{"title":"Central node identification via weighted kernel density estimation","authors":"","doi":"10.1007/s10618-024-01003-4","DOIUrl":null,"url":null,"abstract":"<h3>Abstract</h3> <p>The detection of central nodes in a network is a fundamental task in network science and graph data analysis. During the past decades, numerous centrality measures have been presented to characterize what is a central node. However, few studies address this issue from a statistical inference perspective. In this paper, we formulate the central node identification issue as a weighted kernel density estimation problem on graphs. Such a formulation provides a generic framework for recognizing central nodes. On one hand, some existing centrality evaluation metrics can be unified under this framework through the manipulation of kernel functions. On the other hand, more effective methods for node centrality assessment can be developed based on proper weighting coefficient specification. Experimental results on 20 simulated networks and 53 real networks show that our method outperforms both six prior state-of-the-art centrality measures and two recently proposed centrality evaluation methods. To the best of our knowledge, this is the first piece of work that addresses the central node identification issue via weighted kernel density estimation.</p>","PeriodicalId":55183,"journal":{"name":"Data Mining and Knowledge Discovery","volume":"21 1","pages":""},"PeriodicalIF":2.8000,"publicationDate":"2024-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Data Mining and Knowledge Discovery","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1007/s10618-024-01003-4","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
Abstract
The detection of central nodes in a network is a fundamental task in network science and graph data analysis. During the past decades, numerous centrality measures have been presented to characterize what is a central node. However, few studies address this issue from a statistical inference perspective. In this paper, we formulate the central node identification issue as a weighted kernel density estimation problem on graphs. Such a formulation provides a generic framework for recognizing central nodes. On one hand, some existing centrality evaluation metrics can be unified under this framework through the manipulation of kernel functions. On the other hand, more effective methods for node centrality assessment can be developed based on proper weighting coefficient specification. Experimental results on 20 simulated networks and 53 real networks show that our method outperforms both six prior state-of-the-art centrality measures and two recently proposed centrality evaluation methods. To the best of our knowledge, this is the first piece of work that addresses the central node identification issue via weighted kernel density estimation.
期刊介绍：
Advances in data gathering, storage, and distribution have created a need for computational tools and techniques to aid in data analysis. Data Mining and Knowledge Discovery in Databases (KDD) is a rapidly growing area of research and application that builds on techniques and theories from many fields, including statistics, databases, pattern recognition and learning, data visualization, uncertainty modelling, data warehousing and OLAP, optimization, and high performance computing.
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