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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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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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Time-Aware and Direction-Constrained Collective Spatial Keyword Query

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2025年第3期26卷 3039-3055页

作者： Feng, Zhe Li, Guohui Li, Jianjun Jin, Changlong Du, Xiaokun Huazhong Univ Sci & Technol Sch Comp Sci & Technol Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Software Wuhan 430074 Peoples R China Shandong Univ Sch Mech Elect & Informat Engn Weihai 250100 Peoples R China South Cent Minzu Univ Coll Comp Sci Wuhan 430079 Peoples R China

Collective spatial keyword query (CoSKQ) is an important variant of spatial keyword queries and has become a research hotspot. In real life, user behavior usually has a certain directionality, so they may want to obtain the result object that conforms to a specific direction, which is what the direction-constrained query studies. In addition, query time information also plays an important role in location-based query processing. To this end, this paper takes the lead in studying the Time-aware and Direction-constrained Collective Spatial Keyword Query (TDCoSKQ). To facilitate direction-related operations, space objects are organized using the polar coordinate system. Firstly, an efficient space partition method is designed, and on this basis, a new hybrid index structure KRPQT is designed. Based on KRPQT, several pruning strategies are proposed to prune irrelevant regions and objects from the perspective of keyword, time, and direction, and the basic algorithm KRPQB is proposed. To further improve the efficiency of query processing, the possible areas of the result objects are analyzed and shrunk to greatly reduce the number of candidate objects, and three optimization algorithms KRPSW, KRPSW+LFO, and KRPSW+KRPQB are proposed. Then, we discuss how to extend the proposed methods to deal with TDCoSKQ queries with other distance functions and TDCoSKQ queries with weight objects. Finally, the efficiency of the proposed algorithm is verified by simulation experiments.

关键词： Query processing Roads Lead partitioning algorithms Indexes Optimization Intelligent transportation systems Collective spatial keyword queries direction-constrained time-aware polar coordinate space

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A Task Scheduling Method for Minimizing Completion Time in Edge Collaboration Environment

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IEEE INTERNET OF THINGS JOURNAL 2025年第5期12卷 5423-5434页

作者： Zhao, Hui Zhang, Chao Lu, Xiaoqin Wang, Jing Yang, Pengfei Wan, Bo Wang, Quan Xidian Univ Key Lab Smart Human Comp Interact & Wearable Techn Xian 710071 Peoples R China Xidian Univ Sch Comp Sci & Technol Xian 710071 Peoples R China

In the edge computing environment, the uneven geographical distribution of tasks may lead to unbalanced load on the edge server. In addition, some larger tasks are difficult to completely offload to edge servers, which cannot fully utilize edge server resources. To solve the above problems, we propose a task scheduling method to minimize the completion time by combining the horizontal edge collaboration and fine-grained task partial offloading technology. First, combining horizontal edge collaboration and fine-grained task partial offloading technology, considering the location relationship between users and edge servers in multiuser multiedge server scenario, a task partial offloading optimization problem is established to minimize task completion time. Second, due to the nonconvex and variables coupling, we decompose the original problem into resource allocation, user-server association, and offloading strategy subproblems. A task scheduling algorithm based on improved teaching-learning-based optimization (ITLBO) is proposed to obtain the best task scheduling decision which includes task offloading location and offloading ratio. Simulation results show that the proposed method can effectively reduce the task completion time in edge collaboration environment.

关键词： Servers Collaboration Optimization Resource management Cloud computing Scheduling Internet of Things Energy consumption Edge computing partitioning algorithms Edge collaboration partial offloading task scheduling teaching-learning-based optimization

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A Fast CU Partition Algorithm for AVS3 Based on Adaptive Tree Search and Pruning Optimization

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IEEE TRANSACTIONS ON BROADCASTING 2025年第1期71卷 125-141页

作者： Yin, Jihang Qi, Honggang Zhong, Liang Zhao, Zhiyuan Wang, Qiang Wu, Jingran Zhang, Xianguo Univ Chinese Acad Sci Sch Comp Sci & Technol Beijing 101408 Peoples R China Tencent Shannon Lab CAPD TEG Shenzhen 518000 Guangdong Peoples R China

In the third generation of the Audio Video Coding Standard (AVS3), the size of Coding Tree Units (CTUs) has been expanded to four times larger than the previous generation, and more Coding Unit (CU) partition modes have been introduced, enhancing adaptability and efficiency in video encoding. CU partition in AVS3 not only brings improvements in encoding performance but also significantly increases the computational complexity, posing substantial challenges to real-time encoding. We propose a fast algorithm for CU partition, which features adaptive tree search and pruning optimization. Firstly, it adjusts the tree search order based on neighbor CU and lookahead information. Specifically, the analysis order of sub-blocks and parent blocks is adaptively adjusted: the potential optimal partition is prioritized, the non-optimal partitions are deferred, and an optimized order of first-full-then-sub or first-sub-then-full is selected. Secondly, the pruning optimization algorithm utilizes analyzed information to skip non-optimal partitions to reduce computational complexity. Due to the adjusted tree search order and the prioritization of potential optimal partitions, more analyzed information becomes available when evaluating non-optimal partitions, thereby improving the recall and precision rates of non-optimal partitions detection, saving more time, and introducing negligible loss in coding performance. The proposed algorithm has been implemented in the open-source encoder uavs3e. Experimental results indicate that under the three encoding configurations of AI, LD B, and RA, the algorithm achieves significant time saving of 51.41%, 40.57%, and 40.57%, with BDBR increases of 0.64%, 1.61%, and 1.04%, respectively. These results outperform the state-of-the-art fast CU partition algorithms.

关键词： Encoding partitioning algorithms Costs Standards Optimization Streaming media Channel coding Video coding Prediction algorithms Real-time systems AVS3 video coding fast cu partition tree search

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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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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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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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