Visual Analysis for Name Disambiguation of Academic Papers

Abstract

The visual presentation of academic social networks can help users better perform the name disambiguation work in academic paper management. The complex network among authors brings a great challenge to visual interaction. In order to help users better perform the name disambiguation work, a name disambiguation visualization analysis method for academic papers is designed and implemented. Proposed method first generates a cooperative relationship graph based on the cooperative network of co-authors, which is used to reveal the cooperative relationship of authors in the scientific research team. Then, to show the correlation between the research directions of different authors, the visual linkage between the collaboration graph and the published journal graph is designed. Finally, through the combination of the deep learning model, papers and authors are classified respectively to achieve the cross-analysis and coherent reasoning starting from the author or team. The system is based on 4 000 actual papers for case studies and professionals and students are invited to use and evaluate the system, proving the effectiveness in solving name disambiguation.