Analyzing and visualizing scientific research collaboration network with core node evaluation and community detection based on network embedding

• Published in: Pattern recognition letters 2021-04, Vol.144, p.54-60
• Main Authors: Zhao, Wenbin, Luo, Jishuang, Fan, Tongrang, Ren, Yan, Xia, Yukun
• Format: Article
• Language: English
• Subjects: Algorithms; Collaboration; Community detection; Community structure; Core node evaluation; Embedding; Heterogeneity; Layouts; Network embedding; Network topologies; Nodes; Organizational structure; Scientific research collaboration network; Visual layout algorithm
• ISSN: 0167-8655; 1872-7344
• DOI: 10.1016/j.patrec.2021.01.007

•This paper focuses on core node evaluation, community detection and visual layout algorithm.•A scientific research collaboration network is constructed based on network embedding.•A core node evaluation method is proposed based on network topology and node heterogeneity.•The community detection and visual layout algorithm is improved to display the community structure from many aspects.•the proposed method can more clearly show the internal structure of scientific research collaboration community. With the increasing complexity of scientific research, it has gradually turned to a collaborative approach, which can promote knowledge sharing, resource sharing and improve the efficiency of scientific research achievements. Therefore, It is of great significance to study the internal organizational structure and evolution mechanism of scientific research collaboration, which plays a crucial role in the management of scientific research work and the formulation of scientific and technological policies. This paper focuses on three aspects: core node evaluation, community detection and visual layout algorithm of scientific research collaboration network, which is constructed based on the network embedding of the scientific research achievements’ attributes. Considering network topology and node heterogeneity, a core node evaluation method is proposed, and a community detection algorithm and a visual layout algorithm is improved to display the community structure of scientific research collaboration network from many aspects. The experimental results show that the proposed method can more clearly show the internal structure of scientific research collaboration community.