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A Seed Growth Algorithm for Local clustering in Complex Networks

IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 2024年第6期11卷 5878-5891页

作者： Tsai, Feng-Sheng Hsu, Sheng-Yi Shih, Mau-Hsiang China Med Univ Dept Biomed Informat Taichung 404328 Taiwan China Med Univ Hosp Res Ctr Interneural Comp Taichung 404327 Taiwan Ever Fortune AI Co Ltd Taichung 406040 Taiwan China Med Univ China Med Univ Hosp Res Ctr Interneural Comp Taichung 404327 Taiwan

A seed growth algorithm based on a local connectivity rule for cluster formation in complex networks is introduced. That accompanies with the cluster normalization algorithm, the parameter determination process, and the pseudocluster inference process, forming the coherent algorithms and formalizing the categories within the realm of the cluster space. The prime clusters can be extracted from the cluster space, so that the overlapping complexity of clusters is confined to the prime clusters. To decide unequivocally whether the coherent algorithms are efficient, we have to simulate on the overlapping stochastic block networks. Our simulation shows that the dice coefficient of the prime cluster corresponding to the overlapping target cluster is 0.978 +/- 0.024 on average. It decodes the underlying meaning that the coherent algorithms can efficiently search out the prime clusters containing almost the same nodes as the overlapping clusters. It provides a firm foundation for a simulation on the Caenorhabditis elegans neuronal network, unraveling the neurons DD04, PDB, VA08, VB06, VB07, VD07, and VD08 lying in the major overlap of clusters, among them the ablation of DD04 and PDB in biological experiments has shown to result in a pronounced loss of controllability of motor behavior.

关键词： clustering algorithms Neurons Partitioning algorithms Inference algorithms Complex networks Linear programming Stochastic processes Caenorhabditis elegans neuronal networks local cluster formation locomotor control cluster overlap stochastic block networks

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PPA-DBSCAN: Privacy-Preserving ρ-Approximate Density-Based clustering

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IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING 2024年第6期21卷 5324-5340页

作者： Fu, Jiaxuan Cheng, Ke Chang, Zhao Shen, Yulong Xidian Univ Sch Comp Sci & Technol Xian 710071 Shaanxi Peoples R China Xian Univ Posts & Telecommun Shaanxi Key Lab Informat Commun Network & Secur Xian 710121 Shaanxi Peoples R China

clustering is widely used for data analysis that partitions a set of data into multiple clusters, where objects in the same cluster have similar properties. Data for clustering analysis often comes from different data sources, which makes it important to maintain data privacy. However, existing privacy-preserving clustering schemes either require the support of prior knowledge or are just applicable for small datasets due to impractical costs. To solve this issue, we follow a classical approximate DBSCAN clustering algorithm and adapt it to the privacy-preserving context. Concretely, to construct our secure approximate clustering algorithm, we propose a series of basic secure computation protocols among additively secret-shared values. In addition, we design a crypto-friendly grid partitioning method based on which an efficient and privacy-preserving approximation DBSCAN scheme is derived. Theoretical analysis and experimental results show that our scheme achieves almost the same cluster quality compared to the plain-text exact DBSCAN. Our extensive experiments on different datasets demonstrate that our scheme is accurate and efficient.

关键词： clustering algorithms Protocols Approximation algorithms Data privacy Time complexity Privacy Partitioning algorithms clustering DBSCAN privacy-preserving secret sharing

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ELBCFVD: An Efficient Low-Energy Balanced clustering Algorithm Based on Fast Voronoi Division for Mobile Sensor Networks

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IEEE SENSORS JOURNAL 2024年第13期24卷 21583-21594页

作者： Qi, Weiwei Xia, Yu Ma, Tao Zhu, Liucun Zhu, Junwu Yangzhou Univ Sch Informat Engn Yangzhou 225000 Peoples R China Beibu Gulf Univ Adv Sci & Technol Res Inst Qinzhou 535011 Peoples R China Yangzhou Univ Sch Informat Engn Yangzhou 225000 Peoples R China

Energy efficiency and balanced load are essential for the development of clustering algorithms for mobile wireless sensor networks (MWSNs). Aiming at the problems of uneven load distribution and high overhead of command interaction in existing clustering schemes, we propose an efficient low-energy balanced clustering based on the fast Voronoi diagram division algorithm. The innovation of this algorithm is that by judging the information value of the data acquired by the nodes, selecting the representative nodes that satisfy the fast solution of the Voronoi diagram division scheme, optimizing the reconfiguration process of the clusters together with the correspondence of the stable workflows in the mobile scenarios, effectively reducing the overall amount of interaction of the control commands, and realizing the redistribution of the network's energy consumption and balancing the routing loads of various parts of the network. Detailed simulation analysis shows that the proposed efficient low-energy balanced clustering based on fast Voronoi division (ELBCFVD) algorithm significantly reduces the frequency of cluster reconstruction and cluster head (CH) rotation, balances the communication load among clusters, and achieves 104.4%, 45.8%, and 15.4% network lifetime extension compared to low energy adaptive clustering hierarchy-mobile (LEACH-M), energy-efficient mobility based CH selection mechanism (EEMCS), and hierarchical layer-balanced clustering scheme (HLBC) algorithms, respectively.

关键词： clustering algorithms Sensors Energy consumption Protocols Heuristic algorithms Data communication Intelligent sensors Graph partitioning hierarchical clustering mobile wireless sensor network (MWSN) workload balance

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Differentiable clustering for Graph Attention

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2024年第8期36卷 3751-3764页

作者： Zhou, Haicang He, Tiantian Ong, Yew-Soon Cong, Gao Chen, Quan Nanyang Technol Univ Sch Comp Sci & Engn Singapore 639798 Singapore ASTAR Inst High Performance Comp Singapore Inst Mfg Technol Ctr Frontier AI Res Singapore 138632 Singapore ASTAR Singapore 138632 Singapore Singtel Cognit & Artificial Intelligence Lab Enter Singapore 639798 Singapore

Graph clusters (or communities) represent important graph structural information. In this paper, we present Differentiable clustering for graph ATtention (DCAT). To the best of our knowledge, DCAT is the first solution that incorporates graph clustering into graph attention networks (GAT) to learn cluster-aware attention scores for semi-supervised learning tasks. In DCAT, we propose a novel approach to formulating graph clustering as an auxiliary differentiable objective based on modularity maximization, which can be optimized together with the learning objective of GAT for a semi-supervised task. Specifically, we propose a solution to relaxing modularity maximization from a discrete optimization problem to a differentiable objective with theoretical guarantee so that we can learn cluster-aware attention scores by jointly learning from graph clustering and a semi-supervised learning task. To address the computational challenge, we further propose to reformulate the constraint introduced by the clustering objective into a new form. Our analysis shows that DCAT allocates higher attention scores to nodes within the same cluster, allowing them to have a higher influence in node representation learning, and thus DCAT will generate better node representations for downstream applications. The experimental results on commonly used datasets show that DCAT outperforms popular and state-of-the-art graph neural networks.

关键词： Task analysis Semisupervised learning Optimization clustering algorithms Supervised learning Representation learning Image color analysis Graph neural networks attention mechanism graph attention networks graph clustering semi-supervised learning

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Large-Scale Decentralized UAV Full-Duplex Ad-Hoc Networking: Architecture, clustering and Access

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IEEE NETWORK 2024年第6期38卷 275-281页

作者： Liu, Suning Ye, Liang Jia, Hanbo Qu, Guanqiao Luo, Xiaomeng Meng, Weixiao Harbin Inst Technol Sch Elect & Informat Engn Harbin Peoples R China Harbin Inst Technol Harbin Peoples R China

Owing to its high mobility and flexible topology, unmanned aerial vehicles (UAV) are envisioned to play an important role in both civil and military fields to enable agile and ubiquitous connectivity. With the development of the B5G/6G communication technologies, the demand for wireless communication services is experiencing a significant increase. Consequently, the spectrum resources are becoming increasingly scarce. However, almost existing full-duplex UAV networking schemes still use the centralized architecture, which is fragile in the circumstances where communication conditions are unpleasant. Thus motivated, we propose a novel decentralized full-duplex UAV ad-hoc network architecture to improve its invulnerability and spectrum efficiency. Then we design the efficient clustering and multiple access protocols respectively under the full-duplex pattern to improve the packet transmission rate and reduce the decentralized network access time effectively. We also conduct a preliminary simulation study to validate the feasibility of the adopted architecture. Finally, we explore the primary challenges and potential avenues for the decentralized full-duplex UAV adhoc networks in conjunction with other cutting-edge technologies.

关键词： Full-duplex system Autonomous aerial vehicles Ad hoc networks clustering algorithms Relays Time division multiple access Magnetic heads

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Outlier Indicator Based Projection Fuzzy K-Means clustering for Hyperspectral Image

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IEEE SIGNAL PROCESSING LETTERS 2025年 32卷 496-500页

作者： Liu, Xinze Yang, Xiaojun Zhang, Jiale Wang, Jing Nie, Feiping Guangdong Univ Technol Sch Informat Engn Guangzhou 510006 Peoples R China Guangdong Univ Technol Guangdong Prov Key Lab Informat Photon Technol China Key Lab Photon Technol Integrated Sensing & Minist Educ China Guangzhou 510006 Peoples R China Northwestern Polytech Univ Sch Comp Sci Xian 710072 Peoples R China Northwestern Polytech Univ Ctr OPT IMagery Anal & Learning OPTIMAL Xian 710072 Peoples R China

The application of hyperspectral image (HSI) clustering has become widely used in the field of remote sensing. Traditional fuzzy K-means clustering methods often struggle with HSI data due to the significant levels of noise, consequently resulting in segmentation inaccuracies. To address this limitation, this letter introduces an innovative outlier indicator-based projection fuzzy K-means clustering (OIPFK) algorithm for clustering of HSI data, enhancing the efficacy and robustness of previous fuzzy K-means methodologies through a two-pronged strategy. Initially, an outlier indicator vector is constructed to identify noise and outliers by computing the distances between each data point in a reduced dimensional space. Subsequently, the OIPFK algorithm incorporates the fuzzy membership relationships between samples and clustering centers within this lower-dimensional framework, along with the integration of the outlier indicator vectors, to significantly mitigates the influence of noise and extraneous features. Moreover, an efficient iterative optimization algorithm is employed to address the optimization challenges inherent to OIPKM. Experimental results from three real-world hyperspectral image datasets demonstrate the effectiveness and superiority of our proposed method.

关键词： Hyperspectral imaging Optimization Noise Vectors clustering algorithms Robustness Linear programming Signal processing algorithms Photonics Indexes Projection fuzzy K-means hyperspectral image robustness outliers clustering

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A clustering Election Game-Based and Two-Level Management Protocol for Wireless Sensor Networks

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IEEE INTERNET OF THINGS JOURNAL 2024年第7期11卷 12058-12070页

作者： Cai, Lingru Huang, Ruisong Li, Zhangjie Luo, Lun Xiong, Zhi Chen, Yindong Shantou Univ Dept Comp Sci Shantou 515063 Peoples R China

Energy balance consumption is an important research topic in the field of wireless sensor networks (WSNs). clustering protocols are widely used to reduce WSN energy consumption. However, this many-to-one cluster head (CH) model can also easily lead to energy exhaustion of some critical nodes near the base station results in the decline of the network quality and lifetime. This article proposes a clustering election game-based and two-level management clustering protocol (CEGT) for WSNs;the network is divided into layers of equal size, and the layer heads (LHs) are selected within the layers based on the location and energy information. Each layer is further divided into clusters of equal size, and the nodes within the clusters undergo a two-stage screening followed by local clustering and multiple rounds of election games to dynamically select the staged CHs. This protocol effectively prevents low-energy nodes from participating in the CH competitions, and selects high-quality nodes through election games which are based on customized qualification values, and ensures reasonable energy utilization as well as efficient network load balancing. Finally, our simulations show that our CEGT protocol outperforms other clustering protocols in terms of the network lifetime, the number of surviving nodes, and the average number of clusters, and the throughput, which also indicate that the CEGT protocol is more suitable for large-scale networks.

关键词： Wireless sensor networks Voting Protocols Games clustering algorithms Energy consumption Routing clustering election game clustering protocol energy balance two-level management (CEGT) wireless sensor networks (WSNs)

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A Self-Representation Weighted-Based Density Peaks clustering Method

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IEEE ACCESS 2024年 12卷 142015-142025页

作者： Yu, Qiangguo Zhang, Zhikun Feng, Yanan Wei, Yuzheng Jia, Liangquan Huzhou Coll Qiuzhen Coll Huzhou 313000 Zhejiang Peoples R China Huzhou Univ Huzhou 313000 Zhejiang Peoples R China

The approach to calculating density significantly impacts the clustering efficacy of the Density Peak clustering (DPC) method, with various density calculation methods tailored for different datasets. To address this, this study introduces a Self-Representation Weighted Density Peak clustering (SR-DPC) method. Unlike traditional DPC, SR-DPC not only utilizes the local data point information but also amplifies the impact of different data points on the data center by implementing a weighting strategy, enhancing the precision with which data centers are identified. Moreover, SR-DPC adaptively reflects the influence of diverse data points on the data centers via feature representation and employs a weighted Gaussian kernel distance instead of the Euclidean distance to boost its clustering capabilities. Experimental evaluations on both synthetic and real datasets demonstrate the effectiveness and practicality of the SR-DPC method.

关键词： Euclidean distance Density measurement clustering methods clustering algorithms Shape measurement Representation learning Gaussian processes Feature detection DPC self-representation weighted feature representation adaptively the weighted Gaussian kernel distance

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Event Graph-Based News clustering: The Role of Named Entity-Centered Subgraphs

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IEEE ACCESS 2024年 12卷 105613-105632页

作者： Komecoglu, Basak Buluz Yilmaz, Burcu Gebze Tech Univ Inst Informat Technol TR-41400 Gebze Kocaeli Turkiye

In an era of exponential growth in online news sources, the need for intelligent digital solutions capable of efficiently analyzing and organizing large amounts of news content has become crucial. This paper presents a graph-based methodology designed to enhance Topic Detection and Tracking (TDT) tasks in natural language processing by efficiently clustering news events into coherent stories. The proposed approach leverages a novel event graph model that captures not only the characteristics of individual news events but also their collective narrative context. Using Named Entity Centred Frequent Subgraphs, the model excels in identifying recurring patterns of events and thus provides a framework for learning a robust, language-independent, and structured representation for structuring news stories, which represents a significant advance in the refinement of traditional clustering algorithms. Empirical experiments using a multilingual benchmark dataset, the News clustering Dataset, highlight the superior clustering performance of our approach compared to state-of-the-art monolingual document clustering techniques, particularly in English and the competitive results in Spanish. To underline the adaptability of the methodology to low-resource languages, the Turkish 'Story-Based News Dataset' developed specifically for this study also promises to serve as an important resource for a wide range of natural language processing tasks.

关键词： Task analysis clustering algorithms Vectors Context modeling Computational modeling Analytical models Semantics Natural language processing Text processing Frequent subgraph mining low-resource language natural language processing text clustering

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Decentralized big data mining: federated learning for clustering youth tobacco use in India

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JOURNAL OF BIG DATA 2024年第1期11卷 1-26页

作者： Haripriya, Rahul Khare, Nilay Pandey, Manish Biswas, Sreemoyee Maulana Azad Natl Inst Technol Dept Comp Sci & Engn Bhopal 462003 MP India

This study examines the smoking patterns of youth across various states and union territories of India using the Global Youth Tobacco Survey (GYTS) dataset. The analysis employs three clustering algorithms K-Means, DBSCAN, and Hierarchical clustering within a federated learning framework, which ensures that sensitive public health data remains decentralized and private. Federated learning enables collaborative analysis across different regions by sharing only model parameters rather than raw data, thus enhancing privacy. Furthermore, the integration of differential privacy ensures additional protection by adding controlled noise to the model parameters, safeguarding individual-level data from exposure during the learning process. The study highlights the varying performances of the clustering algorithms, revealing valuable insights into regional smoking behaviors and the effectiveness of government anti-tobacco campaigns. These insights offer important guidance for public health authorities, allowing for the design and implementation of more targeted and effective campaigns tailored to the needs of specific regions. By leveraging federated learning and differential privacy, this study demonstrates a privacy-preserving approach to analyzing large-scale public health data, providing a blueprint for future health interventions and tobacco control strategies in India and beyond.

关键词： Big data Federated learning for big data Big data privacy Federated learning Decentralized data mining clustering algorithms Privacy preservation Machine learning AI in public health

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