Academic team dynamic community discovery method based on temporal collaboration network and quality evaluation method

Abstract

The invention provides an academic team dynamic community discovery algorithm based on a temporal collaboration network. A temporal collaboration network model is established. On the basis of real-time detection of the importance of author nodes and the strength change of relation edges in a collaboration network, the evolution of an academic team in the academic collaboration network is analyzed and tracked, and a community is created, expanded, contracted, divided and disappeared in order to achieve the purpose of dynamic community discovery. The algorithm is of high accuracy. In addition, the invention provides an academic team community division quality evaluation method based on word features, which is used to evaluate the quality of community division. The algorithm is experimentally verified using a public document information record data set. The experimental results show that the algorithm is effective.

Description

technical field [0001] The invention relates to a method for discovering a dynamic community of an academic team based on a temporal co-authoring network. Background technique [0002] The national innovation system needs a group of high-level scientific research groups with independent innovation capabilities as an important part of it. How to effectively observe and analyze the evolution of research groups and comprehensively and objectively evaluate the overall performance of academic teams is a new topic in the selection and evaluation of scientific and technological innovation groups. The scientific research team is a whole naturally formed by long-term cooperation, and its complex evolution process is often hidden in the historical records of various academic activities such as scientific research workers co-publishing articles and joint research projects. Therefore, it is an important task in the field of complex network analysis to use the large-scale scientific and...

AI Technical Summary

Problems solved by technology

In 2016, Rossetti et al.[10] proposed the TILES (Temporal Interactions a Local Edge Strategy) algorithm. In this algorithm, the network will not only add relationship edges over time, but also existing edges may disappear. The impact on the evolution of the community makes the dynamic community discovery algorithm more effective, but does not consider the impact of the importance of nodes and the closeness of interaction on the community structure

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