IEEE Transactions on Knowledge and Data Engineering最新文献_第9页

A Survey on Mixture of Experts in Large Language Models 大型语言模型中专家混合的研究

IF 8.9 2区计算机科学

IEEE Transactions on Knowledge and Data Engineering Pub Date : 2025-03-25 DOI: 10.1109/TKDE.2025.3554028

Weilin Cai;Juyong Jiang;Fan Wang;Jing Tang;Sunghun Kim;Jiayi Huang

{"title":"A Survey on Mixture of Experts in Large Language Models","authors":"Weilin Cai;Juyong Jiang;Fan Wang;Jing Tang;Sunghun Kim;Jiayi Huang","doi":"10.1109/TKDE.2025.3554028","DOIUrl":"https://doi.org/10.1109/TKDE.2025.3554028","url":null,"abstract":"Large language models (LLMs) have garnered unprecedented advancements across diverse fields, ranging from natural language processing to computer vision and beyond. The prowess of LLMs is underpinned by their substantial model size, extensive and diverse datasets, and the vast computational power harnessed during training, all of which contribute to the emergent abilities of LLMs (e.g., in-context learning) that are not present in small models. Within this context, the mixture of experts (MoE) has emerged as an effective method for substantially scaling up model capacity with minimal computation overhead, gaining significant attention from academia and industry. Despite its growing prevalence, there lacks a systematic and comprehensive review of the literature on MoE. This survey seeks to bridge that gap, serving as an essential resource for researchers delving into the intricacies of MoE. We first briefly introduce the structure of the MoE layer, followed by proposing a new taxonomy of MoE. Next, we overview the core designs for various MoE models including both algorithmic and systemic aspects, alongside collections of available open-source implementations, hyperparameter configurations and empirical evaluations. Furthermore, we delineate the multifaceted applications of MoE in practice, and outline some potential directions for future research.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 7","pages":"3896-3915"},"PeriodicalIF":8.9,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144219812","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

On Efficient Single-Source Personalized PageRank Computation in Online Social Networks 在线社交网络中高效的单源个性化PageRank计算

IF 8.9 2区计算机科学

IEEE Transactions on Knowledge and Data Engineering Pub Date : 2025-03-24 DOI: 10.1109/TKDE.2025.3551751

Victor Junqiu Wei;Di Jiang;Jason Chen Zhang

{"title":"On Efficient Single-Source Personalized PageRank Computation in Online Social Networks","authors":"Victor Junqiu Wei;Di Jiang;Jason Chen Zhang","doi":"10.1109/TKDE.2025.3551751","DOIUrl":"https://doi.org/10.1109/TKDE.2025.3551751","url":null,"abstract":"The Single-Source Personalized PageRank (SSPPR) problem is widely used in information retrieval and recommendation systems. Traditional algorithms assume full knowledge of the network, making them inapplicable to online social networks (OSNs), where the topology is unknown, and users can only explore the network step by step via APIs. The only feasible approach for SSPPR in OSNs is Monte Carlo (MC) simulation, but traditional MC methods rely on static sampling, which lacks flexibility, delays feedback, and overestimates the number of required random walks. To address these limitations, we propose PANDA (Single-Source Personalized PageRank on OSNs with Rademacher Average), a progressive sampling algorithm. PANDA iteratively samples random walks in batches, estimating accuracy dynamically using Rademacher Average from statistical learning theory. This data-dependent approach allows for early termination once the desired accuracy is met. Additionally, PANDA features a dynamic sampling schedule to optimize efficiency. Empirical studies show that PANDA significantly outperforms existing methods, achieving the same accuracy with far greater efficiency.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 6","pages":"3598-3612"},"PeriodicalIF":8.9,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143896231","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

Adaptive Traffic Forecasting on Daily Basis: A Spatio-Temporal Context Learning Approach 基于时空背景学习的自适应交通预测

IF 8.9 2区计算机科学

IEEE Transactions on Knowledge and Data Engineering Pub Date : 2025-03-23 DOI: 10.1109/TKDE.2025.3570484

Xiaoyu Li;Yitian Zhang;Guodong Long;Yupeng Hu;Wenpeng Lu;Meng Chen;Chengqi Zhang;Yongshun Gong

{"title":"Adaptive Traffic Forecasting on Daily Basis: A Spatio-Temporal Context Learning Approach","authors":"Xiaoyu Li;Yitian Zhang;Guodong Long;Yupeng Hu;Wenpeng Lu;Meng Chen;Chengqi Zhang;Yongshun Gong","doi":"10.1109/TKDE.2025.3570484","DOIUrl":"https://doi.org/10.1109/TKDE.2025.3570484","url":null,"abstract":"Traffic forecasting plays a crucial role in establishing an Intelligent Transportation System (ITS) by providing essential insights. Existing traffic forecasting relies on the assumption that there is a hidden invariant spatial-temporal pattern in the large-scale dataset. However, the traffic patterns are easily influenced by many unpredictable external factors, such as policy interventions and climate changes. Due to the dynamic nature of these exogenous factors, the traffic network’s spatial-temporal patterns are also changed, thus impacting the performance of traffic forecasting models. Thus, there is an urgent need to rethink the traffic forecasting model in a fast-adaptive manner. To solve this challenge, this paper proposes an Adaptive Spatio-Temporal Context Learning framework named ASTCL, which achieves desired forecasting accuracy using daily basis traffic data collected from dozens of sensors. ASTCL constructs adaptive spatio-temporal contexts for target locations in the traffic network and generates dynamic sequence graphs based on semantic similarities. The adaptive contexts aggregate valuable information from available data, while the graphs reveal dynamic trends in traffic properties. Further, ASTCL introduces a joint convolution and attention mechanism to model intricate spatio-temporal relationships from multiple perspectives. Extensive experiments conducted on four real-world datasets demonstrate that ASTCL achieves remarkable fast adaptability and outperforms other state-of-the-art methods by a significant margin.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 8","pages":"4446-4459"},"PeriodicalIF":8.9,"publicationDate":"2025-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144573012","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

#REval: A Semantic Evaluation Framework for Hashtag Recommendation #REval：标签推荐的语义评估框架

IF 8.9 2区计算机科学

IEEE Transactions on Knowledge and Data Engineering Pub Date : 2025-03-21 DOI: 10.1109/TKDE.2025.3553683

Areej Alsini;Du Q. Huynh;Amitava Datta

{"title":"#REval: A Semantic Evaluation Framework for Hashtag Recommendation","authors":"Areej Alsini;Du Q. Huynh;Amitava Datta","doi":"10.1109/TKDE.2025.3553683","DOIUrl":"https://doi.org/10.1109/TKDE.2025.3553683","url":null,"abstract":"Automatic evaluation of hashtag recommendation models is a fundamental task in Twitter. In the traditional evaluation methods, the recommended hashtags from an algorithm are first compared with the ground truth hashtags for exact correspondences. The number of exact matches is then used to calculate the hit rate, hit ratio, precision, recall, or F1-score. This way of evaluating hashtag similarities is inadequate as it ignores the semantic correlation between the recommended and ground truth hashtags. To tackle this problem, we propose a novel semantic evaluation framework for hashtag recommendation, called #REval. This framework includes an internal module referred to as <italic>BERTag, which automatically learns the hashtag embeddings. We investigate on how the #REval framework performs under different word embedding methods and different numbers of synonyms and hashtags in the recommendation using our proposed #REval-hit-ratio measure. Our experiments of the proposed framework on three large datasets show that #REval gave more meaningful hashtag synonyms for hashtag recommendation evaluation. Our analysis also highlights the sensitivity of the framework to the word embedding technique, with #REval based on BERTag more superior over #REval based on Word2Vec, FastText, and GloVe.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 6","pages":"3075-3087"},"PeriodicalIF":8.9,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143896389","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

Efficient Bitruss Decomposition on GPU 基于GPU的高效比特线分解

IF 8.9 2区计算机科学

IEEE Transactions on Knowledge and Data Engineering Pub Date : 2025-03-21 DOI: 10.1109/TKDE.2025.3569804

Shunyang Li;Kai Wang;Wenjie Zhang;Xuemin Lin;Yizhang He

{"title":"Efficient Bitruss Decomposition on GPU","authors":"Shunyang Li;Kai Wang;Wenjie Zhang;Xuemin Lin;Yizhang He","doi":"10.1109/TKDE.2025.3569804","DOIUrl":"https://doi.org/10.1109/TKDE.2025.3569804","url":null,"abstract":"Cohesive subgraph computation on bipartite graphs has drawn significant research interest recently. As a popular cohesive subgraph model, <inline-formula><tex-math>$k$</tex-math></inline-formula>-bitruss is defined as the maximal subgraph where each edge is contained in at least <inline-formula><tex-math>$k$</tex-math></inline-formula> butterflies (i.e., a (2, 2)-biclique). The bitruss decomposition problem is widely studied, which aims to compute all <inline-formula><tex-math>$k$</tex-math></inline-formula>-bitrusses for <inline-formula><tex-math>$k geq 0$</tex-math></inline-formula>. The state-of-the-art CPU-based solutions require extensive costs to construct an index structure for grouping butterflies, leading to scalability challenges on large bipartite graphs. In this paper, we explore bitruss decomposition with GPU by leveraging the parallel computing capabilities of GPU architectures. As the index-based approach requires extensive space and the memory resources of GPUs are limited, we propose <monospace>GBiD</monospace>, which is a peeling-based algorithm on GPUs that utilizes a block-centric computation scheme to enable space-efficient bitruss decomposition without any indexing structure. In addition, cost-aware common neighbor exploration and neighbor list accessing optimizations are proposed to enhance <monospace>GBiD</monospace> by reducing the cost of enumerating butterflies and accessing the graph structure during the peeling process. Extensive experiments conducted on 10 real-world datasets demonstrate that our proposed techniques significantly surpass existing CPU-based solutions in terms of both space and time efficiency.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 8","pages":"4578-4590"},"PeriodicalIF":8.9,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144573004","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

Learning Temporal Event Knowledge for Continual Social Event Classification 学习时间事件知识用于连续社会事件分类

IF 8.9 2区计算机科学

IEEE Transactions on Knowledge and Data Engineering Pub Date : 2025-03-20 DOI: 10.1109/TKDE.2025.3553162

Shengsheng Qian;Shengjie Zhang;Dizhan Xue;Huaiwen Zhang;Changsheng Xu

{"title":"Learning Temporal Event Knowledge for Continual Social Event Classification","authors":"Shengsheng Qian;Shengjie Zhang;Dizhan Xue;Huaiwen Zhang;Changsheng Xu","doi":"10.1109/TKDE.2025.3553162","DOIUrl":"https://doi.org/10.1109/TKDE.2025.3553162","url":null,"abstract":"With the rapid development of Internet and the burgeoning scale of social media, Social Event Classification (SEC) has garnered increasing attention. The existing study of SEC focuses on recognizing a fixed set of social events. However, in real-world scenarios, new social events continually emerge on social media, which suggests the necessity for a practical SEC model that can swiftly adapt to the evolving environment with incremental social events. Therefore, in this paper, we study a new yet crucial problem defined as Continual Social Event Classification (C-SEC), where new events continually emerge in the sequentially collected social data. Accordingly, we propose a novel Temporal Event Knowledge Network (TEKNet) to continually learn temporal event knowledge for C-SEC with temporally incremental events. First, we conduct present event knowledge learning to learn the classification of newly emerging events in the presently incoming data. Second, we design past event knowledge replay with self-knowledge distillation to consolidate the learned knowledge of past events and prevent catastrophic forgetting. Finally, we propose future event knowledge pretraining with a modality mixture mechanism to pretrain the classifiers for events that occur in the future. Comprehensive experiments on real-world social event datasets demonstrate the superiority of our proposed TEKNet for C-SEC.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 6","pages":"3485-3498"},"PeriodicalIF":8.9,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143896149","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

Implicit Multi-Behavior Generative Recommendation With Mixture of Quantization 混合量化的隐式多行为生成推荐

IF 8.9 2区计算机科学

IEEE Transactions on Knowledge and Data Engineering Pub Date : 2025-03-20 DOI: 10.1109/TKDE.2025.3572014

Yuze Tan;Yanjie Gou;Kouying Xue;Shudong Huang;Yi Hu;Ivor W. Tsang;Jiancheng Lv

{"title":"Implicit Multi-Behavior Generative Recommendation With Mixture of Quantization","authors":"Yuze Tan;Yanjie Gou;Kouying Xue;Shudong Huang;Yi Hu;Ivor W. Tsang;Jiancheng Lv","doi":"10.1109/TKDE.2025.3572014","DOIUrl":"https://doi.org/10.1109/TKDE.2025.3572014","url":null,"abstract":"Generative recommendation systems have recently seen a surge in interest, largely due to the promising advancements in generative AI. As a competitive solution for multi-behavior sequence recommendations, much of the recent research has concentrated on predicting the next item a user will likely interact with using a generative approach. However, these methods often 1). assign multiple residual quantization layers to obtain item codes, which leads to extra storage costs of more codebooks. And 2). explicitly utilize behavior sequences leading to longer sequences, potentially increasing the training time as well as inference time compared with original sequences. In response to these challenges, we introduce the <bold>Implicit <bold>Multi-<bold>Behavior <bold>Generative recommendation with a mixture of quantization (IMBGen) approach in this paper. Specifically, we have devised a <bold>Mixture <bold>of <bold>Quantization (MoQ) that combines the merits of both residual and parallel quantization for a more effective tokenization process. Additionally, we propose an Implicit Behavior Modeling (IBM) framework, allowing for more efficient integration of users’ behaviors into the interacted items. Finally, we conducted extensive experiments on two widely used benchmark datasets and further confirmed our findings with an online A/B test. The results consistently demonstrate the advantages of our approach over other baseline methods.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 8","pages":"4704-4715"},"PeriodicalIF":8.9,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144573016","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

QSTGNN: Quaternion Spatio-Temporal Graph Neural Networks 四元数时空图神经网络

IF 8.9 2区计算机科学

IEEE Transactions on Knowledge and Data Engineering Pub Date : 2025-03-20 DOI: 10.1109/TKDE.2025.3571983

Ye Liu;Chaoxiong Lin;Yuchen Mou;Huaiguang Jiang;Hongmin Cai

{"title":"QSTGNN: Quaternion Spatio-Temporal Graph Neural Networks","authors":"Ye Liu;Chaoxiong Lin;Yuchen Mou;Huaiguang Jiang;Hongmin Cai","doi":"10.1109/TKDE.2025.3571983","DOIUrl":"https://doi.org/10.1109/TKDE.2025.3571983","url":null,"abstract":"Spatio-temporal time series forecasting has attracted great attentions in various fields, including climate, power, and traffic forecasting. Recently, Spatio-temporal Graph Neural Networks (STGNNs) have shown promising performances in modeling spatial dependencies based on graph neural networks (GNNs) and temporal dependencies based on temporal learning modules. However, most STGNNs do not effectively integrate explicit and implicit relationships between nodes, nor do they adequately capture long and short-term time dependencies. To address these challenges, this paper presents a Quaternion Spatio-temporal Graph Neural Network (QSTGNN). Specifically, the quaternion spatio-temporal graph is constructed firstly, such that the information of both short and long-term time steps are preserved in quaternion feature tensor, and information of multiple explicit graphs and implicit graph are integrated in quaternion graph adjacency matrix. Then, two modules are designed: a 1D quaternion convolution module and a quaternion graph convolution module. In the 1D quaternion convolution module, complex temporal correlations among short and long-term time steps can be well exploited by 1D quaternion convolution operator based on the quaternion Hamilton product. In the quaternion graph convolution module, quaternion graph convolution is designed to characterize nonlinear dependencies among multiple spatial graphs, including explicit and implicit graphs. Extensive experiments are conducted on six datasets, and the results show that QSTGNN achieves state-of-the-art performances over the existing ten methods. Explainable analysis presents that multiple spatial correlations can accurately illustrate the traffic flow and road functional information in real traffic roads.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 8","pages":"4776-4790"},"PeriodicalIF":8.9,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144573018","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

Deep Learning Based Knowledge Tracing: A Review, a Tool and Empirical Studies 基于深度学习的知识追踪：综述、工具与实证研究

IF 8.9 2区计算机科学

IEEE Transactions on Knowledge and Data Engineering Pub Date : 2025-03-19 DOI: 10.1109/TKDE.2025.3552759

Zitao Liu;Teng Guo;Qianru Liang;Mingliang Hou;Bojun Zhan;Jiliang Tang;Weiqi Luo;Jian Weng

{"title":"Deep Learning Based Knowledge Tracing: A Review, a Tool and Empirical Studies","authors":"Zitao Liu;Teng Guo;Qianru Liang;Mingliang Hou;Bojun Zhan;Jiliang Tang;Weiqi Luo;Jian Weng","doi":"10.1109/TKDE.2025.3552759","DOIUrl":"https://doi.org/10.1109/TKDE.2025.3552759","url":null,"abstract":"Knowledge tracing (KT) involves utilizing historical data from students’ learning interactions to model their mastery of knowledge over time, with the aim of predicting their future performance in interactions. Recently, significant advancements have been achieved through the application of various deep learning methodologies to address the KT challenge. However, a considerable proportion of deep learning-based knowledge tracing (DLKT) approaches exhibit striking similarities in their methodologies, and model designs, and even the outcomes demonstrate minimal divergence. In addition, the evaluation procedures employed in current DLKT studies are not standardized, resulting in substantial inconsistencies in the reported area under the curve (AUC) outcomes, despite analyzing the same model on identical datasets. To address the two aforementioned problems, this paper proposes a generalized DLKT framework and represents the existing DLKT models with five components, i.e., multimodal data encoder, student knowledge memory, auxiliary knowledge base, learning outcome objective, and computational efficiency and scalability. Furthermore, we develop and open source a standardized DLKT benchmark platform named <sc>pyKT,1 that consists of a standardized set of integrated data preprocessing procedures on 9 popular datasets across different domains, and 21 frequently compared DLKT model implementations. With <sc>pyKT, we conduct empirical and reproducible research to assess the performance of prevalent DLKT algorithms in an unbiased and clear setting over multiple data sources. Finally, we discuss the applications of KT techniques in the educational sector and their future development directions.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 8","pages":"4512-4536"},"PeriodicalIF":8.9,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144572950","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

Efficient Algorithms for Minimizing the Kirchhoff Index via Adding Edges 通过添加边最小化Kirchhoff指数的有效算法

IF 8.9 2区计算机科学

IEEE Transactions on Knowledge and Data Engineering Pub Date : 2025-03-18 DOI: 10.1109/TKDE.2025.3552644

Xiaotian Zhou;Ahad N. Zehmakan;Zhongzhi Zhang

{"title":"Efficient Algorithms for Minimizing the Kirchhoff Index via Adding Edges","authors":"Xiaotian Zhou;Ahad N. Zehmakan;Zhongzhi Zhang","doi":"10.1109/TKDE.2025.3552644","DOIUrl":"https://doi.org/10.1109/TKDE.2025.3552644","url":null,"abstract":"The Kirchhoff index, which is the sum of the resistance distance between every pair of nodes in a network, is a key metric for gauging network performance, where lower values signify enhanced performance. In this paper, we study the problem of minimizing the Kirchhoff index by adding edges. We first provide a greedy algorithm for solving this problem and give an analysis of its quality based on the bounds of the submodularity ratio and the curvature. Then, we introduce a gradient-based greedy algorithm as a new paradigm to solve this problem. To accelerate the computation cost, we leverage geometric properties, convex hull approximation, and approximation of the projected coordinate of each point. To further improve this algorithm, we use pre-pruning and fast update techniques, making it particularly suitable for large networks. Our proposed algorithms have nearly-linear time complexity. We provide extensive experiments on ten real networks to evaluate the quality of our algorithms. The results demonstrate that our proposed algorithms outperform the state-of-the-art methods in terms of efficiency and effectiveness. Moreover, our algorithms are scalable to large graphs with over 5 million nodes and 12 million edges.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 6","pages":"3342-3355"},"PeriodicalIF":8.9,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143896274","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