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Motif-based embedding label propagation algorithm for community detection

INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS 2022年第3期37卷 1880-1902页

作者： Li, Chunying Tang, Yong Tang, Zhikang Cao, Jinli Zhang, Yanchun GuangDong Polytech Normal Univ Sch Comp Sci Guangzhou 510665 Peoples R China South China Normal Univ Sch Comp Guangzhou 510631 Peoples R China LA TROBE Univ Sch Engn & Math Sci Melbourne Vic Australia Guangzhou Univ Cyberspace Inst Adv Technol Guangzhou Peoples R China Oujiang Lab Zhejiang Lab Regenerat Med Vis & Brain Hlth Wenzhou Peoples R China

Community detection can exhibit the aggregation behavior of complex networks. Network motifs are the fundamental building blocks which can reveal the higher-order structure of complex networks. Label propagation algorithm has the advantage of approximately linear time complexity, unfortunately, the randomness of label update is a major but unsolved issue. For these reasons, this paper proposes a novel community detection method, named motif-based embedding label propagation algorithm (MELPA). First, complex network topology is reconstructed by merging higher-order topology with lower-order connectivity features, where higher-order topology is captured by mining network motifs. Second, We design a label propagation characteristic model according to nodes influence, then a new label update rule is formulated based on reconstructed weighted network, the rule integrates frequency among neighbor labels, influence of nodes, propagation characteristics and closeness of nodes to update the node label, the purpose is to overcome the randomness of label selection and identify a better and more stable community structure. Finally, extensive experiments on synthetic networks and real-world complex networks are conducted to verify the effectiveness of MELPA, especially for the complex networks with unobvious community structure, MELPA will get unexpected results.

关键词： clustering algorithm community detection label propagation network motif

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A Multi-attribute Large Group Decision-making Method Based on Interval-valued Pythagorean Fuzzy Number

A Multi-attribute Large Group Decision-making Method Based o...

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2024 International Conference on Machine Learning and Intelligent Computing, MLIC 2024

作者： You, Xinshang Zhou, Zhe School of Economics and Management Hebei University of Science and Technology Shijiazhuang050000 China

Firstly, a large group clustering algorithm based on data similarity is proposed, which can set different thresholds to cluster the decision results of expert groups. Secondly, the interval-valued pythagorean fuzzy number (IVPFN) hesitancy accuracy function and hesitancy score function are proposed. And based on the consideration of the distance between centroids and rectangular area in the geometric meaning of IVPFN, a calculation formula is proposed to distinguish different IVPFNs. Thirdly, use the above formula to construct a weight calculation model for evaluation criteria with adjustment coefficients. The decision matrix is weighted and its relative distance from the positive and negative ideal solutions is calculated to produce the final ranking. Finally, the cultural tourism project decision-making problem is analyzed as an arithmetic example and compared with the methods in the literature of other related fields to illustrate the rationality and scientificity of this paper. © 2024 X. You & Z. Zhou.

关键词： clustering algorithm cultural tourism project interval-valued pythagorean fuzzy number multi-attribute large group decision-making

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FADOHS: Framework for Detection and Integration of Unstructured Data of Hate Speech on Facebook Using Sentiment and Emotion Analysis

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IEEE ACCESS 2022年 10卷 22400-22419页

作者： Rodriguez, Axel Yi-Ling Chen Argueta, Carlos Natl Taiwan Univ Sci & Technol Dept Comp Sci & Informat Engn Taipei 106335 Taiwan Soul Hackers Labs Hsinchu 30074 Taiwan

Hate speech is a form of expression that assaults a person or a community based on race, origin, religion, sexual orientation, or other attributes. Although it can be expressed in multiple ways, both online and offline, the increasing popularity of social media has exponentially increased both its use and severity. Therefore, the aim of this research is to locate and analyze the unstructured data of selected social media posts that intend to spread hate in the comment sections. To address this issue, we propose a novel framework called FADOHS, which combines data analysis and natural language processing strategies, to sensitize all social media providers to the pervasiveness of hate on social media. Specifically, we use sentiment and emotion analysis algorithms to analyze recent posts and comments on these pages. Posts suspected of containing dehumanizing words will be processed before fed to the clustering algorithm for further evaluation. According to the experimental results, the proposed FADOHS framework is able to surpass the state-of-the-art approach in terms of precision, recall, and F1 scores by approximately 10%.

关键词： Hate speech Social networking (online) Dictionaries clustering algorithms Sentiment analysis Machine learning algorithms Licenses Emotion recognition clustering algorithm sentiment analysis data mining

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Effectiveness of array of lightning triggering towers as regional lightning protection for transmission lines in areas with strong lightning activity

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ENERGY REPORTS 2022年 8卷 5246-5255页

作者： Deng, Yeqiang Wang, Yu Chen, Xiaoyue Wen, Xishan Lan, Lei Xiang, Meichen Hao, Pan Ma, Yi Wuhan Univ Sch Elect Engn & Automat Wuhan Peoples R China State Grid Xiangyang Power Supply Cpmpany Xiangyang Peoples R China Yunnan Power Grid Co Ltd Elect Power Res Inst Kunming Peoples R China

The strong and frequent lightning activity in Guangdong, China, severely affects the safe operation of the power grid. In this study, the lightning database of Guangdong Power Grid in Foshan from 2008 to 2020 was used to compare the CG lightning activity intensity parameters in the areas of four high towers before and after the towers began operation. The lightning frequency within a certain distance from the high towers and the lightning current amplitude around the high towers were higher after the construction of the towers . The clustering method was used to count the number of strong thunderstorms and ground-flash activity processes. The temporal and spatial variations in the aforementioned parameters were also obtained. The high towers increased the number of strong cloud-to-ground activity processes in their adjacent areas, increased the proportion of cloud to ground activity trajectories close to the towers, and changed the direction of lightning corridors. Moreover, the lightning current amplitude of the cloud to ground CG lightning activity was decreased away from the tower. What is more, from the statistical results of operation data, it can be seen that the number of lightning tripping of transmission lines in the area of the towers is significantly reduced.(C) 2022 The Authors. Published by Elsevier Ltd.

关键词： Cloud-to-ground CG lightning activity Lightning location system clustering algorithm Array of high towers Regional lightning protection

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Cluster mechanism for sensing data report using robust collaborative distributed spectrum sensing

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2022年第4期25卷 2541-2556页

作者： Mokhtar, Rania A. Saeed, Rashid A. Alhumyani, Hesham Khayyat, Mashael Abdel-Khalek, S. Taif Univ Coll Comp & Informat Technol Dept Comp Engn POB 11099 At Taif 21944 Saudi Arabia Univ Jeddah Dept Informat Syst & Technol Coll Comp Sci & Engn Jeddah Saudi Arabia Taif Univ Dept Math Coll Sci POB 11099 At Taif 21944 Saudi Arabia

In cooperative mobile communications, spectrum sensing performance may encounter difficulties, which alert with many reporting errors, especially in dense network scenarios. In such networks, the decision fusion process for cooperating users becomes very complex, which requires sensing heavy traffic that needs a large bandwidth. To enhance the reliability of robust cooperative spectrum sensing, the paper proposed a new data fusion scheme based on clustering algorithm and distributed detection, in addition to an adapted threshold based on controlled false alarm probability. The proposed algorithm is dedicated to a highly Rayleigh faded environment to improves the channel errors. The results show that the use of two stages process of distribution clusters and selection fusion node (FN)s gives 0.42 error improvement. The results of the receiver operating characteristic (ROC) curve show an improvement in both false alarms and detection probabilities. Moreover, the sensitivity is also enhanced by 0.95.

关键词： clustering algorithm Spectrum sensing Cooperative communications Distributed scheme False alarm probability Data fusion Cognitive radio Detection probability

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A K-Means clustering-Based Hybrid Offspring Generation Mechanism in Evolutionary Multi-Objective Optimization

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IEEE ACCESS 2021年 9卷 167642-167651页

作者： Wang, Yuhong Zhang, Yi Changzhou Univ Sch Mech Engn Changzhou 213164 Jiangsu Peoples R China

Model-based recombination operators ignore individual quality information, while genetic-based differential evolution (DE) operators lack the extraction and use of global information. This makes it impossible for the single offspring generation method to always achieve excellent performance on various optimization problems. In order to solve the above problems, the K-means clustering-based hybrid offspring generation mechanism multi-objective evolutionary algorithm (KMDEA) is proposed. KMDEA performs K-means clustering on the population, and builds a multivariate Gaussian model based on the clustering results to discover the global information (the regularity property) of the population. For realizing the fusion of global and individual information, this paper designs a new hybrid offspring generation mechanism (KMD mechanism) to extract and use local individual information. Compared with a variety of mainstream multi-objective evolutionary algorithms (MOEAs), the results show that KMDEA has obvious advantages in solving multi-objective optimization problems (MOPs) with complex characteristics.

关键词： Optimization Statistics Sociology clustering algorithms Evolutionary computation Hybrid power systems Sorting Evolutionary algorithm multi-objective optimization clustering algorithm hybrid recombination operator information fusion

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clustering by Constructing Hyper-Planes

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IEEE ACCESS 2021年 9卷 70167-70181页

作者： Diao, Luhong Deng, Manman Gao, Jinying Beijing Univ Technol Beijing Inst Sci & Engn Comp Beijing 100022 Peoples R China Beijing Univ Technol Fac Sci Coll Math Beijing 100022 Peoples R China

As a ubiquitous method in the field of machine learning, clustering algorithm attracts a lot attention. Because only some basic information can be utilized, clustering data points into correct categories is a critical task especially when the cluster number is unknown. This paper presents an algorithm which can find the cluster number automatically. It firstly constructs hyper-planes based on the marginal of sample points. Then an adjacent relationship between data points is defined. Based on it, connective components are derived. According to a validity index proposed in this paper, the high-qualified connective components are selected as cluster centers. Meanwhile, the clusters' number is also determined. Another contribution of this paper is that all the parameters in this algorithm can be set automatically. To evaluate its robustness, experiments on different kinds of benchmark datasets are carried out. They show that the performances are even better than some other methods' best results which are selected manually.

关键词： clustering algorithms Manifolds Indexes Machine learning algorithms Support vector machines Machine learning Licenses clustering algorithm hyper-planes support vector machine validity index

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Wind Power and Photovoltaic Scene Generation and Reduction Method Based on Improved Gaussian Mixture Model of Cuckoo Search 13

Wind Power and Photovoltaic Scene Generation and Reduction M...

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13th International Conference of Information and Communication Technology, ICTech 2024

作者： Han, Bo Ni, Xueyan Tang, Rui Yu, Chengxin Shenyang Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Shenyang China State Grid Liaoning Ultra High Voltage Company Shenyang China

ISBN: (纸本)9798350376258

A method for generating and reducing distributed power generation output scenarios based on improved clustering analysis is proposed to address the issues of low accuracy and susceptibility to local optima in typical Gaussian mixture model clustering. Using the K-means clustering results as the initial value, the cuckoo search algorithm is used to solve for the optimal solution of GMM parameters, forming the CS-GMM improved clustering algorithm. Based on Latin hypercube median sampling, generate a large number of distributed power generation output scenarios, use CS-GMM algorithm to complete scene reduction, and obtain the reduced scenario results and the proportion of each scenario. This article verifies through experiments that the CS-GMM algorithm outperforms other comparison algorithms and has good application prospects by comparing the silhouette coefficient, Davies-Bouldin index and Calinski Harabasz score after clustering. ©2024 IEEE.

关键词： clustering algorithm cuckoo search distributed generation Gaussian mixture model scene generation and reduction

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PCB Defect Detection Method Based on Transformer-YOLO

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IEEE ACCESS 2022年 10卷 129480-129489页

作者： Chen, Wei Huang, Zhongtian Mu, Qian Sun, Yi Xian Univ Sci & Technol Sch Commun & Informat Engn Xian 710054 Shaanxi Peoples R China Xian Key Lab Network Convergence Commun Xian 710054 Shaanxi Peoples R China

In order to solve the problem of low accuracy and efficiency in printed circuit board(PCB) defect detection using reference methods, a Transformer-YOLO network detection model is proposed. Firstly, an improved clustering algorithm is used to generate the anchor box suitable for the PCB defect data set of this paper. Secondly, abandoning the traditional idea of using convolutional neural network to extract image feature, Swin Transformer is used as the feature extraction network, which can effectively establish the dependency between image features. Finally, to modify the order of the channels in the feature map and enable the network to more effectively focus on the information with greater value, the convolution and attention mechanism module is added to the feature detection network component. Comparing the network model proposed in this paper with Faster R-CNN, SSD, YOLOv3, YOLOv4 and YOLOv5, the experimental results show that the proposed model improves the accuracy by 23.90%, 15.51%, 10.70%, 7.83% and 6.12% respectively, which is better than other most mainstream target detection models and has relatively small volume.

关键词： PCB defects clustering algorithm convolutional neural network Swin Transformer attention mechanism

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An improved bat algorithm with velocity weight and curve decreasing

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JOURNAL OF SUPERCOMPUTING 2022年第10期78卷 12461-12475页

作者： Ye, Yun Zhao, Xiaojuan Xiong, Lu Guangdong Ind Polytech Guangzhou 510300 Peoples R China Zhongkai Univ Agr & Engn Coll Automat Guangzhou 510070 Guangdong Peoples R China

A bat algorithm (BA) is a heuristic algorithm that operates by imitating the echolocation behavior of bats to perform global optimization, which has fast convergence, a simple structure, and strong search ability. One of the issues in the standard bat algorithm is the premature convergence that can occur due to the low exploration ability of the algorithm under some conditions. To overcome this problem, the paper proposes a hybrid approach to improving its local search mechanism. The local search strategy of curve decreasing and speed weight is introduced to the standard bat algorithm to enhance its exploration and exploitation capabilities. The performance of the improved bat algorithm has better global optimization ability and higher convergence accuracy than the standard bat algorithm.

关键词： clustering algorithm Bat algorithm Curve decreasing strategy Speed weight

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