Scott Leo Renshaw, Selena M. Livas, Miruna G. Petrescu-Prahova, Carter T. Butts
Abstract When subjected to a sudden, unanticipated threat, human groups characteristically self-organize to identify the threat, determine potential responses, and act to reduce its impact. Central to this process is the challenge of coordinating information sharing and response activity within a disrupted environment. In this paper, we consider coordination in the context of responses to the 2001 World Trade Center (WTC) disaster. Using records of communications among 17 organizational units, we examine the mechanisms driving communication dynamics, with an emphasis on the emergence of coordinating roles. We employ relational event models (REMs) to identify the mechanisms shaping communications in each unit, finding a consistent pattern of behavior across units with very different characteristics. Using a simulation-based “knock-out” study, we also probe the importance of different mechanisms for hub formation. Our results suggest that, while preferential attachment and pre-disaster role structure generally contribute to the emergence of hub structure, temporally local conversational norms play a much larger role in the WTC case. We discuss broader implications for the role of microdynamics in driving macroscopic outcomes, and for the emergence of coordination in other settings.
{"title":"Modeling complex interactions in a disrupted environment: Relational events in the WTC response","authors":"Scott Leo Renshaw, Selena M. Livas, Miruna G. Petrescu-Prahova, Carter T. Butts","doi":"10.1017/nws.2023.4","DOIUrl":"https://doi.org/10.1017/nws.2023.4","url":null,"abstract":"Abstract When subjected to a sudden, unanticipated threat, human groups characteristically self-organize to identify the threat, determine potential responses, and act to reduce its impact. Central to this process is the challenge of coordinating information sharing and response activity within a disrupted environment. In this paper, we consider coordination in the context of responses to the 2001 World Trade Center (WTC) disaster. Using records of communications among 17 organizational units, we examine the mechanisms driving communication dynamics, with an emphasis on the emergence of coordinating roles. We employ relational event models (REMs) to identify the mechanisms shaping communications in each unit, finding a consistent pattern of behavior across units with very different characteristics. Using a simulation-based “knock-out” study, we also probe the importance of different mechanisms for hub formation. Our results suggest that, while preferential attachment and pre-disaster role structure generally contribute to the emergence of hub structure, temporally local conversational norms play a much larger role in the WTC case. We discuss broader implications for the role of microdynamics in driving macroscopic outcomes, and for the emergence of coordination in other settings.","PeriodicalId":51827,"journal":{"name":"Network Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135927288","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
András György, Thomas Marlow, B. Abrahao, K. Makovi
The global and uneven spread of COVID-19, mirrored at the local scale, reveals stark differences along racial and ethnic lines. We respond to the pressing need to understand these divergent outcomes via neighborhood level analysis of mobility and case count information. Using data from Chicago over 2020, we leverage a metapopulation Susceptible-Exposed-Infectious-Removed model to reconstruct and simulate the spread of SARS-CoV-2 at the ZIP Code level. We demonstrate that exposures are mostly contained within one’s own ZIP Code and demographic group. Building on this observation, we illustrate that we can understand epidemic progression using a composite metric combining the volume of mobility and the risk that each trip represents, while separately these factors fail to explain the observed heterogeneity in neighborhood level outcomes. Having established this result, we next uncover how group level differences in these factors give rise to disparities in case rates along racial and ethnic lines. Following this, we ask what-if questions to quantify how segregation impacts COVID-19 case rates via altering mobility patterns. We find that segregation in the mobility network has contributed to inequality in case rates across demographic groups.
{"title":"Segregated mobility patterns amplify neighborhood disparities in the spread of COVID-19","authors":"András György, Thomas Marlow, B. Abrahao, K. Makovi","doi":"10.1017/nws.2023.6","DOIUrl":"https://doi.org/10.1017/nws.2023.6","url":null,"abstract":"\u0000 The global and uneven spread of COVID-19, mirrored at the local scale, reveals stark differences along racial and ethnic lines. We respond to the pressing need to understand these divergent outcomes via neighborhood level analysis of mobility and case count information. Using data from Chicago over 2020, we leverage a metapopulation Susceptible-Exposed-Infectious-Removed model to reconstruct and simulate the spread of SARS-CoV-2 at the ZIP Code level. We demonstrate that exposures are mostly contained within one’s own ZIP Code and demographic group. Building on this observation, we illustrate that we can understand epidemic progression using a composite metric combining the volume of mobility and the risk that each trip represents, while separately these factors fail to explain the observed heterogeneity in neighborhood level outcomes. Having established this result, we next uncover how group level differences in these factors give rise to disparities in case rates along racial and ethnic lines. Following this, we ask what-if questions to quantify how segregation impacts COVID-19 case rates via altering mobility patterns. We find that segregation in the mobility network has contributed to inequality in case rates across demographic groups.","PeriodicalId":51827,"journal":{"name":"Network Science","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2023-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"57044286","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Multilayer networks are in the focus of the current complex network study. In such networks, multiple types of links may exist as well as many attributes for nodes. To fully use multilayer—and other types of complex networks in applications, the merging of various data with topological information renders a powerful analysis. First, we suggest a simple way of representing network data in a data matrix where rows correspond to the nodes and columns correspond to the data items. The number of columns is allowed to be arbitrary, so that the data matrix can be easily expanded by adding columns. The data matrix can be chosen according to targets of the analysis and may vary a lot from case to case. Next, we partition the rows of the data matrix into communities using a method which allows maximal compression of the data matrix. For compressing a data matrix, we suggest to extend so-called regular decomposition method for non-square matrices. We illustrate our method for several types of data matrices, in particular, distance matrices, and matrices obtained by augmenting a distance matrix by a column of node degrees, or by concatenating several distance matrices corresponding to layers of a multilayer network. We illustrate our method with synthetic power-law graphs and two real networks: an Internet autonomous systems graph and a world airline graph. We compare the outputs of different community recovery methods on these graphs and discuss how incorporating node degrees as a separate column to the data matrix leads our method to identify community structures well-aligned with tiered hierarchical structures commonly encountered in complex scale-free networks.
{"title":"A network community detection method with integration of data from multiple layers and node attributes","authors":"H. Reittu, L. Leskelä, Tomi D. Räty","doi":"10.1017/nws.2023.2","DOIUrl":"https://doi.org/10.1017/nws.2023.2","url":null,"abstract":"\u0000 Multilayer networks are in the focus of the current complex network study. In such networks, multiple types of links may exist as well as many attributes for nodes. To fully use multilayer—and other types of complex networks in applications, the merging of various data with topological information renders a powerful analysis. First, we suggest a simple way of representing network data in a data matrix where rows correspond to the nodes and columns correspond to the data items. The number of columns is allowed to be arbitrary, so that the data matrix can be easily expanded by adding columns. The data matrix can be chosen according to targets of the analysis and may vary a lot from case to case. Next, we partition the rows of the data matrix into communities using a method which allows maximal compression of the data matrix. For compressing a data matrix, we suggest to extend so-called regular decomposition method for non-square matrices. We illustrate our method for several types of data matrices, in particular, distance matrices, and matrices obtained by augmenting a distance matrix by a column of node degrees, or by concatenating several distance matrices corresponding to layers of a multilayer network. We illustrate our method with synthetic power-law graphs and two real networks: an Internet autonomous systems graph and a world airline graph. We compare the outputs of different community recovery methods on these graphs and discuss how incorporating node degrees as a separate column to the data matrix leads our method to identify community structures well-aligned with tiered hierarchical structures commonly encountered in complex scale-free networks.","PeriodicalId":51827,"journal":{"name":"Network Science","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2023-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"57044106","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Scholars have explored the science of science from a networks perspective from the early days of the study of social networks. Price (1965) pioneered the methodology and theoretical import of citation networks. Crane (1969) examined the social structure among scientists to test the invisible college hypothesis wherein groups of researchers working in a common area shared informal ties with one another. Indeed, science has been described as “a complex, self-organizing, and evolving network of scholars, projects, papers, and ideas” (Fortunato et al., 2018, p. 1). Hence, it is not surprising that scientific networks play a significant role within the larger domain of network science, focusing on the relational nature of scientific endeavors. And by doing so they have contributed to advances in network science while also contributing to the emergent debates about the transformation of science. Recent trends in analysis of science transformation are focused on a rising demand for interdisciplinary collaboration, knowledge application, decreasing the gap between knowledge production and transfer to practice, and increasing interaction between science and other societal actors and spheres (industry and government). Research on scientific networks, with its relational nature, helps us to understand and enable these modern trends of science transformation across disciplines. It enables us to analyze themultidimensional networks encompassing scientists, scientific organizations, funding entities, publication outlets, and projects; to discover the reasons for their collaboration, integration, importance; and to measure their prestige, popularity, success, and social impact. In short, how and why collaborations form—and how they perform. In light of these intellectual developments, a group of scholars, led by Anuška Ferligoj, Valentina Kuskova, and Dmitry Zaytsev, convened an International Workshop on Scientific Networks in Moscow, Russia, on July 20–21, 2019. Another special session on scientific networks was held at the Seventh International Workshop on Social Network Analysis at the University of Salerno, Italy, on October 29–31, 2019. The papers presented at these workshops, and the ensuing intellectual dialog, led to the development of a call for papers to this special issue of Network Science. This special issue demonstrates the diversity of recent scholarship on scientific networks. This diversity is reflected in the types of scientific networks studied, the multiple theoretic frameworks utilized to formulate questions, the development, and deployment of advancedmethods to analyze them, and their applicability to various scientific disciplines and other fields. The articles in this special issue represent only a quarter of all inquiries we received in response to the advertisement for this special issue. Yet, they cover the wide range of possible types of scientific networks: the nature and effectiveness of collaboration networks, issues related to qu
从社会网络研究的早期开始，学者们就从网络的角度来探索科学。Price(1965)开创了引文网络的方法论和理论意义。Crane(1969)研究了科学家之间的社会结构，以检验隐形大学假说，即在公共领域工作的研究人员群体彼此之间有着非正式的联系。事实上，科学被描述为“一个复杂的、自组织的、不断发展的学者、项目、论文和思想网络”(Fortunato et al.， 2018, p. 1)。因此，科学网络在更大的网络科学领域发挥重要作用并不奇怪，重点关注科学努力的关系本质。通过这样做，他们为网络科学的进步做出了贡献，同时也为关于科学转型的新兴辩论做出了贡献。科学转型分析的最新趋势集中在对跨学科合作、知识应用、减少知识生产和转移到实践之间的差距以及增加科学与其他社会行为者和领域(工业和政府)之间的互动的日益增长的需求上。对科学网络及其关系性质的研究，有助于我们理解和实现这些跨学科科学转型的现代趋势。它使我们能够分析包括科学家、科学组织、资助实体、出版渠道和项目在内的多维网络;发现它们协作、融合、重要性的原因;并衡量他们的声望、受欢迎程度、成功程度和社会影响。简而言之，合作是如何形成的，为什么形成的，以及它们是如何表现的。鉴于这些智力发展，由Anuška Ferligoj、Valentina Kuskova和Dmitry Zaytsev领导的一组学者于2019年7月20日至21日在俄罗斯莫斯科召开了科学网络国际研讨会。2019年10月29日至31日，在意大利萨莱诺大学举行的第七届社会网络分析国际研讨会上举行了另一场关于科学网络的特别会议。在这些研讨会上发表的论文，以及随后的智力对话，导致了本期《网络科学》特刊的论文征集。本期特刊展示了近期科学网络学术研究的多样性。这种多样性反映在所研究的科学网络的类型，用于提出问题的多种理论框架，分析它们的先进方法的发展和部署，以及它们对各种科学学科和其他领域的适用性。本期特刊的文章只占我们收到的针对本期特刊广告的所有咨询的四分之一。然而，它们涵盖了广泛的可能的科学网络类型:合作网络的性质和有效性，与科学网络数据质量有关的问题，应用科学网络分析的新方法的发展，检查个别期刊对科学网络发展的影响，或整个领域的发展。
{"title":"Introduction to the special issue on scientific networks","authors":"Dmitry G. Zaytsev, N. Contractor","doi":"10.1017/nws.2023.7","DOIUrl":"https://doi.org/10.1017/nws.2023.7","url":null,"abstract":"Scholars have explored the science of science from a networks perspective from the early days of the study of social networks. Price (1965) pioneered the methodology and theoretical import of citation networks. Crane (1969) examined the social structure among scientists to test the invisible college hypothesis wherein groups of researchers working in a common area shared informal ties with one another. Indeed, science has been described as “a complex, self-organizing, and evolving network of scholars, projects, papers, and ideas” (Fortunato et al., 2018, p. 1). Hence, it is not surprising that scientific networks play a significant role within the larger domain of network science, focusing on the relational nature of scientific endeavors. And by doing so they have contributed to advances in network science while also contributing to the emergent debates about the transformation of science. Recent trends in analysis of science transformation are focused on a rising demand for interdisciplinary collaboration, knowledge application, decreasing the gap between knowledge production and transfer to practice, and increasing interaction between science and other societal actors and spheres (industry and government). Research on scientific networks, with its relational nature, helps us to understand and enable these modern trends of science transformation across disciplines. It enables us to analyze themultidimensional networks encompassing scientists, scientific organizations, funding entities, publication outlets, and projects; to discover the reasons for their collaboration, integration, importance; and to measure their prestige, popularity, success, and social impact. In short, how and why collaborations form—and how they perform. In light of these intellectual developments, a group of scholars, led by Anuška Ferligoj, Valentina Kuskova, and Dmitry Zaytsev, convened an International Workshop on Scientific Networks in Moscow, Russia, on July 20–21, 2019. Another special session on scientific networks was held at the Seventh International Workshop on Social Network Analysis at the University of Salerno, Italy, on October 29–31, 2019. The papers presented at these workshops, and the ensuing intellectual dialog, led to the development of a call for papers to this special issue of Network Science. This special issue demonstrates the diversity of recent scholarship on scientific networks. This diversity is reflected in the types of scientific networks studied, the multiple theoretic frameworks utilized to formulate questions, the development, and deployment of advancedmethods to analyze them, and their applicability to various scientific disciplines and other fields. The articles in this special issue represent only a quarter of all inquiries we received in response to the advertisement for this special issue. Yet, they cover the wide range of possible types of scientific networks: the nature and effectiveness of collaboration networks, issues related to qu","PeriodicalId":51827,"journal":{"name":"Network Science","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41309076","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Dmitry G. Zaytsev, Valentina V. Kuskova, Gregory S. Khvatsky, Anna A. Sokol
Abstract In this paper, we answer the multiple calls for systematic analysis of paradigms and subdisciplines in political science—the search for coherence within a fragmented field. We collected a large dataset of over seven hundred thousand writings in political science from Web of Science since 1946. We found at least two waves of political science development, from behaviorism to new institutionalism. Political science appeared to be more fragmented than literature suggests—instead of ten subdisciplines, we found 66 islands. However, despite fragmentation, there is also a tendency for integration in contemporary political science, as revealed by co-existence of several paradigms and coherent and interconnected topics of the “canon of political science,” as revealed by the core-periphery structure of topic networks. This was the first large-scale investigation of the entire political science field, possibly due to newly developed methods of bibliometric network analysis: temporal bibliometric analysis and island methods of clustering. Methodological contribution of this work to network science is evaluation of islands method of network clustering against a hierarchical cluster analysis for its ability to remove misleading information, allowing for a more meaningful clustering of large weighted networks.
{"title":"Searching for coherence in a fragmented field: Temporal and keywords network analysis in political science","authors":"Dmitry G. Zaytsev, Valentina V. Kuskova, Gregory S. Khvatsky, Anna A. Sokol","doi":"10.1017/nws.2022.39","DOIUrl":"https://doi.org/10.1017/nws.2022.39","url":null,"abstract":"Abstract In this paper, we answer the multiple calls for systematic analysis of paradigms and subdisciplines in political science—the search for coherence within a fragmented field. We collected a large dataset of over seven hundred thousand writings in political science from Web of Science since 1946. We found at least two waves of political science development, from behaviorism to new institutionalism. Political science appeared to be more fragmented than literature suggests—instead of ten subdisciplines, we found 66 islands. However, despite fragmentation, there is also a tendency for integration in contemporary political science, as revealed by co-existence of several paradigms and coherent and interconnected topics of the “canon of political science,” as revealed by the core-periphery structure of topic networks. This was the first large-scale investigation of the entire political science field, possibly due to newly developed methods of bibliometric network analysis: temporal bibliometric analysis and island methods of clustering. Methodological contribution of this work to network science is evaluation of islands method of network clustering against a hierarchical cluster analysis for its ability to remove misleading information, allowing for a more meaningful clustering of large weighted networks.","PeriodicalId":51827,"journal":{"name":"Network Science","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2023-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43990074","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract Is it always beneficial to create a new relationship (have a new follower/friend) in a social network? This question can be formally stated as a property of the centrality measure that defines the importance of the actors of the network. Score monotonicity means that adding an arc increases the centrality score of the target of the arc; rank monotonicity means that adding an arc improves the importance of the target of the arc relatively to the remaining nodes. It is known that most centralities are both score and rank monotone on directed, strongly connected graphs. In this paper, we study the problem of score and rank monotonicity for classical centrality measures in the case of undirected networks: in this case, we require that score, or relative importance, improves at both endpoints of the new edge. We show that, surprisingly, the situation in the undirected case is very different, and in particular that closeness, harmonic centrality, betweenness, eigenvector centrality, Seeley’s index, Katz’s index, and PageRank are not rank monotone; betweenness and PageRank are not even score monotone. In other words, while it is always a good thing to get a new follower, it is not always beneficial to get a new friend.
{"title":"Monotonicity in undirected networks","authors":"Paolo Boldi, Flavio Furia, Sebastiano Vigna","doi":"10.1017/nws.2022.42","DOIUrl":"https://doi.org/10.1017/nws.2022.42","url":null,"abstract":"Abstract Is it always beneficial to create a new relationship (have a new follower/friend) in a social network? This question can be formally stated as a property of the centrality measure that defines the importance of the actors of the network. Score monotonicity means that adding an arc increases the centrality score of the target of the arc; rank monotonicity means that adding an arc improves the importance of the target of the arc relatively to the remaining nodes. It is known that most centralities are both score and rank monotone on directed, strongly connected graphs. In this paper, we study the problem of score and rank monotonicity for classical centrality measures in the case of undirected networks: in this case, we require that score, or relative importance, improves at both endpoints of the new edge. We show that, surprisingly, the situation in the undirected case is very different, and in particular that closeness, harmonic centrality, betweenness, eigenvector centrality, Seeley’s index, Katz’s index, and PageRank are not rank monotone; betweenness and PageRank are not even score monotone. In other words, while it is always a good thing to get a new follower, it is not always beneficial to get a new friend.","PeriodicalId":51827,"journal":{"name":"Network Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135360231","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Domenico De Stefano, V. Fuccella, M. P. Vitale, S. Zaccarin
Abstract A stream of research on co-authorship, used as a proxy of scholars’ collaborative behavior, focuses on members of a given scientific community defined at discipline and/or national basis for which co-authorship data have to be retrieved. Recent literature pointed out that international digital libraries provide partial coverage of the entire scholar scientific production as well as under-coverage of the scholars in the community. Bias in retrieving co-authorship data of the community of interest can affect network construction and network measures in several ways, providing a partial picture of the real collaboration in writing papers among scholars. In this contribution, we collected bibliographic records of Italian academic statisticians from an online platform (IRIS) available at most universities. Even if it guarantees a high coverage rate of our population and its scientific production, it is necessary to deal with some data quality issues. Thus, a web scraping procedure based on a semi-automatic tool to retrieve publication metadata, as well as data management tools to detect duplicate records and to reconcile authors, is proposed. As a result of our procedure, it emerged that collaboration is an active and increasing practice for Italian academic statisticians with some differences according to the gender, the academic ranking, and the university location of scholars. The heuristic procedure to accomplish data quality issues in the IRIS platform can represent a working case report to adapt to other bibliographic archives with similar characteristics.
{"title":"Quality issues in co-authorship data of a national scientific community","authors":"Domenico De Stefano, V. Fuccella, M. P. Vitale, S. Zaccarin","doi":"10.1017/nws.2022.40","DOIUrl":"https://doi.org/10.1017/nws.2022.40","url":null,"abstract":"Abstract A stream of research on co-authorship, used as a proxy of scholars’ collaborative behavior, focuses on members of a given scientific community defined at discipline and/or national basis for which co-authorship data have to be retrieved. Recent literature pointed out that international digital libraries provide partial coverage of the entire scholar scientific production as well as under-coverage of the scholars in the community. Bias in retrieving co-authorship data of the community of interest can affect network construction and network measures in several ways, providing a partial picture of the real collaboration in writing papers among scholars. In this contribution, we collected bibliographic records of Italian academic statisticians from an online platform (IRIS) available at most universities. Even if it guarantees a high coverage rate of our population and its scientific production, it is necessary to deal with some data quality issues. Thus, a web scraping procedure based on a semi-automatic tool to retrieve publication metadata, as well as data management tools to detect duplicate records and to reconcile authors, is proposed. As a result of our procedure, it emerged that collaboration is an active and increasing practice for Italian academic statisticians with some differences according to the gender, the academic ranking, and the university location of scholars. The heuristic procedure to accomplish data quality issues in the IRIS platform can represent a working case report to adapt to other bibliographic archives with similar characteristics.","PeriodicalId":51827,"journal":{"name":"Network Science","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2023-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45074954","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Valentina V. Kuskova, Dmitry G. Zaytsev, Gregory S. Khvatsky, Anna A. Sokol, Maria D. Vorobeva, Rustam A. Kamalov
Abstract In this paper, we examine the contribution of Network Science journal to the network science discipline. We do so from two perspectives. First, expanding the existing taxonomy of article contribution, we examine trends in theory testing, theory building, and new method development within the journal’s articles. We find that the journal demands a high level of theoretical contribution and methodological rigor. High levels of theoretical and methodological contribution become significant predictors of article citation rates. Second, we look at the composition of the studies in Network Science and determine that the journal has already established a solid “hard core” for the new discipline.
{"title":"Expanding the boundaries of interdisciplinary field: Contribution of Network Science journal to the development of network science","authors":"Valentina V. Kuskova, Dmitry G. Zaytsev, Gregory S. Khvatsky, Anna A. Sokol, Maria D. Vorobeva, Rustam A. Kamalov","doi":"10.1017/nws.2022.41","DOIUrl":"https://doi.org/10.1017/nws.2022.41","url":null,"abstract":"Abstract In this paper, we examine the contribution of Network Science journal to the network science discipline. We do so from two perspectives. First, expanding the existing taxonomy of article contribution, we examine trends in theory testing, theory building, and new method development within the journal’s articles. We find that the journal demands a high level of theoretical contribution and methodological rigor. High levels of theoretical and methodological contribution become significant predictors of article citation rates. Second, we look at the composition of the studies in Network Science and determine that the journal has already established a solid “hard core” for the new discipline.","PeriodicalId":51827,"journal":{"name":"Network Science","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2023-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45192244","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Alina Lungeanu, Ryan Whalen, Y. J. Wu, Leslie A. DeChurch, N. Contractor
Abstract Despite the importance of diverse expertise in helping solve difficult interdisciplinary problems, measuring it is challenging and often relies on proxy measures and presumptive correlates of actual knowledge and experience. To address this challenge, we propose a text-based measure that uses researcher’s prior work to estimate their substantive expertise. These expertise estimates are then used to measure team-level expertise diversity by determining similarity or dissimilarity in members’ prior knowledge and skills. Using this measure on 2.8 million team invented patents granted by the US Patent Office, we show evidence of trends in expertise diversity over time and across team sizes, as well as its relationship with the quality and impact of a team’s innovation output.
{"title":"Diversity, networks, and innovation: A text analytic approach to measuring expertise diversity","authors":"Alina Lungeanu, Ryan Whalen, Y. J. Wu, Leslie A. DeChurch, N. Contractor","doi":"10.1017/nws.2022.34","DOIUrl":"https://doi.org/10.1017/nws.2022.34","url":null,"abstract":"Abstract Despite the importance of diverse expertise in helping solve difficult interdisciplinary problems, measuring it is challenging and often relies on proxy measures and presumptive correlates of actual knowledge and experience. To address this challenge, we propose a text-based measure that uses researcher’s prior work to estimate their substantive expertise. These expertise estimates are then used to measure team-level expertise diversity by determining similarity or dissimilarity in members’ prior knowledge and skills. Using this measure on 2.8 million team invented patents granted by the US Patent Office, we show evidence of trends in expertise diversity over time and across team sizes, as well as its relationship with the quality and impact of a team’s innovation output.","PeriodicalId":51827,"journal":{"name":"Network Science","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2022-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44328724","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract Higher-order networks aim at improving the classical network representation of trajectories data as memory-less order $1$ Markov models. To do so, locations are associated with different representations or “memory nodes” representing indirect dependencies between visited places as direct relations. One promising area of investigation in this context is variable-order network models as it was suggested by Xu et al. that random walk-based mining tools can be directly applied on such networks. In this paper, we focus on clustering algorithms and show that doing so leads to biases due to the number of nodes representing each location. To address them, we introduce a representation aggregation algorithm that produces smaller yet still accurate network models of the input sequences. We empirically compare the clustering found with multiple network representations of real-world mobility datasets. As our model is limited to a maximum order of $2$ , we discuss further generalizations of our method to higher orders.
{"title":"Toward random walk-based clustering of variable-order networks","authors":"Julie Queiros, C. Coquidé, François Queyroi","doi":"10.1017/nws.2022.36","DOIUrl":"https://doi.org/10.1017/nws.2022.36","url":null,"abstract":"Abstract Higher-order networks aim at improving the classical network representation of trajectories data as memory-less order \u0000$1$\u0000 Markov models. To do so, locations are associated with different representations or “memory nodes” representing indirect dependencies between visited places as direct relations. One promising area of investigation in this context is variable-order network models as it was suggested by Xu et al. that random walk-based mining tools can be directly applied on such networks. In this paper, we focus on clustering algorithms and show that doing so leads to biases due to the number of nodes representing each location. To address them, we introduce a representation aggregation algorithm that produces smaller yet still accurate network models of the input sequences. We empirically compare the clustering found with multiple network representations of real-world mobility datasets. As our model is limited to a maximum order of \u0000$2$\u0000 , we discuss further generalizations of our method to higher orders.","PeriodicalId":51827,"journal":{"name":"Network Science","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41445092","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：