Evolution Analysis Of Academic Associations With Spatial Attributes

The progress of science and technology is inseparable from the hard work of the vast number of scientific researchers! Today,with the deeper and more professional development of science and technology,more sophisticated professional research requires a large number of researchers to work together and accomplish together.At present,the number of scientific research papers published in cooperation has shown a rising trend.This shows that the majority of researchers have realized that building academic associations to carry out cooperative research will have great effect on the improvement of personal level.Based on this kind of thinking,combined with the relevant theoretical knowledge of complex network community detection,this paper studies and proposes the academic community evolution analysis framework that integrates spatial attributes.The specific work and innovations of this paper are as follows:Firstly,the evolutionary analysis framework of academic associations with spatial attributes is proposed.The extracted spatial information is used as an attribute,combined with the complex network community detection model,to design an evolutionary framework of academic communities with stronger detection capabilities.The framework includes an evolutionary ontology model of academic communities that expands spatial attributes and a joint-text model that integrates spatial attributes,a common-text network model that integrates spatial attributes,an operator that combines spatial attributes,and an academic of fusion spatial attributes community evolution operator.Secondly,based on the MAG biological dataset,a total of 4,186,426 papers published in the field of biology since 2017 were collected,involving 16,171,773 authors.The raw data is technically processed,including name ambiguity elimination,named entity recognition,and spatial attribute acquisition.On this basis,using the principle of complex network generation,a common text network with authors as nodes and authors co-authored published papers as links is constructed.Through empirical analysis,the network characteristics such as degree distribution,clustering coefficient and average path length of the network are studied.Finally,the common text network constructed by MAG biodata is analyzed as the basic data of the academic community evolution analysis framework of the proposed fusion space attribute.The results show two new phenomena that are different from the results of traditional complex network research community detection.First,the community results detected by the modularity method are much lower than the accuracy of the results of the fusion of spatial attributes in this paper.Second,the empirical analysis found that between 1997 and 2017,genetic editing in the field of biological science research The number of academic societies for the main researchdirection has increased year by year,and it has shown a trend of rapid spread by developed countries such as Europe,America and Japan to developing countries such as China.In summary,this paper proposes an academic community evolution analysis framework that combines spatial attributes,and analyzes the evolution direction of biology hotspots through this framework,and mines valuable information in academic data,which provides guidance for scholars in the field of biology,and The framework provides some reference value for academic data research in other fields.