IEEE Transactions on Knowledge and Data Engineering最新文献_第8页

Rethinking Variational Bayes in Community Detection From Graph Signal Perspective 从图信号的角度重新思考变分贝叶斯在社区检测中的应用

IF 8.9 2区计算机科学

IEEE Transactions on Knowledge and Data Engineering Pub Date : 2025-02-18 DOI: 10.1109/TKDE.2025.3543378

Junwei Cheng;Yong Tang;Chaobo He;Pengxing Feng;Kunlin Han;Quanlong Guan

{"title":"Rethinking Variational Bayes in Community Detection From Graph Signal Perspective","authors":"Junwei Cheng;Yong Tang;Chaobo He;Pengxing Feng;Kunlin Han;Quanlong Guan","doi":"10.1109/TKDE.2025.3543378","DOIUrl":"https://doi.org/10.1109/TKDE.2025.3543378","url":null,"abstract":"Methods based on variational bayes theorytare widely used to detect community structures in networks. In recent years, many related methods have emerged that provide valuable insights into variational bayes theory. Remarkably, a fundamental assumption remains incomprehensible. Variational bayes-based methods typically employ a posterior distribution that follows a gaussian distribution to approximate the unknown prior distribution. However, the complexity and irregularity of node distributions in real-world networks prompt us to consider what characteristics of network information are suitable for the posterior distribution. Mathematically, inappropriate low- and high-frequency signals in expectation inference and variance inference can intensify the adverse effects of community distortion and ambiguity. To analysis these two phenomena and propose reasonable countermeasures, we conduct an empirical study. It is found that appropriately compressing low-frequency signals during expectation inference and amplifying high-frequency signals during variance inference are effective strategies. Based on these two strategies, this paper proposes a novel variational bayes plug-in, namely VBPG, to boost the performance of existing variational bayes-based community detection methods. Specifically, we modulate the frequency signals during expectation and variance inference to generate a new gaussian distribution. This strategy improves the fitting accuracy between the posterior distribution and the unknown true distribution without altering the modules of existing methods. The comprehensive experimental results validate that methods using VBPG achieve competitive performance improvements in most cases.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 5","pages":"2903-2917"},"PeriodicalIF":8.9,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143769512","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}

引用次数: 0

Style Feature Extraction Using Contrastive Conditioned Variational Autoencoders With Mutual Information Constraints 基于互信息约束的对比条件变分自编码器的风格特征提取

IF 8.9 2区计算机科学

IEEE Transactions on Knowledge and Data Engineering Pub Date : 2025-02-18 DOI: 10.1109/TKDE.2025.3543383

Suguru Yasutomi;Toshihisa Tanaka

{"title":"Style Feature Extraction Using Contrastive Conditioned Variational Autoencoders With Mutual Information Constraints","authors":"Suguru Yasutomi;Toshihisa Tanaka","doi":"10.1109/TKDE.2025.3543383","DOIUrl":"https://doi.org/10.1109/TKDE.2025.3543383","url":null,"abstract":"Extracting fine-grained features such as styles from unlabeled data is crucial for data analysis. Unsupervised methods such as variational autoencoders (VAEs) can extract styles that are usually mixed with other features. Conditional VAEs (CVAEs) can isolate styles using class labels; however, there are no established methods to extract only styles using unlabeled data. In this paper, we propose a CVAE-based method that extracts style features using only unlabeled data. The proposed model consists of a contrastive learning (CL) part that extracts style-independent features and a CVAE part that extracts style features. The CL model learns representations independent of data augmentation, which can be viewed as a perturbation in styles, in a self-supervised manner. Considering the style-independent features from the pretrained CL model as a condition, the CVAE learns to extract only styles. Additionally, we introduce a constraint based on mutual information between the CL and VAE features to prevent the CVAE from ignoring the condition. Experiments conducted using two simple datasets, MNIST and an original dataset based on Google Fonts, demonstrate that the proposed method can efficiently extract style features. Further experiments using real-world natural image datasets were also conducted to illustrate the method’s extendability.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 5","pages":"3001-3014"},"PeriodicalIF":8.9,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10891874","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143769516","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Build a Good Human-Free Prompt Tuning: Jointly Pre-Trained Template and Verbalizer for Few-Shot Classification 构建一个良好的无人工提示调优：联合预训练模板和少弹分类语言器

IF 8.9 2区计算机科学

IEEE Transactions on Knowledge and Data Engineering Pub Date : 2025-02-18 DOI: 10.1109/TKDE.2025.3543422

Mouxiang Chen;Han Fu;Chenghao Liu;Xiaoyun Joy Wang;Zhuo Li;Jianling Sun

{"title":"Build a Good Human-Free Prompt Tuning: Jointly Pre-Trained Template and Verbalizer for Few-Shot Classification","authors":"Mouxiang Chen;Han Fu;Chenghao Liu;Xiaoyun Joy Wang;Zhuo Li;Jianling Sun","doi":"10.1109/TKDE.2025.3543422","DOIUrl":"https://doi.org/10.1109/TKDE.2025.3543422","url":null,"abstract":"Prompt tuning for pre-trained language models (PLMs) has been an effective approach for few-shot text classification. To make a prediction, a typical prompt tuning method employs a template wrapping the input text into a cloze question, and a verbalizer mapping the output embedding to labels. However, current methods typically depend on handcrafted templates and verbalizers, which require much domain-specific prior knowledge by human efforts. In this work, we investigate how to build a good human-free prompt tuning using soft prompt templates and soft verbalizers, which can be learned directly from data. To address the challenge of data scarcity, we integrate a set of trainable bases for sentence representation to transfer the contextual information into a low-dimensional space. By jointly pre-training the soft prompts and the bases using contrastive learning, the projection space can catch critical semantics at the sentence level, which could be transferred to various downstream tasks. To better bridge the gap between downstream tasks and the pre-training procedure, we formulate the few-shot classification tasks as another contrastive learning problem. We name this Jointly Pretrained Template and Verbalizer (JPTV). Extensive experiments show that this human-free prompt tuning can achieve comparable or even better performance than manual prompt tuning.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 5","pages":"2253-2265"},"PeriodicalIF":8.9,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143769392","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}

引用次数: 0

Cross-Graph Interaction Networks 跨图交互网络

IF 8.9 2区计算机科学

IEEE Transactions on Knowledge and Data Engineering Pub Date : 2025-02-18 DOI: 10.1109/TKDE.2025.3543377

Qihang Guo;Xibei Yang;Weiping Ding;Yuhua Qian

{"title":"Cross-Graph Interaction Networks","authors":"Qihang Guo;Xibei Yang;Weiping Ding;Yuhua Qian","doi":"10.1109/TKDE.2025.3543377","DOIUrl":"https://doi.org/10.1109/TKDE.2025.3543377","url":null,"abstract":"Graph neural networks (GNNs) are recognized as a significant methodology for handling graph-structure data. However, with the increasing prevalence of learning scenarios involving multiple graphs, traditional GNNs mostly overlook the relationships between nodes across different graphs, mainly due to their limitation of traditional message passing within each graph. In this paper, we propose a novel GNN architecture called cross-graph interaction networks (GInterNet) to enable inter-graph message passing. Specifically, we develop a cross-graph topology construction module to uncover and learn the potential topologies between nodes across different graphs. Furthermore, we establish inter-graph message passing based on the learned cross-graph topologies, achieving cross-graph interaction by aggregating information from different graphs. Finally, we employ cross-graph construction functions involving the relationships between contextual information and cross-graph topology structure to iteratively update the cross-graph topologies. Different to existing related approaches, GInterNet is designed as a cross-graph interaction paradigm for inter-graph message passing. It enables multi-graph interaction during the message passing process. Additionally, it is a plug-and-play framework that can be easily embedded into other models. We evaluate its performance in semi-supervised and unsupervised learning scenarios involving multiple graphs. A detailed theoretical analysis and extensive experiment results have shown that GInterNet improves the performance and robustness of the base models.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 5","pages":"2341-2355"},"PeriodicalIF":8.9,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143769369","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}

引用次数: 0

REP: An Interpretable Robustness Enhanced Plugin for Differentiable Neural Architecture Search 可微分神经结构搜索的可解释鲁棒性增强插件

IF 8.9 2区计算机科学

IEEE Transactions on Knowledge and Data Engineering Pub Date : 2025-02-18 DOI: 10.1109/TKDE.2025.3543503

Yuqi Feng;Yanan Sun;Gary G. Yen;Kay Chen Tan

{"title":"REP: An Interpretable Robustness Enhanced Plugin for Differentiable Neural Architecture Search","authors":"Yuqi Feng;Yanan Sun;Gary G. Yen;Kay Chen Tan","doi":"10.1109/TKDE.2025.3543503","DOIUrl":"https://doi.org/10.1109/TKDE.2025.3543503","url":null,"abstract":"Neural architecture search (NAS) is widely used to automate the design of high-accuracy deep architectures, which are often vulnerable to adversarial attacks in practice due to the lack of adversarial robustness. Existing methods focus on the direct utilization of regularized optimization process to address this critical issue, which causes the lack of interpretability for the end users to learn how the robust architecture is constructed. In this paper, we introduce a robust enhanced plugin (REP) method for differentiable NAS to search for robust neural architectures. Different from existing peer methods, REP focuses on the robust search primitives in the search space of NAS methods, and naturally has the merit of contributing to understanding how the robust architectures are progressively constructed. Specifically, we first propose an effective sampling strategy to sample robust search primitives in the search space. In addition, we also propose a probabilistic enhancement method to guarantee natural accuracy and adversarial robustness simultaneously during the search process. We conduct experiments on both convolutional neural networks and graph neural networks with widely used benchmarks against state of the arts. The results reveal that REP can achieve superiority in terms of both the adversarial robustness to popular adversarial attacks and the natural accuracy of original data. REP is flexible and can be easily used by any existing differentiable NAS methods to enhance their robustness without much additional effort.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 5","pages":"2888-2902"},"PeriodicalIF":8.9,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143769514","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}

引用次数: 0

Intent Propagation Contrastive Collaborative Filtering 意图传播对比协同过滤

IF 8.9 2区计算机科学

IEEE Transactions on Knowledge and Data Engineering Pub Date : 2025-02-18 DOI: 10.1109/TKDE.2025.3543241

Haojie Li;Junwei Du;Guanfeng Liu;Feng Jiang;Yan Wang;Xiaofang Zhou

{"title":"Intent Propagation Contrastive Collaborative Filtering","authors":"Haojie Li;Junwei Du;Guanfeng Liu;Feng Jiang;Yan Wang;Xiaofang Zhou","doi":"10.1109/TKDE.2025.3543241","DOIUrl":"https://doi.org/10.1109/TKDE.2025.3543241","url":null,"abstract":"Disentanglement techniques used in collaborative filtering uncover interaction intents between nodes, improving the interpretability of node representations and enhancing recommendation performance. However, existing disentanglement methods still face the following two problems. 1) They focus on local structural features derived from direct node interactions, overlooking the comprehensive graph structure, which limits disentanglement accuracy. 2) The disentanglement process depends on backpropagation signals derived from recommendation tasks, lacking direct supervision, which may lead to biases and overfitting. To address the issues, we propose the <bold>Intent <bold>Propagation <bold>Contrastive <bold>Collaborative <bold>Filtering (IPCCF) algorithm. Specifically, we design a double helix message propagation framework to more effectively extract the deep semantic information of nodes, thereby improving the model's understanding of interactions between nodes. An intent message propagation method is also developed that incorporates graph structure information into the disentanglement process, thereby expanding the consideration scope of disentanglement. In addition, contrastive learning techniques are employed to align node representations derived from the structure and intents, providing direct supervision for the disentanglement process, mitigating biases, and enhancing the model's robustness to overfitting. The experiments on three real data graphs illustrate the superiority of the proposed approach.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 5","pages":"2665-2679"},"PeriodicalIF":8.9,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143769557","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}

引用次数: 0

QAEA-DR: A Unified Text Augmentation Framework for Dense Retrieval 面向密集检索的统一文本增强框架

IF 8.9 2区计算机科学

IEEE Transactions on Knowledge and Data Engineering Pub Date : 2025-02-18 DOI: 10.1109/TKDE.2025.3543203

Hongming Tan;Shaoxiong Zhan;Hai Lin;Hai-Tao Zheng;Wai Kin Chan

{"title":"QAEA-DR: A Unified Text Augmentation Framework for Dense Retrieval","authors":"Hongming Tan;Shaoxiong Zhan;Hai Lin;Hai-Tao Zheng;Wai Kin Chan","doi":"10.1109/TKDE.2025.3543203","DOIUrl":"https://doi.org/10.1109/TKDE.2025.3543203","url":null,"abstract":"In dense retrieval, embedding long texts into dense vectors can result in information loss, leading to inaccurate query-text matching. Additionally, low-quality texts with excessive noise or sparse key information are unlikely to align well with relevant queries. Recent studies mainly focus on improving the sentence embedding model or retrieval process. In this work, we introduce a novel text augmentation framework for dense retrieval. This framework transforms raw documents into information-dense text formats, which supplement the original texts to effectively address the aforementioned issues without modifying embedding or retrieval methodologies. Two text representations are generated via large language models (LLMs) zero-shot prompting: question-answer pairs and element-driven events. We term this approach QAEA-DR: unifying question-answer generation and event extraction in a text augmentation framework for dense retrieval. To further enhance the quality of generated texts, a scoring-based evaluation and regeneration mechanism is introduced in LLM prompting. Our QAEA-DR model has a positive impact on dense retrieval, supported by both theoretical analysis and empirical experiments.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 6","pages":"3669-3683"},"PeriodicalIF":8.9,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143896436","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}

引用次数: 0

Multiscale Temporal Dynamic Learning for Time Series Classification 时间序列分类的多尺度时间动态学习

IF 8.9 2区计算机科学

IEEE Transactions on Knowledge and Data Engineering Pub Date : 2025-02-18 DOI: 10.1109/TKDE.2025.3542799

Shikang Liu;Xiren Zhou;Huanhuan Chen

{"title":"Multiscale Temporal Dynamic Learning for Time Series Classification","authors":"Shikang Liu;Xiren Zhou;Huanhuan Chen","doi":"10.1109/TKDE.2025.3542799","DOIUrl":"https://doi.org/10.1109/TKDE.2025.3542799","url":null,"abstract":"Time series classification (TSC) is crucial in many applications, yet accurately modeling complex time series patterns remains challenging. Model-based TSC strives to aptly model time series by capturing their intrinsic temporal dynamics, deriving effective dynamic representations for classification. Despite significant progress in this domain, existing works are still constrained by a singular and overly simplistic modeling paradigm, which proves inadequate to handle the multiscale hierarchies inherent in time series. Additionally, the prevailing reliance on manual model configuration fails to address the diverse dynamic characteristics across varying data scenarios. In this paper, we amalgamate multiple recurrent reservoirs to devise a model-based Multiscale Temporal Dynamic Learning (MsDL) approach. These reservoirs are endowed with varied recurrent connection skips, ensuring a comprehensive capture of temporal dynamics across different timescales. We also present a multi-objective optimization algorithm, which adaptively configures the memory length of each reservoir, allowing for more accurate time series modeling. This optimization further encourages time series from the same class to look closer, while separating those from different classes, thereby enhancing the category-discriminability. Extensive experiments on public datasets demonstrate that MsDL outperforms the state-of-the-art methods. Additionally, ablation studies confirm that our multiscale design and optimization algorithm effectively enhance classification accuracy.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 6","pages":"3543-3555"},"PeriodicalIF":8.9,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143896391","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}

引用次数: 0

Practical Equi-Join Over Encrypted Database With Reduced Leakage 实用的对等连接加密数据库与减少泄漏

IF 8.9 2区计算机科学

IEEE Transactions on Knowledge and Data Engineering Pub Date : 2025-02-18 DOI: 10.1109/TKDE.2025.3543168

Qiaoer Xu;Jianfeng Wang;Shi-Feng Sun;Zhipeng Liu;Xiaofeng Chen

{"title":"Practical Equi-Join Over Encrypted Database With Reduced Leakage","authors":"Qiaoer Xu;Jianfeng Wang;Shi-Feng Sun;Zhipeng Liu;Xiaofeng Chen","doi":"10.1109/TKDE.2025.3543168","DOIUrl":"https://doi.org/10.1109/TKDE.2025.3543168","url":null,"abstract":"Secure join schemes, an important class of queries over encrypted databases, have attracted increasing attention. While efficient querying is paramount, data owners also emphasize the significance of privacy preservation. The state-of-the-art JXT (Jutla and Patranabis ASIACRYPT 2022) enables efficient join queries over encrypted tables with a symmetric-key solution. However, we observe that JXT inadvertently leaks undesirable query results as the number of queries increases. In this paper, we propose a novel equi-join scheme, One-Time Join Cross-Tags (OTJXT), which can avoid additional result leakage in multiple queries and extend to equi-join as opposed to natural join in JXT. Specifically, we design a new data encoding method using nonlinear transformations that reveals only the union of results for each query without extra leakage observed in JXT. Moreover, OTJXT addresses the linear search complexity issue (Shafieinejad et al. ICDE 2022) while preventing multiple query leakage. Finally, we implement OTJXT and compare its performance with JXT and Shafieinejad et al.'s scheme on the TPC-H dataset. The results show that OTJXT outperforms in search and storage efficiency, achieving a <inline-formula><tex-math>$mathbf {98.5times }$</tex-math></inline-formula> (resp., <inline-formula><tex-math>$mathbf {10^{6}times }$</tex-math></inline-formula>) speedup in search latency and reducing storage cost by 62.5% (resp., 78.5%), compared to JXT (resp., Shafieinejad et al.'s scheme). Using OTJXT, a TPC-H query on a 40 MB database only takes 21 ms.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 5","pages":"2846-2860"},"PeriodicalIF":8.9,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143769540","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}

引用次数: 0

Graph Clustering With Harmonic-Maxmin Cut Guidance 调和-最大割制导的图聚类

IF 8.9 2区计算机科学

IEEE Transactions on Knowledge and Data Engineering Pub Date : 2025-02-17 DOI: 10.1109/TKDE.2025.3542839

Jingwei Chen;Zihan Wu;Jingqing Cheng;Xiaohua Xu;Feiping Nie

{"title":"Graph Clustering With Harmonic-Maxmin Cut Guidance","authors":"Jingwei Chen;Zihan Wu;Jingqing Cheng;Xiaohua Xu;Feiping Nie","doi":"10.1109/TKDE.2025.3542839","DOIUrl":"https://doi.org/10.1109/TKDE.2025.3542839","url":null,"abstract":"Graph clustering has become a crucial technique for uncovering community structures in complex network data. However, existing approaches often introduce cumbersome regularization or constraints (hyperparameter tuning burden) to obtain balanced clustering results, thereby increasing hyperparameter tuning requirements and intermediate variables. These limitations can lead to suboptimal performance, particularly in scenarios involving imbalanced clusters or large-scale datasets. Besides, most graph cut clustering methods solve two separate discrete problems, resulting in information loss and relying on time-consuming eigen-decomposition. To address these challenges, this paper propose an effective graph cut framework, termed Harmonic MaxMin Cut (HMMC), inspired by worst-case objective optimization and the harmonic mean. Unlike traditional spectral clustering, HMMC produces all cluster assignments in a single step, eliminating the need for additional discretization and notably enhancing robustness to “worst-case cluster” boundaries. this paper further devise a fast coordinate descent (CD) solver that scales linearly complexity with the graph size, offering a computationally efficient alternative to eigen decomposition. Extensive experiments on real-world datasets demonstrate that HMMC is comparable to, or even surpasses, state-of-the-art methods, while also finding more favorable local solutions than non-negative matrix factorization techniques.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 5","pages":"2600-2613"},"PeriodicalIF":8.9,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143769366","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}

引用次数: 0