[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-82611-en":3,"doc-seo-82611-105":29,"detail-sidebar-cat-0-en-105":90},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":13,"seo_description":14,"update_tm":27,"read_time":28},82611,8796095360427,"Lucas Martin","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","The Evolution of the Peridynamics Community in Its First Quarter Century","Peridynamics is a rapidly expanding branch of continuum mechanics, developed about a quarter century ago for simulating fracture. This study tracks how the peridynamics research community evolved from its beginnings by analyzing publication co-authorship patterns. Year-by-year co-authorship networks are built for 2000–2024, with network-level metrics describing field-wide change and node-level metrics revealing the most collaborative scientists. Network deviations after 2019 motivate a country-based analysis to assess COVID-19 impacts.","arXiv :2607 .0 146 1v 1 [ cs . SI] 1 Jul 2026  \nThe Evolution of the Peridynamics Community in Its First Quarter Century  \nBiraj Dahal 1 and Pablo Seleson2*  \n1 School of Mathematics, Georgia Institute of Technology, North Ave NW, Atlanta, GA, 30332, USA.  \n2 Computer Science and Mathematics Division, Oak Ridge National Laboratory, P.O. Box 2008, MS-6013, Oak Ridge, TN, 37831-6013, USA.  \n*Corresponding author. E-mail: [selesonpd@ornl.gov](selesonpd@ornl.gov) ; Contributing authors: [bdahal6@gatech.edu](bdahal6@gatech.edu) ;  \nAbstract  \nPeridynamics is a fast growing field of continuum mechanics, especially developed for the modeling and simulation of fracture problems, initiated a quarter century ago. In this study, we analyze the evolution of the peridynamics community since its inception in terms of publication co-authorship. For this purpose, we construct a peridynamics co-authorship network for each year from 2000 to 2024 and perform network analysis based on selected metrics. Nodes represent scientists, and links connect co-authoring scientists with link weights representing the number of co-authorships (based on the total number of co-authors per publication) . Network-level metrics are used to quantify the evolution of the field, and node-level metrics are used to identify trends in the most collaborative scientists in peridynamics. We noticed a deviation in network trends that occurred in the years since 2019, and we subsequently performed a country-based analysis with insights about the impact of the COVID-19 pandemic on the evolution of the peridynamics co-authorship network.  \nKeywords: Peridynamics, Co-authorship, Network science, Country-based analysis, COVID-19  \n1  \n1 Introduction  \nPeridynamics is a field of continuum mechanics developed in 2000, which uses a nonlocal formulation that is especially suitable for modeling fracture (Silling, 2000) . Over the past quarter century, the field has grown to encompass thousands of scientists and research publications worldwide. In order to quantify the evolution of the peridynamics community, we employ network analysis to study the peridynamics co-authorship network from 2000 to 2024 . We construct a network for each year whose nodes represent authors of peridynamics publications, and whose links connect two nodes if the corresponding authors co-authored a peridynamics publication by that given year. Using this approach, we analyze the evolving network over the years. The peridynamics co-authorship network grew from 1 node in the year 2000 to thousands of nodes and links in the year 2024 . Network-level metrics are used to glean insights about the community as a whole (see Section 2), and node-level metrics are used to identify the most collaborative scientists (see Section 3) .  \nIn this paper, we analyze the peridynamics co-authorship network with an emphasis on 2020-2024, an extension of our previous analysis of the peridynamics co-authorship network until 2019 (Dahal, Seleson, & Trageser, 2023) . The major differences between the current analysis and the previous analysis are discussed in Section 1.2. In the years since 2019, we noticed a deviation in network trends observed in our previous work, especially when considering the countries of the authors’ institutional affiliations, which seems to be a result of the COVID-19 pandemic; see Section 4 for more details.  \nThe rest of this paper is organized as follows. In the remainder of this section, we explain the data collection process (Section 1.1) and differences from our previous work (Section 1.2) . In Section 2, we perform a network-level analysis of the peridynamics coauthorship network. In Section 3, we perform a node-level analysis of the peridynamics co-authorship network. In Section 4, we analyze the countries of the institutional affiliations of new scientists joining the peridynamics co-authorship network. Finally, in Section 5, we summarize the main implications of this study.  \n1.1 Data collection  \nW","cbCaihpTypZFemPk","https://ap.wps.com/l/cbCaihpTypZFemPk","pdf",2516047,1,17,"English","en",105,"# Introduction\n## Data collection\n# Network-level analysis\n# Node-level analysis\n# Country-based analysis\n# Summary of implications","[{\"question\":\"How does the paper quantify the evolution of the peridynamics community?\",\"answer\":\"It constructs a peridynamics co-authorship network for each year from 2000 to 2024. Nodes are authors and weighted links represent co-authorship frequency derived from publication co-author lists.\"},{\"question\":\"What dataset is used to build the co-authorship networks?\",\"answer\":\"The dataset includes Scopus-indexed publications published no later than 2024 that contain “peridynamic” or “peridynamics” in the title, abstract, or author keywords. The data was downloaded on August 13, 2025.\"},{\"question\":\"What role does COVID-19 play in the study’s findings?\",\"answer\":\"The authors observe deviations in network trends occurring in the years since 2019. They then conduct a country-based analysis to evaluate how COVID-19 may have influenced the evolution of the peridynamics co-authorship network.\"}]",1784181803,43,{"code":4,"msg":30,"data":31},"ok",{"site_id":24,"language":23,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":85,"head_meta":87,"extra_data":89,"updated_unix":27},"the-evolution-of-the-peridynamics-community-in-its-first-quarter-century","",{"@graph":35,"@context":84},[36,53,67],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/the-evolution-of-the-peridynamics-community-in-its-first-quarter-century/82611/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":61,"encodingFormat":60,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-16",true,{"@type":64,"interactionType":65,"userInteractionCount":4},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"How does the paper quantify the evolution of the peridynamics community?","Question",{"text":74,"@type":75},"It constructs a peridynamics co-authorship network for each year from 2000 to 2024. Nodes are authors and weighted links represent co-authorship frequency derived from publication co-author lists.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"What dataset is used to build the co-authorship networks?",{"text":79,"@type":75},"The dataset includes Scopus-indexed publications published no later than 2024 that contain “peridynamic” or “peridynamics” in the title, abstract, or author keywords. The data was downloaded on August 13, 2025.",{"name":81,"@type":72,"acceptedAnswer":82},"What role does COVID-19 play in the study’s findings?",{"text":83,"@type":75},"The authors observe deviations in network trends occurring in the years since 2019. 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