AUTHOR-CENTRIC PATTERN DETECTION IN SCOPUS CITATION NETWORK VIA COMMUNITY STRUCTURES (Conference Paper)

Abstract

Analyzing citation networks provides effective utilization of research such as identifying domain-specific research influencers, encouraging the growth of collaboration within various scientific domains: Discover the essential trending topics to promote scholarly development. Performing Citation network analysis and bibliometric analysis on Scopus as a complex interconnected network dataset provides an in-depth knowledge about network analysis, structures, and Community detection based on their citation patterns. Citation network analysis involves studying the relationships, interconnections, and patterns among scientific publications through references. The importance of community detection in the Scopus network is to find hidden intellectual connections, interdisciplinary relationships, and research trends. The research objectives of this work focus on using Python-based methodologies to analyze the citation patterns among authors in the Scopus citation network. The key findings are to analyze the citation patterns of top authors, top 10 solo contributing authors, and authorship patterns from the Scopus network. The main contribution of this study is to understand evolving research trends and the importance of community detection in academic fields. This study employs the Louvain approach, which identifies eight communities from the Scopus citation network. The goal is to discover communities within the academic Scopus data through bibliometric and network analysis, with the aim of enhancing research publication in different fields.