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Higher Order Local Search Assisted Community Detection in Signed Networks

IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS 2025年

作者： Xiao, Jing Dang, Zi-Zheng Yang, Zhi-Xiang Xu, Xiao-Ke Shenzhen Technol Univ Coll Big Data & Internet Shenzhen 518118 Peoples R China Dalian Minzu Univ Coll Informat & Commun Engn Dalian 116600 Peoples R China Beijing Normal Univ Sch Journalism & Commun Beijing 100875 Peoples R China

Community detection in signed networks reveals mesoscale structures and sign characteristics, facilitating accurate and realistic understanding of real-life signed networks. Given the complexity of signed community structures and the NP-hard challenge of achieving optimal partitions, modularity optimization using memetic algorithms has emerged as a promising approach. However, the identified signed partitions still face challenges in modularity quality and precision, with many misassigned nodes or being stuck at local optima, particularly in ambiguous and complex structures. To address this issue, this article introduces a new concept: central-node signed motif, a higher order local topological feature that effectively identifies signed communities, providing richer and more efficient topological information. Furthermore, we utilize these central-node signed motifs to improve community detection through a framework named higher order local search assisted memetic modularity (HLSMM). In HLSMM, we design a central-node signed motif-based local search strategy to optimize node assignments and improve modularity quality and structural balance of optimized signed communities. Experimental results from synthetic and real-world datasets demonstrate the effectiveness of the local search strategy in enhancing accuracy and modularity quality. HLSMM exhibits a general advantage in modularity quality over currently representative methods on real-world datasets, particularly in ambiguous and complex community structures.

关键词： Optimization Image edge detection Search problems Memetics Vectors partitioning algorithms Faces Accuracy Topology Measurement Community detection higher order local search memetic algorithm signed motifs signed network

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Examining the Impact of Fingerprint Vector Size on Similarity Determination and Clustering Performance of a Pattern-Based Similarity Metric

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IEEE ACCESS 2025年 13卷 51660-51668页

作者： Yaro, Abdulmalik Shehu Maly, Filip Prazak, Pavel Univ Hradec Kralove Fac Informat & Management Dept Informat & Quantitat Methods Hradec Kralove 50003 Czech Republic Ahmadu Bello Univ Dept Elect & Telecommun Engn Zaria 810106 Nigeria

The correlation-based context similarity coefficient (CSC) metric is a pattern-based fingerprint similarity metric that has gained interest in fingerprint database clustering operations. The performance of the traditional distance-based metric in fingerprint vector similarity determination has been known to be influenced by the size of the fingerprint vector. However, as for the correlation-based CSC metric, there is no comprehensive research on how fingerprint vector size affects its performance in similarity determination and subsequently clustering performance. As such, this paper examines the impact of fingerprint vector size on the similarity determination performance of the correlation-based CSC metric with the k-medoids algorithm employed for clustering. The analysis is performed across four synthetic and two experimentally generated fingerprint databases with varying fingerprint vector sizes. The impact analysis is carried out with the clustering algorithm set to generate K is an element of [3, 5] clusters. Additionally, the results are compared against three distance-based metrics: squared Euclidean, Manhattan, and cosine. With silhouette score as the clustering performance metric, the simulation result shows that the size of the fingerprint vector influences the similarity determination performance of the correlation-based CSC metric. Additionally, the number of clusters in which the clustering algorithm is set to generate also contributes to how the correlation-based CSC metric performs in similarity determination. The similarity determination time complexity of the correlation-based CSC metric increases with fingerprint vector size, making efficient clustering more challenging as fingerprint vector sizes increase. For optimal performance, the correlation-based CSC metric is recommended for us as a similarity metric on a database with a fingerprint vector size of N <= 4 and a clustering algorithm configured to generate no more than 3 clusters.

关键词： Fingerprint recognition Vectors Measurement Clustering algorithms Databases Measurement uncertainty Accuracy Mathematical models partitioning algorithms Location awareness Clustering context similarity coefficient fingerprint vector size pattern-based metric similarity determination

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Compact Workspace Decomposition Based on a Bottom-Up Approach

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IEEE ACCESS 2025年 13卷 3917-3928页

作者： Skorobogatov, Georgy Calvo, Toni Barrado, Cristina Salami, Esther Tech Univ Catalonia Comp Architecture Dept Castelldefels 08860 Spain

In this paper, we present an algorithm that addresses the challenge of dividing a workspace among multiple UAVs. The workspace can be any convex or non-convex polygon and may contain holes of various shapes that represent no-fly zones. The UAVs can be heterogeneous, with different levels of autonomy, speed, and range. The goal of the workspace division is to obtain areas whose sizes are best matched to the capabilities of the UAVs while maximizing compactness. The algorithm decomposes the polygon representing the workspace into a triangular grid, followed by an iterative process of accumulating adjacent triangles while maximizing the compactness of the resulting regions. The performance of the algorithm and the quality of the partitions generated by the algorithm are compared to existing methods. Results show that this approach outperforms others in several metrics, achieving a 5% to 10% improvement in compactness, while maintaining reasonable performance, with a time overhead of up to approximately two seconds when splitting a polygon into ten parts.

关键词： partitioning algorithms Autonomous aerial vehicles Drones Robots Clustering algorithms Classification algorithms Shape Approximation algorithms Sensors Area measurement UAV multi-robot systems workspace division polygon partition

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Low-Overlap Point Cloud Registration by Semiglobal Block Matching

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2025年第3期21卷 2491-2500页

作者： Guo, Shiyi Wu, Yihong Xie, Binjian Liu, Bingxi Jia, Tong Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Peoples R China Chinese Acad Sci Inst Automat Beijing 100190 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Multimodal Artificial Intelligence S Beijing 100190 Peoples R China Southern Univ Sci & Technol Beijing Peoples R China Southern Univ Sci & Technol Shenzhen 518055 Peoples R China Peng Cheng Lab Shenzhen 518066 Peoples R China Northeastern Univ Minist Educ Key Lab Data Anal & Optimizat Smart Ind Shenyang 110819 Peoples R China

In recent years, the local feature-based point cloud registration methods have attracted more attention for their high robustness. The state-of-the-art local feature-based pipeline consists of local feature extraction, feature matching, and outlier rejection. However, the accuracy of this pipeline decreases significantly between low-overlap point clouds. In this article, we propose a novel hand-crafted framework to realize efficient low-overlap point cloud registration through point cloud partitioning, semiglobal point cloud block matching, and best transformation selection with refinement. Experimental results on benchmark datasets show that the proposed algorithm presents the superior performance over the previous methods on low-overlap scenes under different features and achieves very competitive accuracy on non-low-overlap data.

关键词： Point cloud compression Feature extraction Three-dimensional displays Accuracy Pipelines Robustness partitioning algorithms Informatics Image color analysis Convolution 3-D feature block matching low overlap point cloud registration

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Sparse Multi-View K-Means Clustering

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IEEE ACCESS 2025年 13卷 46773-46793页

作者： Yang, Miin-Shen Parveen, Shazia Chung Yuan Christian Univ Dept Appl Math Taoyuan 32023 Taiwan

In machine learning, k-means clustering is an unsupervised leaning technique to partition the data into k clusters that are homogeneous within the cluster and heterogeneous between clusters. The k-means algorithm assigns equal importance to all features in a single view. Several multi-view clustering techniques have been developed over the last few decades, including multi-view k-means (MVKM). In MVKM, patterns can be detected more accurately, and clustering performance is improved by applying each view individually for pattern discovery. The demand for highly accurate and flexible methods in machine learning and artificial intelligence has increased the use of multi-view datasets. These datasets are complicated and pose challenges for researchers in real-life situations. A novel type of sparse k-means clustering algorithm, sparse MVKM (S-MVKM), is proposed in this study for multi-view data. The main aim of our proposed S-MVKM algorithm is to achieve sparsity in features and shrink unimportant features to exactly zero using Lasso (least absolute shrinkage and selection operator). For considering theoretic behaviors of the S-MVKM algorithm, we create a convergence theorem to ensure that any convergent subsequence in S-MVKM will ultimately tend to an optimal solution. The proposed S-MVKM algorithm is then compared with several existing algorithms, such as traditional MVKM, feature reduction MVKM (FR-MVKM), MVKM with adaptive sparse membership weight allocation (M-VASM), and redundant and sparse feature learning (RSF-MVKM). Based on the three clustering performance measures of accuracy rate, Rand index, and normalized mutual information, we make the comparisons between them by using several synthetic and real-world datasets. The comparative results demonstrate that the proposed S-MVKM clustering algorithm effectively surpasses these competing clustering algorithms in terms of these three performance measures. Finally, we make concluding remarks that provide potential g

关键词： Clustering algorithms Linear programming Machine learning algorithms Convergence Accuracy Data mining Social networking (online) partitioning algorithms Internet of Things Industries K-means multi-view k-means (MVKM) sparsity sparse MVKM (S-MVKM) feature reduction performance measures

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Optimization of Depth Map Intra Coding algorithms Based on Machine Learning

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IEEE ACCESS 2025年 13卷 5846-5857页

作者： Song, Wenjun Yang, Jiaying Zhang, Qiuwen Zhengzhou Univ Light Ind Coll Comp & Commun Engn Zhengzhou 450002 Peoples R China

As a cutting-edge field of global research, 3D video technology faces the dual challenges of large data volumes and high processing complexity. Although the most recent video coding standard VVC, surpasses HEVC in coding efficiency, dedicated research on 3D video coding remains relatively scarce. Building on existing research, this study aims to develop a 3D video coding algorithm based on VVC that lowers the complexity of the encoding procedure. We focus specifically on the depth maps in 3D video content and introduce an extreme forest model from machine learning to optimize intra-frame coding. This paper proposes a novel CU partitioning strategy implemented through a two-stage extreme forest model. First, the initial model predicts the CU partitioning type, including no partition, QuadTree partitioning, Multi-type tree horizontal partitioning, and Multi-type tree vertical partitioning. For the latter two cases, a second model further refines the partitioning into binary or ternary trees. Through this two-stage prediction mechanism, we effectively bypass CU partitioning types with low probability, significantly reducing the coding complexity. The experimental results demonstrate that the proposed algorithm saves 47.46% in encoding time while maintaining coding quality, with only a 0.26% increase in Bjontegaard Delta Bitrate. This achievement provides an effective low-complexity solution for the 3D video coding field.

关键词： Encoding Prediction algorithms Complexity theory Video coding partitioning algorithms Three-dimensional displays Forestry Predictive models Adaptation models Classification algorithms Depth map intra-frame coding extreme forest

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Network-Wide Data Collection Based on In-Band Network Telemetry for Digital Twin Networks

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IEEE TRANSACTIONS ON MOBILE COMPUTING 2025年第1期24卷 86-101页

作者： Wang, Zhihao Jiang, Dingde Mumtaz, Shahid Univ Elect Sci & Technol China Chengdu 611731 Peoples R China Univ Elect Sci & Technol China Shenzhen Inst Adv Study Sch Informat & Commun Engn Chengdu 611731 Peoples R China Lab Electromagnet Space Cognit & Intelligent Contr Beijing 100091 Peoples R China Sichuan Conservatory Mus Key Lab Sichuan Prov Digital Media Art Chengdu 610041 Peoples R China Nottingham Trent Univ Dept Comp Sci Nottingham NG1 4FQ England Kyung Hee Univ Dept Elect Engn Yongin 17104 South Korea

The Digital Twin Network (DTN) establishes a real-time virtual mirror of physical networks. Data collection plays an essential role in DTN, which collects the status data of physical network for building highly consistent digital twins. In this paper, we present a network-wide data collection scheme based on In-band Network Telemetry (INT). To build a lifelike mirror of the physical network, the probing path set is required to cover all links so that network topology, traffic load, and port-level device information is captured. We present a Latency-aware High-degree Replicated First (LHRF) vertex-cut graph partitioning algorithm to partition the network into several balanced subgraphs while trying to replicate the high-degree vertexes among partitions first. LHRF aims to balance the length and accumulated latency of the probing paths. With shorter and stabler probing latencies, the information received by digital twin can reflect the latest and consistent network-wide status. To prevent the packets from being fragmented due to overlong paths, a deep limited search (DLS) based path planning algorithm is employed to generate non-overlapped probing paths covering all edges in the separated subgraphs. Simulation results demonstrate that the proposed scheme generates more balanced INT paths with constrained path length and shorter, stabler probing delay.

关键词： Data collection Telemetry Path planning Accuracy Digital twins partitioning algorithms Artificial intelligence Network telemetry digital twin network graph partition data collection probing path

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Accelerating Distributed Repartition Joins on Skewed Datasets via Patch-Based Shuffling

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IEEE ACCESS 2025年 13卷 41068-41096页

作者： Kassela, Evdokia Konstantinou, Ioannis Koziris, Nectarios Natl & Tech Univ Athens Sch Elect & Comp Engn Athens 11527 Greece Univ Thessaly Dept Informat & Telecommun Lamia 35100 Greece

In distributed workloads involving joins and aggregations, skewed attribute values often cause load balancing issues, leading to stragglers and increased execution times. Existing solutions often rely on cost-based models, require extensive parameter tuning, or necessitate modifications to distributed execution engines, limiting their usability and generality. To address this challenge, we present a novel patch-based repartitioning algorithm that eliminates load imbalances while minimizing network overhead. Our approach extends the subset-replicate technique by leveraging data distribution and location statistics to optimize data locality and reduce unnecessary data movement. Unlike traditional hash-based methods, our technique is skew-insensitive, requires no parameter tuning, and integrates seamlessly into existing distributed execution engines as a drop-in replacement for the shuffle mechanism. The proposed method operates in three distinct stages: (1) statistics collection and load threshold computation, (2) patch-based subgroup assignment to ensure optimal load balancing with minimal replication, and (3) informed data shuffling and join execution. This structured process ensures even workload distribution across workers while reducing network I/O. Theoretical analysis proves the scalability, skew robustness, and load-balancing guarantees of our approach, establishing bounds on maximum worker load and network data movement. Experimental evaluations demonstrate that our method achieves perfectly balanced workloads and reduces execution time by up to 81% compared to conventional hash-based joins under moderate to high skew, while introducing negligible overhead at low skew levels. These improvements are attributed to reduced data movement, optimized worker utilization, and the algorithm's robust theoretical foundation. Our research provides a versatile and practical solution for skewed data processing, significantly advancing the efficiency of distributed data man

关键词： Engines partitioning algorithms Load management Distributed databases Costs Telecommunication traffic Optimization Logic Load modeling Histograms Load balancing repartition join shuffling subset-replicate

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Group Synchronization of Heterogeneous Multiagent System With Hierarchical Structure and Beyond Pairwise Interactions

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2025年第3期55卷 1578-1590页

作者： Chen, Lin Xia, Yuanqing Zhang, Yuan Beijing Inst Technol Sch Automat Beijing 100081 Peoples R China Zhongyuan Univ Technol Zhengzhou 450007 Henan Peoples R China

Systems of different types of agents coupled with high-order interactions can emerge complex synchronization patterns. This article presents an analysis framework to study the group synchronization of heterogeneous multiagent systems with hierarchical structure and directed interactions of arbitrary order. First, directed hypergraph is introduced to describe the arbitrary-order interactions among the agents. Then, to explore the possible group synchronization patterns, the agent partition methods commonly used in dyadic graphs are extended to the directed hypergraph. Meanwhile, to verify the admissibility of the group synchronization patterns, two equitable partition types of agent partition are defined from the perspective of hyperedge partition. Next, using the simultaneous block diagonalization method, the stability problem of the group synchronous solution is decoupled into several independent subblocks. These subblocks are associated with the perturbations along and transverse to the synchronous manifold, with transverse subblocks related to loss of synchronization. Therefore, the stability of the group synchronous solution can be investigated by evaluating the stability of each transverse subblock via the master stability function method, thus significantly reducing the stability calculation complexity. Finally, the effectiveness of the analysis framework is illustrated via the simulation.

关键词： Synchronization Multi-agent systems Stability criteria Circuit stability partitioning algorithms Neurons Nonhomogeneous media Tensors Social networking (online) Mathematical models Directed hypergraph group synchronization heterogeneous multiagent systems high-order interactions master stability function (MSF)

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Fuzzy Min-Cut With Soft Balancing Effects

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IEEE TRANSACTIONS ON FUZZY SYSTEMS 2025年第2期33卷 767-778页

作者： Chen, Huimin Zhang, Runxin Wang, Rong Nie, Feiping Northwestern Polytech Univ Sch Comp Sci Sch Artificial Intelligence Opt & Elect iOPEN Xian 710072 Peoples R China Northwestern Polytech Univ Key Lab Intelligent Interact & Applicat Minist Ind & Informat Technol Xian 710072 Peoples R China Northwestern Polytech Univ Sch Artificial Intelligence OPt & Elect iOPEN Xian 710072 Peoples R China

The clustering algorithm has always been a hot spot in machine learning, which has made great progress and been widely used in different scenarios. Due to the characteristics and requirements of some application scenarios, the branch of the balanced clustering algorithm has been developed. The ideal of these algorithms is to obtain clusters containing approximately the same number of samples. However, when there are data points distributed at the boundary of different clusters, resulting in different probabilities of their belonging, hard-partitioned balanced clustering may not be able to handle these boundary data well, thus limiting their performance. Motivated by this, we propose a Fuzzy Min-Cut with Soft Balancing Effects (FCBE) method in this article. Specifically, the FCBE model utilizes fuzzy constraints to simultaneously enhance the ability of the balanced algorithm to capture boundary data members and the advantage of directly obtaining the partitioning results of graph-cut problem without postprocessing. In addition, a sparse regularization is introduced to avoid trivial solutions and maintain the separability of the relationship matrix. Furthermore, the proposed FCBE method can be viewed as a flexibly adjustable generalization pattern that not only has clear interpretability but also can become special cases with clear physical meanings under different parameter values. The feasibility of FCBE has been verified on real datasets.

关键词： Clustering algorithms partitioning algorithms Fuzzy systems Vectors Sparse matrices Switches Data models Clustering methods Wireless sensor networks Uncertainty Fuzzy constraints soft-balanced sparse regularization spectral clustering (SC)

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