Existing cold-start recommendation methods typically use item-level alignment strategies to align the content feature and collaborative feature of warm items during model training. However, these methods are less effective for cold items with low semantic similarity to the warm items when they first appear in the test stage, as they have no historical interactions to obtain the collaborative feature. In this paper, we propose a preference aware recommendation (PARec) model with hierarchical item alignment to solve the item cold-start issue. Our approach exploits user preference from historical records to achieve group-level alignment with item content feature, enhancing recommendation performance. Specifically, our hierarchical item alignment strategy improves recommendations for both high and low similarity cold items by using item-level alignment for high similarity cold items and introducing group-level alignment for low similarity cold items. Low similarity cold items can be successfully recommended through relationships among items, captured by our group-level alignment, based on their co-occurrence possibilities and semantic similarities. For model training, a hierarchical contrastive objective function is presented to balance the performance of warm and cold items, achieving better overall performance. Extensive experiments demonstrate the effectiveness of our method, with results showing its superiority compared to state-of-the-art approaches.
{"title":"Preference Aware Item Cold-Start Recommendation With Hierarchical Item Alignment","authors":"Wenbo Wang;Ben Chen;Bingquan Liu;Lili Shan;Chengjie Sun;Qian Chen;Feiyang Xiao;Jian Guan","doi":"10.1109/TKDE.2025.3613263","DOIUrl":"https://doi.org/10.1109/TKDE.2025.3613263","url":null,"abstract":"Existing cold-start recommendation methods typically use item-level alignment strategies to align the content feature and collaborative feature of warm items during model training. However, these methods are less effective for cold items with low semantic similarity to the warm items when they first appear in the test stage, as they have no historical interactions to obtain the collaborative feature. In this paper, we propose a preference aware recommendation (PARec) model with hierarchical item alignment to solve the item cold-start issue. Our approach exploits user preference from historical records to achieve group-level alignment with item content feature, enhancing recommendation performance. Specifically, our hierarchical item alignment strategy improves recommendations for both high and low similarity cold items by using item-level alignment for high similarity cold items and introducing group-level alignment for low similarity cold items. Low similarity cold items can be successfully recommended through relationships among items, captured by our group-level alignment, based on their co-occurrence possibilities and semantic similarities. For model training, a hierarchical contrastive objective function is presented to balance the performance of warm and cold items, achieving better overall performance. Extensive experiments demonstrate the effectiveness of our method, with results showing its superiority compared to state-of-the-art approaches.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 12","pages":"7388-7401"},"PeriodicalIF":10.4,"publicationDate":"2025-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145456052","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Visual Question Answering (VQA), aimed at improving AI-driven interactions and solving complex visual-linguistic tasks, has increasingly garnered attention as a pivotal research domain in both academic and industrial spheres. Despite progress in VQA, current studies still suffer from the challenge of language bias posed by spurious semantic correlations and minority class collapse, leading to semantic ambiguities and distribution shifts that hinder robust performance across challenging scenarios. To address these challenges, we propose a robust multi-space collaborative debiasing paradigm, termed “LBF-VQA”, which systematically leverages multi-space collaborative debiasing strategies to achieve language bias-free VQA, encompassing both Euclidean space debiasing (ESD) and Spherical space debiasing (SSD). By strategically introducing bias-examples and their corresponding counter-examples, the ESD strategy focuses on uncovering hidden prior correlations and the complex interactions between modality and semantics within the Euclidean space. Benefiting from the infinite contrastive and distribution debiasing learning mechanisms, the SSD strategy is devoted to effectively preventing the collapse of minority classes while enhancing the manifold representations of instance de-bias and distribution de-dependence in the Spherical space. Furthermore, we meticulously constructed a specialized medical dataset intentionally embedded with deliberate language bias to comprehensively examine the negative effects of language bias on medical VQA systems. Extensive experiments on multiple general and medical VQA benchmarks consistently verify the effectiveness and generalizability of our LBF-VQA in handling various complex VQA scenarios than state-of-the-art baselines.
{"title":"LBF-VQA: Towards Language Bias-Free Visual Question Answering With Multi-Space Collaborative Debiasing","authors":"Yishu Liu;Huanjia Zhu;Bingzhi Chen;Xiaozhao Fang;Guangming Lu;Shengli Xie","doi":"10.1109/TKDE.2025.3613421","DOIUrl":"https://doi.org/10.1109/TKDE.2025.3613421","url":null,"abstract":"Visual Question Answering (VQA), aimed at improving AI-driven interactions and solving complex visual-linguistic tasks, has increasingly garnered attention as a pivotal research domain in both academic and industrial spheres. Despite progress in VQA, current studies still suffer from the challenge of <italic>language bias</i> posed by spurious semantic correlations and minority class collapse, leading to semantic ambiguities and distribution shifts that hinder robust performance across challenging scenarios. To address these challenges, we propose a robust multi-space collaborative debiasing paradigm, termed “LBF-VQA”, which systematically leverages multi-space collaborative debiasing strategies to achieve language bias-free VQA, encompassing both Euclidean space debiasing (ESD) and Spherical space debiasing (SSD). By strategically introducing bias-examples and their corresponding counter-examples, the ESD strategy focuses on uncovering hidden prior correlations and the complex interactions between modality and semantics within the Euclidean space. Benefiting from the infinite contrastive and distribution debiasing learning mechanisms, the SSD strategy is devoted to effectively preventing the collapse of minority classes while enhancing the manifold representations of instance de-bias and distribution de-dependence in the Spherical space. Furthermore, we meticulously constructed a specialized medical dataset intentionally embedded with deliberate language bias to comprehensively examine the negative effects of language bias on medical VQA systems. Extensive experiments on multiple general and medical VQA benchmarks consistently verify the effectiveness and generalizability of our LBF-VQA in handling various complex VQA scenarios than state-of-the-art baselines.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 12","pages":"7255-7271"},"PeriodicalIF":10.4,"publicationDate":"2025-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145455968","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-22DOI: 10.1109/TKDE.2025.3607765
Yaming Yang;Zhe Wang;Ziyu Guan;Wei Zhao;Xinyan Huang;Xiaofei He
Entity Alignment (EA) is to link potential equivalent entities across different knowledge graphs (KGs). Most existing EA methods are supervised as they require the supervision of seed alignments, i.e., manually specified aligned entity pairs. Very recently, several EA studies have made some attempts to get rid of seed alignments. Despite achieving preliminary progress, they still suffer two limitations: (1) The entity embeddings produced by their GNN-like encoders lack personalization since some of the aggregation subpaths are shared between different entities. (2) They cannot fully alleviate the distribution distortion issue between candidate KGs due to the absence of supervised signals. In this work, we propose a novel unsupervised entity alignment approach called UNEA to address the above two issues. First, we parametrically sample a tree neighborhood rooted at each entity, and accordingly develop a tree attention aggregation mechanism to extract a personalized embedding for each entity. Second, we introduce an auxiliary task of maximizing the mutual information between the input and the output of the KG encoder, which serves as a regularization to prevent the distribution distortion. Extensive experiments show that our UNEA achieves a new state-of-the-art for the unsupervised EA task, and can even outperform many existing supervised EA baselines.
{"title":"Unsupervised Entity Alignment Based on Personalized Discriminative Rooted Tree","authors":"Yaming Yang;Zhe Wang;Ziyu Guan;Wei Zhao;Xinyan Huang;Xiaofei He","doi":"10.1109/TKDE.2025.3607765","DOIUrl":"https://doi.org/10.1109/TKDE.2025.3607765","url":null,"abstract":"Entity Alignment (EA) is to link potential equivalent entities across different knowledge graphs (KGs). Most existing EA methods are supervised as they require the supervision of seed alignments, i.e., manually specified aligned entity pairs. Very recently, several EA studies have made some attempts to get rid of seed alignments. Despite achieving preliminary progress, they still suffer two limitations: (1) The entity embeddings produced by their GNN-like encoders lack personalization since some of the aggregation subpaths are shared between different entities. (2) They cannot fully alleviate the distribution distortion issue between candidate KGs due to the absence of supervised signals. In this work, we propose a novel unsupervised entity alignment approach called UNEA to address the above two issues. First, we parametrically sample a tree neighborhood rooted at each entity, and accordingly develop a tree attention aggregation mechanism to extract a personalized embedding for each entity. Second, we introduce an auxiliary task of maximizing the mutual information between the input and the output of the KG encoder, which serves as a regularization to prevent the distribution distortion. Extensive experiments show that our UNEA achieves a new state-of-the-art for the unsupervised EA task, and can even outperform many existing supervised EA baselines.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 12","pages":"7440-7452"},"PeriodicalIF":10.4,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145455963","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-22DOI: 10.1109/TKDE.2025.3613148
Hao Huang;Mingxin Wang;Mengqi Shan;Zhigao Zheng;Ting Gan;Jiawei Jiang;Zongpeng Li
Outdoor billboard advertising has proven effective for commercial promotions, attracting potential customers, and boosting product sales. Auction serves as a popular method for leasing billboard usage rights, enabling a seller to rent billboards to winning users for predefined periods according to their bids. An effective auction algorithm is of great significance to maximize the efficiency of the billboard ecosystem. In contrast to a rich literature on Internet advertising auctions, well-crafted algorithms tailored for outdoor billboard auctions remain rare. In this work, we investigate the problem of outdoor billboard auctions, in the practical setting where bids are received and processed on the fly. Our goal is to maximize social welfare, namely the total benefits of auction participants, including the billboard service provider and the bidding users. To this end, we first formulate the billboard social welfare maximization problem into an Integer Linear Problem (ILP), and then reformulate the ILP into a compact form with a reduced size of constraints (at the cost of involving exponentially many primal variables), based on which we derive the dual problem. Furthermore, we design a dual oracle to handle the exponentially many dual constraints, avoiding exhaustive enumeration. We present a primal-dual online algorithm with an incentive-compatible pricing mechanism. Theoretical analysis proves the individual rationality, incentive compatibility, and computational efficiency of our online algorithm. Extensive experimental results show that the online algorithm is both effective and efficient, and achieves a good competitive ratio.
{"title":"Online Billboard Auction With Social Welfare Maximization","authors":"Hao Huang;Mingxin Wang;Mengqi Shan;Zhigao Zheng;Ting Gan;Jiawei Jiang;Zongpeng Li","doi":"10.1109/TKDE.2025.3613148","DOIUrl":"https://doi.org/10.1109/TKDE.2025.3613148","url":null,"abstract":"Outdoor billboard advertising has proven effective for commercial promotions, attracting potential customers, and boosting product sales. Auction serves as a popular method for leasing billboard usage rights, enabling a seller to rent billboards to winning users for predefined periods according to their bids. An effective auction algorithm is of great significance to maximize the efficiency of the billboard ecosystem. In contrast to a rich literature on Internet advertising auctions, well-crafted algorithms tailored for outdoor billboard auctions remain rare. In this work, we investigate the problem of outdoor billboard auctions, in the practical setting where bids are received and processed on the fly. Our goal is to maximize social welfare, namely the total benefits of auction participants, including the billboard service provider and the bidding users. To this end, we first formulate the billboard social welfare maximization problem into an Integer Linear Problem (ILP), and then reformulate the ILP into a compact form with a reduced size of constraints (at the cost of involving exponentially many primal variables), based on which we derive the dual problem. Furthermore, we design a dual oracle to handle the exponentially many dual constraints, avoiding exhaustive enumeration. We present a primal-dual online algorithm with an incentive-compatible pricing mechanism. Theoretical analysis proves the individual rationality, incentive compatibility, and computational efficiency of our online algorithm. Extensive experimental results show that the online algorithm is both effective and efficient, and achieves a good competitive ratio.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 12","pages":"7362-7373"},"PeriodicalIF":10.4,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145456020","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The demand for more precise and timely urban resource allocation and management has driven the extension of urban flow prediction from short-term to long-term horizons. As the time scale expands, the issue of urban flow distribution shift becomes increasingly prominent due to various impact factors, such as weather, events, city changes, etc. Traditionally, comprehensively analyzing and addressing the causal relationships underlying the distribution shift caused by these factors has been challenging. In this paper, we propose that these impact factors can be partitioned in two major types, i.e., context factors and structural factors. We then present a decomposition-based model for long-term urban flow prediction from a causal perspective, named DeCau, which can discriminate between the two types of factors for effectively solving the problem of urban flow distribution shift. First, we employ a decomposition module to decompose urban flow into seasonal part and trend part. The seasonal part contains high frequency irregular variations caused by context factors. We advise a shared distribution estimator to approximate the unavailable prior distributions of context factors, and then apply causal intervention to mitigate the confounding impact of context factors. The distribution shift in the trend part is induced by structural factors. We design a dual causal dependency extractor to model the causality between POIs distribution and urban flow, and then eliminate spurious correlations through causal adjustment. Finally, we design an end-to-end framework for long-term urban flow prediction by combining the embeddings from two parts, enabling the model to generalize to unseen distribution. Extensive experimental results demonstrate DeCau outperforms state-of-the-art baselines.
{"title":"Long-Term Urban Flow Prediction Against Data Distribution Shift: A Causal Perspective","authors":"Yuting Liu;Qiang Zhou;Hanzhe Li;Fuzhen Zhuang;Jingjing Gu","doi":"10.1109/TKDE.2025.3612033","DOIUrl":"https://doi.org/10.1109/TKDE.2025.3612033","url":null,"abstract":"The demand for more precise and timely urban resource allocation and management has driven the extension of urban flow prediction from short-term to long-term horizons. As the time scale expands, the issue of urban flow distribution shift becomes increasingly prominent due to various impact factors, such as weather, events, city changes, etc. Traditionally, comprehensively analyzing and addressing the causal relationships underlying the distribution shift caused by these factors has been challenging. In this paper, we propose that these impact factors can be partitioned in two major types, i.e., context factors and structural factors. We then present a decomposition-based model for long-term urban flow prediction from a causal perspective, named <italic>DeCau</i>, which can discriminate between the two types of factors for effectively solving the problem of urban flow distribution shift. First, we employ a decomposition module to decompose urban flow into seasonal part and trend part. The seasonal part contains high frequency irregular variations caused by context factors. We advise a shared distribution estimator to approximate the unavailable prior distributions of context factors, and then apply causal intervention to mitigate the confounding impact of context factors. The distribution shift in the trend part is induced by structural factors. We design a dual causal dependency extractor to model the causality between POIs distribution and urban flow, and then eliminate spurious correlations through causal adjustment. Finally, we design an end-to-end framework for long-term urban flow prediction by combining the embeddings from two parts, enabling the model to generalize to unseen distribution. Extensive experimental results demonstrate <italic>DeCau</i> outperforms state-of-the-art baselines.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 12","pages":"7286-7299"},"PeriodicalIF":10.4,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145455973","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Bipartite graphs are widely used in many real-world applications, where discovering clusters is crucial for understanding their underlying structure. However, most existing clustering methods for bipartite graphs enforce the assignment of all vertices to clusters, often neglecting the important roles of outliers and hubs. To address this limitation, we plan to extend the structural clustering model from unipartite to bipartite graphs. This extension is non-trivial due to the lack of common neighbors in bipartite graphs, which renders traditional similarity measures less effective. Recognizing that similarity is key to structural clustering, we resort to butterflies—the fundamental building blocks of bipartite graphs—to define a more effective similarity measure. Building on this, we further propose a novel structural clustering model, ${mathsf {SBC}}$, tailored for bipartite graphs. To enable clustering under this model, we develop efficient online and index-based methods, along with a dynamic maintenance method to accommodate graph updates over time. Extensive experiments on real-world bipartite graphs demonstrate that: (1) The ${mathsf {SBC}}$ model greatly enhances clustering quality, achieving higher modularity while effectively identifying outliers and hubs. (2) Our proposed clustering methods are highly scalable, enabling the processing of graphs with up to 12.2 million edges within 2 seconds.
{"title":"Structural Clustering for Bipartite Graphs","authors":"Mingyu Yang;Wentao Li;Wei Wang;Dong Wen;Min Gao;Lu Qin","doi":"10.1109/TKDE.2025.3612290","DOIUrl":"https://doi.org/10.1109/TKDE.2025.3612290","url":null,"abstract":"Bipartite graphs are widely used in many real-world applications, where discovering clusters is crucial for understanding their underlying structure. However, most existing clustering methods for bipartite graphs enforce the assignment of <i>all</i> vertices to clusters, often neglecting the important roles of outliers and hubs. To address this limitation, we plan to extend the structural clustering model from unipartite to bipartite graphs. This extension is non-trivial due to the lack of common neighbors in bipartite graphs, which renders traditional similarity measures less effective. Recognizing that similarity is key to structural clustering, we resort to butterflies—the fundamental building blocks of bipartite graphs—to define a more effective similarity measure. Building on this, we further propose a novel structural clustering model, <inline-formula><tex-math>${mathsf {SBC}}$</tex-math></inline-formula>, tailored for bipartite graphs. To enable clustering under this model, we develop efficient online and index-based methods, along with a dynamic maintenance method to accommodate graph updates over time. Extensive experiments on real-world bipartite graphs demonstrate that: (1) The <inline-formula><tex-math>${mathsf {SBC}}$</tex-math></inline-formula> model greatly enhances clustering quality, achieving higher modularity while effectively identifying outliers and hubs. (2) Our proposed clustering methods are highly scalable, enabling the processing of graphs with up to 12.2 million edges within 2 seconds.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"38 1","pages":"645-658"},"PeriodicalIF":10.4,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145705912","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Recently, large language models (LLMs) have made remarkable progress in table understanding, yet they remain vulnerable to the structural noise (SN) and the textual noise (TN). Existing methods usually employ biased denoising strategies such as structural matching and textual filtering, or overzealous denoising strategies such as introducing supplementary tasks like text-to-SQL and table-to-text to reduce these two types of noise. However, these methods either neglect one type of noise or introduce substantial external noise. Therefore, how to simultaneously mitigate the structural and textual noise without introducing extra noise and improve the performance of LLMs in table understanding is still an unresolved issue. In this paper, we rethink the bottlenecks in table understanding from the perspective of noise reduction and propose a novel dual-denoiser-reasoner model, called TabDDR, for balanced and effective denoising. Specially, our model consists of a structural-and-textual denoiser and a task-adaptive reasoner. The former removes two types of noise via triplet alignment and planning extraction to seek an interpretable balance between breaking structural barriers and preserving structural characteristics, eliminating textual noise and retaining maximal information; the latter ensures a simple but effective reasoning process which can adapt to various downstream tasks. To highlight the presence and impact of the structural and textual noise, we construct the WTQ-SN and WTQ-TN datasets based on the WikiTableQuestion (WTQ) dataset. Extensive experiments on these self-constructed datasets and two other public datasets demonstrate that our proposed method performs better than state-of-the-art baselines.
{"title":"Toward Balanced Denoising: Building a Structural and Textual Denoiser for Table Understanding","authors":"Shu-Xun Yang;Xian-Ling Mao;Yu-Ming Shang;Heyan Huang","doi":"10.1109/TKDE.2025.3612217","DOIUrl":"https://doi.org/10.1109/TKDE.2025.3612217","url":null,"abstract":"Recently, large language models (LLMs) have made remarkable progress in table understanding, yet they remain vulnerable to the structural noise (SN) and the textual noise (TN). Existing methods usually employ biased denoising strategies such as structural matching and textual filtering, or overzealous denoising strategies such as introducing supplementary tasks like text-to-SQL and table-to-text to reduce these two types of noise. However, these methods either neglect one type of noise or introduce substantial external noise. Therefore, how to simultaneously mitigate the structural and textual noise without introducing extra noise and improve the performance of LLMs in table understanding is still an unresolved issue. In this paper, we rethink the bottlenecks in table understanding from the perspective of noise reduction and propose a novel dual-denoiser-reasoner model, called TabDDR, for balanced and effective denoising. Specially, our model consists of a structural-and-textual denoiser and a task-adaptive reasoner. The former removes two types of noise via triplet alignment and planning extraction to seek an interpretable balance between breaking structural barriers and preserving structural characteristics, eliminating textual noise and retaining maximal information; the latter ensures a simple but effective reasoning process which can adapt to various downstream tasks. To highlight the presence and impact of the structural and textual noise, we construct the WTQ-SN and WTQ-TN datasets based on the WikiTableQuestion (WTQ) dataset. Extensive experiments on these self-constructed datasets and two other public datasets demonstrate that our proposed method performs better than state-of-the-art baselines.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 12","pages":"7414-7425"},"PeriodicalIF":10.4,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145456048","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-19DOI: 10.1109/TKDE.2025.3610998
Run-An Wang;Zhaonian Zou;Dandan Liu;Xudong Liu
Community search on multilayer graphs has significant applications in fields such as bioinformatics, social network analysis, and financial fraud detection, offering deeper insights compared to traditional community search on single-layer graphs. However, existing approaches often suffer from several key limitations, including inefficiency and a lack of flexibility in accommodating query requirements. To address these challenges, we investigate the problem of community search over large multilayer graphs. Specifically, we introduce a novel multilayer community model called PivotTruss Community (PiTC) with provably nice structural guarantees. We formalize the PiTC search (PiTCS) problem, which aims to efficiently identify personalized PiTCs for a given query vertex. To solve the PiTCS problem, we propose an efficient algorithm and design an elegant index to accelerate the search process. In addition, we propose a parameter recommendation method to improve the usability of PiTCS. To further optimize performance, we introduce a method to compact the index by making a trade-off between search time and index size. Extensive experiments on real-world datasets demonstrate the effectiveness and efficiency of our proposed algorithms.
{"title":"PiTruss Community Search for Multilayer Graphs","authors":"Run-An Wang;Zhaonian Zou;Dandan Liu;Xudong Liu","doi":"10.1109/TKDE.2025.3610998","DOIUrl":"https://doi.org/10.1109/TKDE.2025.3610998","url":null,"abstract":"Community search on multilayer graphs has significant applications in fields such as bioinformatics, social network analysis, and financial fraud detection, offering deeper insights compared to traditional community search on single-layer graphs. However, existing approaches often suffer from several key limitations, including inefficiency and a lack of flexibility in accommodating query requirements. To address these challenges, we investigate the problem of community search over large multilayer graphs. Specifically, we introduce a novel multilayer community model called <underline>Pi</u>vot<underline>T</u>russ <underline>C</u>ommunity (PiTC) with provably nice structural guarantees. We formalize the PiTC search (PiTCS) problem, which aims to efficiently identify personalized PiTCs for a given query vertex. To solve the PiTCS problem, we propose an efficient algorithm and design an elegant index to accelerate the search process. In addition, we propose a parameter recommendation method to improve the usability of PiTCS. To further optimize performance, we introduce a method to compact the index by making a trade-off between search time and index size. Extensive experiments on real-world datasets demonstrate the effectiveness and efficiency of our proposed algorithms.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 12","pages":"7374-7387"},"PeriodicalIF":10.4,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145455797","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-17DOI: 10.1109/TKDE.2025.3611270
Lei Zhang;Zihao Chen;Wuji Zhang;Hongke Zhao;Likang Wu
Despite advancements using graph neural networks (GNNs) to capture complex user-item interactions, challenges persist due to data sparsity and noise. To address these, self-supervised learning (SSL) methods, particularly recent generative approaches, have gained attention due to their ability to augment graph data without requiring complex view constructions and unstable negative sampling. However, existing generative SSL solutions often focus on structural rather than semantic (refer to collaborative signals in recommendation scenarios) reconstruction, limiting their potential as comprehensive recommender. This paper explores the untapped potential of generative SSL for graph-based recommender systems. We highlight two critical challenges: firstly, designing effective diffusion mechanisms to enhance semantic information and collaborative signals while avoiding optimization biases; and secondly, developing adaptive structural masking mechanisms within graph diffusion to improve overall model performance. Motivated by these challenges, we propose a novel approach: the Guided Diffusion enhanced Mask graph AutoEncoder (GDiffMAE). GDiffMAE integrates an adaptive mask encoder for structural reconstruction and a guided diffusion model for semantic reconstruction, addressing the limitations of current methods. Experimental results on diverse datasets demonstrate that GDiffMAE consistently outperforms powerful baseline models, particularly in handling noisy data scenarios. By enhancing both structural and semantic dimensions through guided diffusion, our model advances the state-of-the-art in graph-based recommender systems.
{"title":"GDiffMAE: Guided Diffusion Enhanced Mask Graph AutoEncoder for Recommendation","authors":"Lei Zhang;Zihao Chen;Wuji Zhang;Hongke Zhao;Likang Wu","doi":"10.1109/TKDE.2025.3611270","DOIUrl":"https://doi.org/10.1109/TKDE.2025.3611270","url":null,"abstract":"Despite advancements using graph neural networks (GNNs) to capture complex user-item interactions, challenges persist due to data sparsity and noise. To address these, self-supervised learning (SSL) methods, particularly recent generative approaches, have gained attention due to their ability to augment graph data without requiring complex view constructions and unstable negative sampling. However, existing generative SSL solutions often focus on structural rather than semantic (refer to collaborative signals in recommendation scenarios) reconstruction, limiting their potential as comprehensive recommender. This paper explores the untapped potential of generative SSL for graph-based recommender systems. We highlight two critical challenges: firstly, designing effective diffusion mechanisms to enhance semantic information and collaborative signals while avoiding optimization biases; and secondly, developing adaptive structural masking mechanisms within graph diffusion to improve overall model performance. Motivated by these challenges, we propose a novel approach: the Guided Diffusion enhanced Mask graph AutoEncoder (GDiffMAE). GDiffMAE integrates an adaptive mask encoder for structural reconstruction and a guided diffusion model for semantic reconstruction, addressing the limitations of current methods. Experimental results on diverse datasets demonstrate that GDiffMAE consistently outperforms powerful baseline models, particularly in handling noisy data scenarios. By enhancing both structural and semantic dimensions through guided diffusion, our model advances the state-of-the-art in graph-based recommender systems.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 12","pages":"7199-7212"},"PeriodicalIF":10.4,"publicationDate":"2025-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145455895","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In the domain of Multi-view Subspace Clustering (MSC) in Latent Embedding Space (LES), existing methods aim to capture and leverage critical multi-view information by mapping it into a low-dimensional LES. However, several aspects can be further improved: (i) Fusion Strategy: Existing methods adopt either early fusion or late fusion to integrate multi-view information, limiting the effectiveness of the fusion. (ii) Diversity: Current methods often overlook the inherent diversity in the multi-view data by focusing on a single LES. (iii) Efficiency: LES-based methods exhibit high computational complexity, with cubic time and quadratic space requirements based on the number of samples. To address these issues, we propose a novel framework called MSC-DOLES (Multi-view Subspace Clustering in Diverse Orthogonal Latent Embedding Spaces), a novel framework designed to tackle these challenges. MSC-DOLES incorporates a two-stage fusion approach that generates and learns from multiple LES to maximize cross-view diversity. Orthogonality constraints on individual LES ensure view-internal diversity, resulting in a set of Diverse Orthogonal Latent Embedding Spaces (DOLES). The DOLES are then fused into a consensus anchor graph using learnable anchors. The final clustering is induced by partitioning the obtained graph without pre-processing. We develop an eight-step optimization algorithm for MSC-DOLES, which exhibits nearly linear time and space complexities relative to the number of samples. Extensive experiments demonstrate the superiority of MSC-DOLES over state-of-the-art methods.
在潜在嵌入空间（LES）中的多视图子空间聚类（MSC）领域，现有方法旨在通过将关键的多视图信息映射到低维的LES中来捕获和利用关键的多视图信息。(1)融合策略：现有方法要么采用早期融合，要么采用后期融合对多视图信息进行融合，限制了融合的有效性。多样性：目前的方法往往只关注单一的LES而忽略了多视图数据的内在多样性。（iii）效率：基于les的方法具有很高的计算复杂度，根据样本数量需要三次时间和二次空间。为了解决这些问题，我们提出了一个新的框架MSC-DOLES (Multi-view Subspace Clustering in Diverse Orthogonal Latent Embedding Spaces)，这是一个旨在解决这些挑战的新框架。MSC-DOLES采用两阶段融合方法，生成并从多个LES中学习，以最大限度地提高跨视图多样性。单个LES的正交性约束保证了视图内部的多样性，从而得到一组不同的正交潜在嵌入空间（DOLES）。然后使用可学习锚点将DOLES融合成共识锚点图。最终的聚类是在不进行预处理的情况下对得到的图进行划分。我们开发了一个八步优化算法MSC-DOLES，其时间和空间复杂度与样本数量呈近似线性关系。大量的实验证明MSC-DOLES优于最先进的方法。
{"title":"MSC-DOLES: Multi-View Subspace Clustering in Diverse Orthogonal Latent Embedding Spaces","authors":"Yuan Fang;Geping Yang;Ruichu Cai;Yiyang Yang;Zhiguo Gong;Can Chen;Zhifeng Hao","doi":"10.1109/TKDE.2025.3610659","DOIUrl":"https://doi.org/10.1109/TKDE.2025.3610659","url":null,"abstract":"In the domain of Multi-view Subspace Clustering (MSC) in Latent Embedding Space (LES), existing methods aim to capture and leverage critical multi-view information by mapping it into a low-dimensional LES. However, several aspects can be further improved: (i) Fusion Strategy: Existing methods adopt either early fusion or late fusion to integrate multi-view information, limiting the effectiveness of the fusion. (ii) Diversity: Current methods often overlook the inherent diversity in the multi-view data by focusing on a single LES. (iii) Efficiency: LES-based methods exhibit high computational complexity, with cubic time and quadratic space requirements based on the number of samples. To address these issues, we propose a novel framework called MSC-DOLES (Multi-view Subspace Clustering in Diverse Orthogonal Latent Embedding Spaces), a novel framework designed to tackle these challenges. MSC-DOLES incorporates a two-stage fusion approach that generates and learns from multiple LES to maximize cross-view diversity. Orthogonality constraints on individual LES ensure view-internal diversity, resulting in a set of Diverse Orthogonal Latent Embedding Spaces (DOLES). The DOLES are then fused into a consensus anchor graph using learnable anchors. The final clustering is induced by partitioning the obtained graph without pre-processing. We develop an eight-step optimization algorithm for MSC-DOLES, which exhibits nearly linear time and space complexities relative to the number of samples. Extensive experiments demonstrate the superiority of MSC-DOLES over state-of-the-art methods.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 12","pages":"7315-7327"},"PeriodicalIF":10.4,"publicationDate":"2025-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145456055","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：