous semantic messages between nodes. However, such schema constructs the node-wise correlation by connecting nodes via pre-computed fixed paths, which neglects the diversities of meta-paths on the path type and path range. In this paper, we propose a meta-path-based semantic embedding schema, which is called Heterogeneous Graph Embedding with Dual Edge Differentiation (HGE-DED) to adequately construct flexible meta-path combinations thus learning the rich and discriminative semantic of target nodes. Concretely, HGEDED devises a Multi-Type and multi-Range Meta-Path Construction (MTR-MP Construction), which covers the comprehensive exploration of meta-path combinations from path type and path range, expressing the diversity of edges at more fine-grained scales. Moreover, HGE-DED designs the semantics and meta-path joint guidance, constructing a hierarchical short- and long-range relation adjustment, which constrains the path learning as well as minimizes the impact of edge heterophily on heterogeneous graphs. Experimental results on four benchmark datasets demonstrate the effectiveness of HGE-DED compared with state-of-the-art methods.
Keyword ：
Graph neural network Graph neural network Heterogeneous information network Heterogeneous information network Meta-path combination Meta-path combination Semantic embedding Semantic embedding Semi-supervised classification Semi-supervised classification
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| GB/T 7714 | Chen, Yuhong , Chen, Fuhai , Wu, Zhihao et al. Heterogeneous Graph Embedding with Dual Edge Differentiation [J]. | NEURAL NETWORKS , 2025 , 183 . |
| MLA | Chen, Yuhong et al. "Heterogeneous Graph Embedding with Dual Edge Differentiation" . | NEURAL NETWORKS 183 (2025) . |
| APA | Chen, Yuhong , Chen, Fuhai , Wu, Zhihao , Chen, Zhaoliang , Cai, Zhiling , Tan, Yanchao et al. Heterogeneous Graph Embedding with Dual Edge Differentiation . | NEURAL NETWORKS , 2025 , 183 . |
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Multi-view learning methods leverage multiple data sources to enhance perception by mining correlations across views, typically relying on predefined categories. However, deploying these models in real-world scenarios presents two primary openness challenges. 1) Lack of Interpretability: The integration mechanisms of multi-view data in existing black-box models remain poorly explained; 2) Insufficient Generalization: Most models are not adapted to multi-view scenarios involving unknown categories. To address these challenges, we propose OpenViewer, an openness-aware multi-view learning framework with theoretical support. This framework begins with a Pseudo-Unknown Sample Generation Mechanism to efficiently simulate open multi-view environments and previously adapt to potential unknown samples. Subsequently, we introduce an Expression-Enhanced Deep Unfolding Network to intuitively promote interpretability by systematically constructing functional prior-mapping modules and effectively providing a more transparent integration mechanism for multi-view data. Additionally, we establish a Perception-Augmented Open-Set Training Regime to significantly enhance generalization by precisely boosting confidences for known categories and carefully suppressing inappropriate confidences for unknown ones. Experimental results demonstrate that OpenViewer effectively addresses openness challenges while ensuring recognition performance for both known and unknown samples. Copyright © 2025, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
Keyword ：
Deep learning Deep learning Multi-task learning Multi-task learning
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| GB/T 7714 | Du, Shide , Fang, Zihan , Tan, Yanchao et al. OpenViewer: Openness-Aware Multi-View Learning [C] . 2025 : 16389-16397 . |
| MLA | Du, Shide et al. "OpenViewer: Openness-Aware Multi-View Learning" . (2025) : 16389-16397 . |
| APA | Du, Shide , Fang, Zihan , Tan, Yanchao , Wang, Changwei , Wang, Shiping , Guo, Wenzhong . OpenViewer: Openness-Aware Multi-View Learning . (2025) : 16389-16397 . |
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Multi-view learning is an emerging field of multi-modal fusion, which involves representing a single instance using multiple heterogeneous features to improve compatibility prediction. However, existing graph-based multi-view learning approaches are implemented on homogeneous assumptions and pairwise relationships, which may not adequately capture the complex interactions among real-world instances. In this paper, we design a compressed hypergraph neural network from the perspective of multi-view heterogeneous graph learning. This approach effectively captures rich multi-view heterogeneous semantic information, incorporating a hypergraph structure that simultaneously enables the exploration of higher-order correlations between samples in multi-view scenarios. Specifically, we introduce efficient hypergraph convolutional networks based on an explainable regularizer-centered optimization framework. Additionally, a low-rank approximation is adopted as hypergraphs to reformat the initial complex multi-view heterogeneous graph. Extensive experiments compared with several advanced node classification methods and multi-view classification methods have demonstrated the feasibility and effectiveness of the proposed method.
Keyword ：
Graph neural network Graph neural network Heterogeneous graph Heterogeneous graph Hypergraph convolution Hypergraph convolution Multi-view learning Multi-view learning
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| GB/T 7714 | Huang, Aiping , Fang, Zihan , Wu, Zhihao et al. Multi-view heterogeneous graph learning with compressed hypergraph neural networks [J]. | NEURAL NETWORKS , 2024 , 179 . |
| MLA | Huang, Aiping et al. "Multi-view heterogeneous graph learning with compressed hypergraph neural networks" . | NEURAL NETWORKS 179 (2024) . |
| APA | Huang, Aiping , Fang, Zihan , Wu, Zhihao , Tan, Yanchao , Han, Peng , Wang, Shiping et al. Multi-view heterogeneous graph learning with compressed hypergraph neural networks . | NEURAL NETWORKS , 2024 , 179 . |
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Existing representation learning approaches lie predominantly in designing models empirically without rigorous mathematical guidelines, neglecting interpretation in terms of modeling. In this work, we propose an optimization-derived representation learning network that embraces both interpretation and extensibility. To ensure interpretability at the design level, we adopt a transparent approach in customizing the representation learning network from an optimization perspective. This involves modularly stitching together components to meet specific requirements, enhancing flexibility and generality. Then, we convert the iterative solution of the convex optimization objective into the corresponding feed-forward network layers by embedding learnable modules. These above optimization-derived layers are seamlessly integrated into a deep neural network architecture, allowing for training in an end-to-end fashion. Furthermore, extra view-wise weights are introduced for multi-view learning to discriminate the contributions of representations from different views. The proposed method outperforms several advanced approaches on semi-supervised classification tasks, demonstrating its feasibility and effectiveness.
Keyword ：
Feature extraction Feature extraction Guidelines Guidelines Linear programming Linear programming Multi-view learning Multi-view learning Optimization Optimization optimization-derived network optimization-derived network representation learning representation learning Representation learning Representation learning Task analysis Task analysis Training Training
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| GB/T 7714 | Fang, Zihan , Du, Shide , Cai, Zhiling et al. Representation Learning Meets Optimization-Derived Networks: From Single-View to Multi-View [J]. | IEEE TRANSACTIONS ON MULTIMEDIA , 2024 , 26 : 8889-8901 . |
| MLA | Fang, Zihan et al. "Representation Learning Meets Optimization-Derived Networks: From Single-View to Multi-View" . | IEEE TRANSACTIONS ON MULTIMEDIA 26 (2024) : 8889-8901 . |
| APA | Fang, Zihan , Du, Shide , Cai, Zhiling , Lan, Shiyang , Wu, Chunming , Tan, Yanchao et al. Representation Learning Meets Optimization-Derived Networks: From Single-View to Multi-View . | IEEE TRANSACTIONS ON MULTIMEDIA , 2024 , 26 , 8889-8901 . |
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With the rapid development of Internet and Web techniques, Cross-Domain Recommendation (CDR) models have been widely explored for resolving the data-sparsity and cold-start problem. Meanwhile, most CDR models should utilize explicit domain-shareable information (e.g., overlapped users or items) for knowledge transfer across domains. However, this assumption may not be always satisfied since users and items are always non-overlapped in real practice. The performance of many previous works will be severely impaired when these domain-shareable information are not available. To address the aforementioned issues, we propose the Joint Preference Exploration and Dynamic Embedding Transportation model (JPEDET) in this paper which is a novel framework for solving the CDR problem when users and items are non-overlapped. JPEDET includes two main modules, i.e., joint preference exploration module and dynamic embedding transportation module. The joint preference exploration module aims to fuse rating and review information for modelling user preferences. The dynamic embedding transportation module is set to share knowledge via neural ordinary equations for dual transformation across domains. Moreover, we innovatively propose the dynamic transport flow equipped with linear interpolation guidance on barycentric Wasserstein path for achieving accurate and bidirectional transformation. Our empirical study on Amazon datasets demonstrates that JPEDET outperforms the state-of-the-art models under the CDR setting. Copyright © 2024, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
Keyword ：
Artificial intelligence Artificial intelligence Clock and data recovery circuits (CDR circuits) Clock and data recovery circuits (CDR circuits) Embeddings Embeddings Information dissemination Information dissemination Knowledge management Knowledge management Linear transformations Linear transformations User profile User profile
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| GB/T 7714 | Liu, Weiming , Chen, Chaochao , Liao, Xinting et al. Learning Accurate and Bidirectional Transformation via Dynamic Embedding Transportation for Cross-Domain Recommendation [C] . 2024 : 8815-8823 . |
| MLA | Liu, Weiming et al. "Learning Accurate and Bidirectional Transformation via Dynamic Embedding Transportation for Cross-Domain Recommendation" . (2024) : 8815-8823 . |
| APA | Liu, Weiming , Chen, Chaochao , Liao, Xinting , Hu, Mengling , Tan, Yanchao , Wang, Fan et al. Learning Accurate and Bidirectional Transformation via Dynamic Embedding Transportation for Cross-Domain Recommendation . (2024) : 8815-8823 . |
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Recently, dual-target cross-domain recommendation (DTCDR) has been proposed to alleviate the data sparsity problem by sharing the common knowledge across domains simultaneously.However, existing methods often assume that personal data containing abundant identifiable information can be directly accessed, which results in a controversial privacy leakage problem of DTCDR.To this end, we introduce the P2DTR framework, a novel approach in DTCDR while protecting private user information.Specifically, we first design a novel inter-client knowledge extraction mechanism, which exploits the private set intersection algorithm and prototype-based federated learning to enable collaboratively modeling among multiple users and a server.Furthermore, to improve the recommendation performance based on the extracted common knowledge across domains, we proposed an intra-client enhanced recommendation, consisting of a constrained dominant set (CDS) propagation mechanism and dual-recommendation module.Extensive experiments on real-world datasets validate that our proposed P2DTR framework achieves superior utility under a privacy-preserving guarantee on both domains. © 2024 International Joint Conferences on Artificial Intelligence. All rights reserved.
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| GB/T 7714 | Lin, Zhenghong , Huang, Wei , Zhang, Hengyu et al. Enhancing Dual-Target Cross-Domain Recommendation with Federated Privacy-Preserving Learning [C] . 2024 : 2153-2161 . |
| MLA | Lin, Zhenghong et al. "Enhancing Dual-Target Cross-Domain Recommendation with Federated Privacy-Preserving Learning" . (2024) : 2153-2161 . |
| APA | Lin, Zhenghong , Huang, Wei , Zhang, Hengyu , Xu, Jiayu , Liu, Weiming , Liao, Xinting et al. Enhancing Dual-Target Cross-Domain Recommendation with Federated Privacy-Preserving Learning . (2024) : 2153-2161 . |
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Cross-Domain Recommendation (CDR) have become increasingly appealing by leveraging useful information to tackle the data sparsity problem across domains. Most of latest CDR models assume that domain-shareable user-item information (e.g., rating and review on overlapped users or items) are accessible across domains. However, these assumptions become impractical due to the strict data privacy protection policy. In this paper, we propose Reducing Item Discrepancy (RidCDR) model on solving Privacy-Preserving Cross-Domain Recommendation (PPCDR) problem. Specifically, we aim to enhance the model performance on both source and target domains without overlapped users and items while protecting the data privacy. We innovatively propose private-robust embedding alignment module in RidCDR for knowledge sharing across domains while avoiding negative transfer privately. Our empirical study on Amazon and Douban datasets demonstrates that RidCDR significantly outperforms the state-of-the-art models under the PPCDR without overlapped users and items. Copyright 2024 by the author(s)
Keyword ：
Differential privacy Differential privacy Privacy-preserving techniques Privacy-preserving techniques
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| GB/T 7714 | Liu, Weiming , Zheng, Xiaolin , Chen, Chaochao et al. Reducing Item Discrepancy via Differentially Private Robust Embedding Alignment for Privacy-Preserving Cross Domain Recommendation [C] . 2024 : 32455-32470 . |
| MLA | Liu, Weiming et al. "Reducing Item Discrepancy via Differentially Private Robust Embedding Alignment for Privacy-Preserving Cross Domain Recommendation" . (2024) : 32455-32470 . |
| APA | Liu, Weiming , Zheng, Xiaolin , Chen, Chaochao , Xu, Jiahe , Liao, Xinting , Wang, Fan et al. Reducing Item Discrepancy via Differentially Private Robust Embedding Alignment for Privacy-Preserving Cross Domain Recommendation . (2024) : 32455-32470 . |
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Progressive diagnosis prediction in healthcare is a promising yet challenging task. Existing studies usually assume a pre-defined prior for generating patient distributions (e.g., Gaussian). However, the inferred approximate posterior can deviate from the real-world distribution, which further affects the modeling of continuous disease progression over time. To alleviate such inference bias, we propose an enhanced progressive diagnostic prediction model (i.e., ProCNF), which integrates continuous normalizing flows (CNF) and neural ordinary differential equations (ODEs) to achieve more accurate approximations of patient health trajectories while capturing the continuity underlying disease progression. We first learn patient embeddings with CNF to construct a complex posterior approximation of patient distributions. Then, we devise a CNF-enhanced neural ODE module for progressive diagnostic prediction, which aims to improve the modeling of disease progression for individual patients. Extensive experiments on two real-world longitudinal EHR datasets show significant performance gains brought by our method over state-of-the-art competitors. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.
Keyword ：
Diagnosis Diagnosis Forecasting Forecasting Health care Health care Knowledge management Knowledge management Ordinary differential equations Ordinary differential equations
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| GB/T 7714 | Tan, Yanchao , Zhang, Hengyu , Zhou, Zihao et al. Enhancing Progressive Diagnosis Prediction in Healthcare with Continuous Normalizing Flows [C] . 2024 : 1166-1169 . |
| MLA | Tan, Yanchao et al. "Enhancing Progressive Diagnosis Prediction in Healthcare with Continuous Normalizing Flows" . (2024) : 1166-1169 . |
| APA | Tan, Yanchao , Zhang, Hengyu , Zhou, Zihao , Ma, Guofang , Wang, Fan , Liu, Weiming et al. Enhancing Progressive Diagnosis Prediction in Healthcare with Continuous Normalizing Flows . (2024) : 1166-1169 . |
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User cold-start recommendation aims to provide accurate items for the newly joint users and is a hot and challenging problem. Nowadays as people participant in different domains, how to recommend items in the new domain for users in an old domain has become more urgent. In this paper, we focus on the Dual Cold-Start Cross Domain Recommendation (Dual-CSCDR) problem. That is, providing the most relevant items for new users on the source and target domains. The prime task in Dual-CSCDR is to properly model user-item rating interactions and map user expressive embeddings across domains. However, previous approaches cannot solve Dual-CSCDR well, since they separate the collaborative filtering and distribution mapping process, leading to the error superimposition issue. Moreover, most of these methods fail to fully exploit the cross-domain relationship among large number of non-overlapped users, which strongly limits their performance. To fill this gap, we propose User Distribution Mapping model with Collaborative Filtering (UDMCF), a novel end-to-end cold-start cross-domain recommendation framework for the Dual-CSCDR problem. UDMCF includes two main modules, i.e., rating prediction module and distribution alignment module. The former module adopts one-hot ID vectors and multi-hot historical ratings for collaborative filtering via a contrastive loss. The latter module contains overlapped user embedding alignment and general user subgroup distribution alignment. Specifically, we innovatively propose unbalance distribution optimal transport with typical subgroup discovering algorithm to map the whole user distributions. Our empirical study on several datasets demonstrates that UDMCF significantly outperforms the state-of-the-art models under the Dual-CSCDR setting. © 2024 ACM.
Keyword ：
Alignment Alignment Collaborative filtering Collaborative filtering Embeddings Embeddings Mapping Mapping User profile User profile
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| GB/T 7714 | Liu, Weiming , Chen, Chaochao , Liao, Xinting et al. User Distribution Mapping Modelling with Collaborative Filtering for Cross Domain Recommendation [C] . 2024 : 334-343 . |
| MLA | Liu, Weiming et al. "User Distribution Mapping Modelling with Collaborative Filtering for Cross Domain Recommendation" . (2024) : 334-343 . |
| APA | Liu, Weiming , Chen, Chaochao , Liao, Xinting , Hu, Mengling , Su, Jiajie , Tan, Yanchao et al. User Distribution Mapping Modelling with Collaborative Filtering for Cross Domain Recommendation . (2024) : 334-343 . |
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Diagnosis prediction is becoming crucial to develop healthcare plans for patients based on Electronic Health Records (EHRs). Existing works usually enhance diagnosis prediction via learning accurate disease representation, where many of them try to capture inclusive relations based on the hierarchical structures of existing disease ontologies such as those provided by ICD-9 codes. However, they overlook exclusive relations that can reflect different and complementary perspectives of the ICD-9 structures, and thus fail to accurately represent relations among diseases and ICD-9 codes. To this end, we propose to project disease embeddings and ICD-9 code embeddings into boxes, where a box is an axis-aligned hyperrectangle with a geometric region and two boxes can clearly 'include' or 'exclude' each other. Upon box embeddings, we further obtain patient embeddings via aggregating the disease representations for diagnosis prediction. Extensive experiments on two real-world EHR datasets show significant performance gains brought by our proposed framework, yielding average improvements of 6.04% for diagnosis prediction over state-of-the-art competitors. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.
Keyword ：
Codes (symbols) Codes (symbols) Diagnosis Diagnosis Embeddings Embeddings Forecasting Forecasting Health care Health care
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| GB/T 7714 | Lv, Hang , Chen, Zehai , Yang, Yacong et al. BoxCare: A Box Embedding Model for Disease Representation and Diagnosis Prediction in Healthcare Data [C] . 2024 : 1130-1133 . |
| MLA | Lv, Hang et al. "BoxCare: A Box Embedding Model for Disease Representation and Diagnosis Prediction in Healthcare Data" . (2024) : 1130-1133 . |
| APA | Lv, Hang , Chen, Zehai , Yang, Yacong , Ma, Guofang , Tan, Yanchao , Yang, Carl . BoxCare: A Box Embedding Model for Disease Representation and Diagnosis Prediction in Healthcare Data . (2024) : 1130-1133 . |
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