The Concept and Application of Scientific Communities Detection within Academic Mixed-Network
Year: 2024
Type: conference-paper
Abstract: Mixed networks are often composed of multiple communities. Dividing mixed-networks into communities can effectively reveal the structural characteristics, content attributes, and group distribution of the networks. This article summarizes and sorts out the relevant research achievements of the author's team in the past few years, including both theoretical and methodological exploration and practical application. The concept and classification of mixed-networks, weighted fusion methods, six types of mixed-network community partitioning algorithms, and application implementation in academic mixed-network community partitioning are discussed. The community division for academic mixed-networks provides theoretical and methodological guidance for revealing scientific structures, discovering scientific communities, and supporting scientific decision-making.
Authors: Yunwei Chen, Xin Zhang, Lu Jiang, Hongshen Pang
Institutions: Chinese Academy of Sciences, Hohai University, Shenzhen University
Language: English
Subfield: Statistical and Nonlinear Physics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4402220789