SCOREH+:用于群落检测的基于特征向量比的高阶节点邻近度谱聚类算法

IF 7.5 3区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS IEEE Transactions on Big Data Pub Date : 2023-12-25 DOI:10.1109/TBDATA.2023.3346715
Yanhui Zhu;Fang Hu;Lei Hsin Kuo;Jia Liu
{"title":"SCOREH+:用于群落检测的基于特征向量比的高阶节点邻近度谱聚类算法","authors":"Yanhui Zhu;Fang Hu;Lei Hsin Kuo;Jia Liu","doi":"10.1109/TBDATA.2023.3346715","DOIUrl":null,"url":null,"abstract":"The research on complex networks has achieved significant progress in revealing the mesoscopic features of networks. Community detection is an important aspect of understanding real-world complex systems. We present in this paper a High-order node proximity Spectral Clustering on Ratios-of-Eigenvectors (SCOREH+) algorithm for locating communities in complex networks. The algorithm improves SCORE and SCORE+ and preserves high-order transitivity information of the network affinity matrix. We optimize the high-order proximity matrix from the initial affinity matrix using the Radial Basis Functions (RBFs) and Katz index. In addition to the optimization of the Laplacian matrix, we implement a procedure that joins an additional eigenvector (the \n<inline-formula><tex-math>$(k+1){\\rm th}$</tex-math></inline-formula>\n leading eigenvector) to the spectrum domain for clustering if the network is considered to be a “weak signal” graph. The algorithm has been successfully applied to both real-world and synthetic data sets. The proposed algorithm is compared with state-of-art algorithms, such as ASE, Louvain, Fast-Greedy, Spectral Clustering (SC), SCORE, and SCORE+. To demonstrate the high efficacy of the proposed method, we conducted comparison experiments on eleven real-world networks and a number of synthetic networks with noise. The experimental results in most of these networks demonstrate that SCOREH+ outperforms the baseline methods. Moreover, by tuning the RBFs and their shaping parameters, we may generate state-of-the-art community structures on all real-world networks and even on noisy synthetic networks.","PeriodicalId":13106,"journal":{"name":"IEEE Transactions on Big Data","volume":"10 3","pages":"301-312"},"PeriodicalIF":7.5000,"publicationDate":"2023-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"SCOREH+: A High-Order Node Proximity Spectral Clustering on Ratios-of-Eigenvectors Algorithm for Community Detection\",\"authors\":\"Yanhui Zhu;Fang Hu;Lei Hsin Kuo;Jia Liu\",\"doi\":\"10.1109/TBDATA.2023.3346715\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The research on complex networks has achieved significant progress in revealing the mesoscopic features of networks. Community detection is an important aspect of understanding real-world complex systems. We present in this paper a High-order node proximity Spectral Clustering on Ratios-of-Eigenvectors (SCOREH+) algorithm for locating communities in complex networks. The algorithm improves SCORE and SCORE+ and preserves high-order transitivity information of the network affinity matrix. We optimize the high-order proximity matrix from the initial affinity matrix using the Radial Basis Functions (RBFs) and Katz index. In addition to the optimization of the Laplacian matrix, we implement a procedure that joins an additional eigenvector (the \\n<inline-formula><tex-math>$(k+1){\\\\rm th}$</tex-math></inline-formula>\\n leading eigenvector) to the spectrum domain for clustering if the network is considered to be a “weak signal” graph. The algorithm has been successfully applied to both real-world and synthetic data sets. The proposed algorithm is compared with state-of-art algorithms, such as ASE, Louvain, Fast-Greedy, Spectral Clustering (SC), SCORE, and SCORE+. To demonstrate the high efficacy of the proposed method, we conducted comparison experiments on eleven real-world networks and a number of synthetic networks with noise. The experimental results in most of these networks demonstrate that SCOREH+ outperforms the baseline methods. Moreover, by tuning the RBFs and their shaping parameters, we may generate state-of-the-art community structures on all real-world networks and even on noisy synthetic networks.\",\"PeriodicalId\":13106,\"journal\":{\"name\":\"IEEE Transactions on Big Data\",\"volume\":\"10 3\",\"pages\":\"301-312\"},\"PeriodicalIF\":7.5000,\"publicationDate\":\"2023-12-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Transactions on Big Data\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10373106/\",\"RegionNum\":3,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, INFORMATION SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Big Data","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10373106/","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 0

摘要

复杂网络研究在揭示网络的中观特征方面取得了重大进展。群落检测是理解现实世界复杂系统的一个重要方面。本文提出了一种在复杂网络中定位群落的高阶节点邻近特征向量比谱聚类算法(SCOREH+)。该算法改进了 SCORE 和 SCORE+,并保留了网络亲缘矩阵的高阶传递信息。我们利用径向基函数(RBF)和卡茨指数从初始亲和矩阵中优化高阶亲和矩阵。除了优化拉普拉斯矩阵外,如果网络被认为是一个 "弱信号 "图,我们还实施了一个程序,将一个额外的特征向量($(k+1){\rm th}$ 领先特征向量)加入频谱域进行聚类。该算法已成功应用于现实世界和合成数据集。该算法与 ASE、Louvain、Fast-Greedy、Spectral Clustering (SC)、SCORE 和 SCORE+ 等先进算法进行了比较。为了证明所提方法的高效性,我们在 11 个真实世界网络和一些带噪声的合成网络上进行了对比实验。其中大部分网络的实验结果表明,SCOREH+ 的性能优于基线方法。此外,通过调整 RBF 及其整形参数,我们可以在所有真实世界网络甚至是有噪声的合成网络上生成最先进的社区结构。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
SCOREH+: A High-Order Node Proximity Spectral Clustering on Ratios-of-Eigenvectors Algorithm for Community Detection
The research on complex networks has achieved significant progress in revealing the mesoscopic features of networks. Community detection is an important aspect of understanding real-world complex systems. We present in this paper a High-order node proximity Spectral Clustering on Ratios-of-Eigenvectors (SCOREH+) algorithm for locating communities in complex networks. The algorithm improves SCORE and SCORE+ and preserves high-order transitivity information of the network affinity matrix. We optimize the high-order proximity matrix from the initial affinity matrix using the Radial Basis Functions (RBFs) and Katz index. In addition to the optimization of the Laplacian matrix, we implement a procedure that joins an additional eigenvector (the $(k+1){\rm th}$ leading eigenvector) to the spectrum domain for clustering if the network is considered to be a “weak signal” graph. The algorithm has been successfully applied to both real-world and synthetic data sets. The proposed algorithm is compared with state-of-art algorithms, such as ASE, Louvain, Fast-Greedy, Spectral Clustering (SC), SCORE, and SCORE+. To demonstrate the high efficacy of the proposed method, we conducted comparison experiments on eleven real-world networks and a number of synthetic networks with noise. The experimental results in most of these networks demonstrate that SCOREH+ outperforms the baseline methods. Moreover, by tuning the RBFs and their shaping parameters, we may generate state-of-the-art community structures on all real-world networks and even on noisy synthetic networks.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
CiteScore
11.80
自引率
2.80%
发文量
114
期刊介绍: The IEEE Transactions on Big Data publishes peer-reviewed articles focusing on big data. These articles present innovative research ideas and application results across disciplines, including novel theories, algorithms, and applications. Research areas cover a wide range, such as big data analytics, visualization, curation, management, semantics, infrastructure, standards, performance analysis, intelligence extraction, scientific discovery, security, privacy, and legal issues specific to big data. The journal also prioritizes applications of big data in fields generating massive datasets.
期刊最新文献
Guest Editorial TBD Special Issue on Graph Machine Learning for Recommender Systems Reliable Data Augmented Contrastive Learning for Sequential Recommendation Denoised Graph Collaborative Filtering via Neighborhood Similarity and Dynamic Thresholding Higher-Order Smoothness Enhanced Graph Collaborative Filtering AKGNN: Attribute Knowledge Graph Neural Networks Recommendation for Corporate Volunteer Activities
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1