Abstract
Purpose: The interpretation of network analysis research can be challenging today. The aim of this study is to analyze the homogeneous and the heterogeneous network information that occurred in the UEFA Champions League 2017-2018. Research Methodology: To obtain an interpretation of the results of network information analysis, centrality measurements and community detection were performed, where the centrality measurements methods used are Degree centrality, Betweenness centrality, Eigencentrality, PageRank, while community detection method used is performed using the Louvain. Result: The homogenous and heterogeneous network analysis was conducted using dataset of 17980 players, 32 teams, and 128 matches in Champions League 2017-2018. In this analysis, homogenous and heterogeneous network schemes were used to represent objects and relationships between objects in the network. The analysis was based on centrality measurements to identify influential nodes and community emergence within the network. The result is an interpretation of network analysis in the form of information about the roles of players, teams, countries, locations, formations, and skills that affect the performance of UEFA Champions League. Limitation: the use of diverse data sources, the application or development of data analysis techniques, and the formation of a broader network scheme Contribution: Obtaining information related to the UEFA Champions League based on the interpretation result of the analysis of homogeneous and heterogeneous networks
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