The Concept and Application of Scientific Communities Detection within Academic Mixed-Network

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.