{"title":"精英数学家协作网络的社群检测","authors":"Yurui Huang, Zimo Wang, Chaolin Tian, Yifang Ma","doi":"10.2478/jdis-2024-0026","DOIUrl":null,"url":null,"abstract":"Purpose This study focuses on understanding the collaboration relationships among mathematicians, particularly those esteemed as elites, to reveal the structures of their communities and evaluate their impact on the field of mathematics. Design/methodology/approach Two community detection algorithms, namely Greedy Modularity Maximization and Infomap, are utilized to examine collaboration patterns among mathematicians. We conduct a comparative analysis of mathematicians’ centrality, emphasizing the influence of award-winning individuals in connecting network roles such as Betweenness, Closeness, and Harmonic centrality. Additionally, we investigate the distribution of elite mathematicians across communities and their relationships within different mathematical sub-fields. Findings The study identifies the substantial influence exerted by award-winning mathematicians in connecting network roles. The elite distribution across the network is uneven, with a concentration within specific communities rather than being evenly dispersed. Secondly, the research identifies a positive correlation between distinct mathematical sub-fields and the communities, indicating collaborative tendencies among scientists engaged in related domains. Lastly, the study suggests that reduced research diversity within a community might lead to a higher concentration of elite scientists within that specific community. Research limitations The study’s limitations include its narrow focus on mathematicians, which may limit the applicability of the findings to broader scientific fields. Issues with manually collected data affect the reliability of conclusions about collaborative networks. Practical implications This study offers valuable insights into how elite mathematicians collaborate and how knowledge is disseminated within mathematical circles. Understanding these collaborative behaviors could aid in fostering better collaboration strategies among mathematicians and institutions, potentially enhancing scientific progress in mathematics. Originality/value The study adds value to understanding collaborative dynamics within the realm of mathematics, offering a unique angle for further exploration and research.","PeriodicalId":44622,"journal":{"name":"Journal of Data and Information Science","volume":"4 1","pages":""},"PeriodicalIF":1.5000,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Community detection on elite mathematicians’ collaboration network\",\"authors\":\"Yurui Huang, Zimo Wang, Chaolin Tian, Yifang Ma\",\"doi\":\"10.2478/jdis-2024-0026\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Purpose This study focuses on understanding the collaboration relationships among mathematicians, particularly those esteemed as elites, to reveal the structures of their communities and evaluate their impact on the field of mathematics. Design/methodology/approach Two community detection algorithms, namely Greedy Modularity Maximization and Infomap, are utilized to examine collaboration patterns among mathematicians. We conduct a comparative analysis of mathematicians’ centrality, emphasizing the influence of award-winning individuals in connecting network roles such as Betweenness, Closeness, and Harmonic centrality. Additionally, we investigate the distribution of elite mathematicians across communities and their relationships within different mathematical sub-fields. Findings The study identifies the substantial influence exerted by award-winning mathematicians in connecting network roles. The elite distribution across the network is uneven, with a concentration within specific communities rather than being evenly dispersed. Secondly, the research identifies a positive correlation between distinct mathematical sub-fields and the communities, indicating collaborative tendencies among scientists engaged in related domains. Lastly, the study suggests that reduced research diversity within a community might lead to a higher concentration of elite scientists within that specific community. Research limitations The study’s limitations include its narrow focus on mathematicians, which may limit the applicability of the findings to broader scientific fields. Issues with manually collected data affect the reliability of conclusions about collaborative networks. Practical implications This study offers valuable insights into how elite mathematicians collaborate and how knowledge is disseminated within mathematical circles. Understanding these collaborative behaviors could aid in fostering better collaboration strategies among mathematicians and institutions, potentially enhancing scientific progress in mathematics. Originality/value The study adds value to understanding collaborative dynamics within the realm of mathematics, offering a unique angle for further exploration and research.\",\"PeriodicalId\":44622,\"journal\":{\"name\":\"Journal of Data and Information Science\",\"volume\":\"4 1\",\"pages\":\"\"},\"PeriodicalIF\":1.5000,\"publicationDate\":\"2024-08-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Data and Information Science\",\"FirstCategoryId\":\"91\",\"ListUrlMain\":\"https://doi.org/10.2478/jdis-2024-0026\",\"RegionNum\":3,\"RegionCategory\":\"管理学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"INFORMATION SCIENCE & LIBRARY SCIENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Data and Information Science","FirstCategoryId":"91","ListUrlMain":"https://doi.org/10.2478/jdis-2024-0026","RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"INFORMATION SCIENCE & LIBRARY SCIENCE","Score":null,"Total":0}
Community detection on elite mathematicians’ collaboration network
Purpose This study focuses on understanding the collaboration relationships among mathematicians, particularly those esteemed as elites, to reveal the structures of their communities and evaluate their impact on the field of mathematics. Design/methodology/approach Two community detection algorithms, namely Greedy Modularity Maximization and Infomap, are utilized to examine collaboration patterns among mathematicians. We conduct a comparative analysis of mathematicians’ centrality, emphasizing the influence of award-winning individuals in connecting network roles such as Betweenness, Closeness, and Harmonic centrality. Additionally, we investigate the distribution of elite mathematicians across communities and their relationships within different mathematical sub-fields. Findings The study identifies the substantial influence exerted by award-winning mathematicians in connecting network roles. The elite distribution across the network is uneven, with a concentration within specific communities rather than being evenly dispersed. Secondly, the research identifies a positive correlation between distinct mathematical sub-fields and the communities, indicating collaborative tendencies among scientists engaged in related domains. Lastly, the study suggests that reduced research diversity within a community might lead to a higher concentration of elite scientists within that specific community. Research limitations The study’s limitations include its narrow focus on mathematicians, which may limit the applicability of the findings to broader scientific fields. Issues with manually collected data affect the reliability of conclusions about collaborative networks. Practical implications This study offers valuable insights into how elite mathematicians collaborate and how knowledge is disseminated within mathematical circles. Understanding these collaborative behaviors could aid in fostering better collaboration strategies among mathematicians and institutions, potentially enhancing scientific progress in mathematics. Originality/value The study adds value to understanding collaborative dynamics within the realm of mathematics, offering a unique angle for further exploration and research.
期刊介绍:
JDIS devotes itself to the study and application of the theories, methods, techniques, services, infrastructural facilities using big data to support knowledge discovery for decision & policy making. The basic emphasis is big data-based, analytics centered, knowledge discovery driven, and decision making supporting. The special effort is on the knowledge discovery to detect and predict structures, trends, behaviors, relations, evolutions and disruptions in research, innovation, business, politics, security, media and communications, and social development, where the big data may include metadata or full content data, text or non-textural data, structured or non-structural data, domain specific or cross-domain data, and dynamic or interactive data.
The main areas of interest are:
(1) New theories, methods, and techniques of big data based data mining, knowledge discovery, and informatics, including but not limited to scientometrics, communication analysis, social network analysis, tech & industry analysis, competitive intelligence, knowledge mapping, evidence based policy analysis, and predictive analysis.
(2) New methods, architectures, and facilities to develop or improve knowledge infrastructure capable to support knowledge organization and sophisticated analytics, including but not limited to ontology construction, knowledge organization, semantic linked data, knowledge integration and fusion, semantic retrieval, domain specific knowledge infrastructure, and semantic sciences.
(3) New mechanisms, methods, and tools to embed knowledge analytics and knowledge discovery into actual operation, service, or managerial processes, including but not limited to knowledge assisted scientific discovery, data mining driven intelligent workflows in learning, communications, and management.
Specific topic areas may include:
Knowledge organization
Knowledge discovery and data mining
Knowledge integration and fusion
Semantic Web metrics
Scientometrics
Analytic and diagnostic informetrics
Competitive intelligence
Predictive analysis
Social network analysis and metrics
Semantic and interactively analytic retrieval
Evidence-based policy analysis
Intelligent knowledge production
Knowledge-driven workflow management and decision-making
Knowledge-driven collaboration and its management
Domain knowledge infrastructure with knowledge fusion and analytics
Development of data and information services