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.

Practical operation and theoretical basis of difference-in-difference regression in science of science:The comparative trial on the scientific performance of Nobel laureates versus their coauthors

Purpose:In recent decades,with the availability of large-scale scientific corpus datasets,difference-in-difference(DID)is increasingly used in the science of science and bibliometrics studies.DID method outputs the unbiased estimation on condition that several hypotheses hold,especially the common trend assumption.In this paper,we gave a systematic demonstration of DID in the science of science,and the potential ways to improve the accuracy of DID method.Design/methodology/approach:At first,we reviewed the statistical assumptions,the model specification,and the application procedures of DID method.Second,to improve the necessary assumptions before conducting DID regression and the accuracy of estimation,we introduced some matching techniques serving as the pre-selecting step for DID design by matching control individuals who are equivalent to those treated ones on observational variables before the intervention.Lastly,we performed a case study to estimate the effects of prizewinning on the scientific performance of Nobel laureates,by comparing the yearly citation impact after the prizewinning year between Nobel laureates and their prizewinning-work coauthors.Findings:We introduced the procedures to conduct a DID estimation and demonstrated the effectiveness to use matching method to improve the results.As a case study,we found that there are no significant increases in citations for Nobel laureates compared to their prizewinning coauthors.Research limitations:This study ignored the rigorous mathematical deduction parts of DID,while focused on the practical parts.Practical implications:This work gives experimental practice and potential guidelines to use DID method in science of science and bibliometrics studies.Originality/value:This study gains insights into the usage of econometric tools in science of science.