{"title":"Exploring publication networks with a local cohesion-maximizing algorithm","authors":"Matthias Held, Jochen Gläser","doi":"10.1162/qss_a_00314","DOIUrl":null,"url":null,"abstract":"\n Global algorithms have taken precedence in bibliometrics as approaches to the reconstruction of topics from networks of publications. They partition a large set of publications and the resulting disjoint clusters are then interpreted as individual topics. This is at odds with a sociological understanding of topics as formed by the participants working on and being influenced by them, an understanding that is best operationalized by algorithms prioritizing cohesion rather than separation, by using local information and by allowing topics to overlap. Thus, a different kind of algorithm is needed for topic reconstruction to be successful. Local algorithms represent a promising solution. In this paper, we present for consideration a new Multilayered, Adjustable, Local Bibliometric Algorithm (MALBA), which is in line with sociological definitions of topics and reconstructs dense regions in bibliometric networks locally. MALBA grows a subgraph from a publications seed either by interacting with a fixed network dataset, or by querying an online database to obtain up-to-date linkage information. New candidates for addition are evaluated by assessing the links in two data models. Experiments with publications on the h-index and with ground truth data positioned in a dataset of AMO physics illustrate the properties of MALBA and its potential.\n \n \n https://www.webofscience.com/api/gateway/wos/peer-review/10.1162/qss_a_00314\n","PeriodicalId":34021,"journal":{"name":"Quantitative Science Studies","volume":null,"pages":null},"PeriodicalIF":4.1000,"publicationDate":"2024-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Quantitative Science Studies","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1162/qss_a_00314","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"INFORMATION SCIENCE & LIBRARY SCIENCE","Score":null,"Total":0}
引用次数: 0
Abstract
Global algorithms have taken precedence in bibliometrics as approaches to the reconstruction of topics from networks of publications. They partition a large set of publications and the resulting disjoint clusters are then interpreted as individual topics. This is at odds with a sociological understanding of topics as formed by the participants working on and being influenced by them, an understanding that is best operationalized by algorithms prioritizing cohesion rather than separation, by using local information and by allowing topics to overlap. Thus, a different kind of algorithm is needed for topic reconstruction to be successful. Local algorithms represent a promising solution. In this paper, we present for consideration a new Multilayered, Adjustable, Local Bibliometric Algorithm (MALBA), which is in line with sociological definitions of topics and reconstructs dense regions in bibliometric networks locally. MALBA grows a subgraph from a publications seed either by interacting with a fixed network dataset, or by querying an online database to obtain up-to-date linkage information. New candidates for addition are evaluated by assessing the links in two data models. Experiments with publications on the h-index and with ground truth data positioned in a dataset of AMO physics illustrate the properties of MALBA and its potential.
https://www.webofscience.com/api/gateway/wos/peer-review/10.1162/qss_a_00314
作为从出版物网络中重建主题的方法,全局算法在文献计量学中占据了主导地位。它们对大量出版物进行分区,然后将由此产生的不相连的聚类解释为单个主题。这与社会学对主题的理解相悖,社会学认为主题是由研究主题并受其影响的参与者形成的,而这种理解的最佳操作方式是算法优先考虑内聚而非分离,使用本地信息并允许主题重叠。因此,要想成功进行话题重构,需要一种不同的算法。本地算法是一种很有前途的解决方案。在本文中,我们提出了一种新的多层、可调整、本地文献计量算法(MALBA)供大家参考,该算法符合社会学对主题的定义,可在本地重建文献计量网络中的密集区域。MALBA 通过与固定的网络数据集交互,或通过查询在线数据库以获取最新的链接信息,从出版物种子中生成子图。通过评估两个数据模型中的链接来评估新的候选添加子图。利用 h 指数上的出版物和 AMO 物理数据集中的地面实况数据进行的实验说明了 MALBA 的特性及其潜力。https://www.webofscience.com/api/gateway/wos/peer-review/10.1162/qss_a_00314。