Community evolution analysis based on co-author network: a case study of academic communities of the journal of “Annals of the Association of American Geographers”

Abstract

Academic community evolution reveals the development of scientific collaboration among scientists. These social interactions of researchers can be well reflected by co-author network, making it feasible to investigate academic community through looking into co-author network, and to study community evolution through dynamic co-author network analysis. Existing metrics measure an author’s impact or centrality in co-author network individually, rather than considering the academic community as a whole. Besides, co-authors of a paper usually make different contributions reflected in the name order, which is often ignored in traditional co-author network analysis. Furthermore, attention has been paid mainly on those structure-level characteristics like the small-world coefficient and the clustering coefficient, the content-level characteristics like community, author, and topics, however, are crucial in the understanding of community evolution. To address those problems, we firstly propose a “comprehensive impact index” to evaluate the author in a co-author network by comprehensively considering the statistic-based impact and the network-based centrality. Then the comprehensive index value of all authors in a community is added up to evaluate the community as a whole. Further, a lifecycle strategy is proposed for the community evolution analysis. Taking geography academic community as a pilot study, we select 919 co-authored papers from the flagship journal of “Annals of the Association of American Geographers”. The co-author groups are generated by community detection method. Top three co-author groups are identified through computing with the proposed index and analyzed through the proposed lifecycle strategy from perspective of community structures, member authors, and impacts respectively. The results demonstrate our proposed index and strategy are more efficient for analyzing academic community evolution than traditional methods.