The field of materials science is expanding rapidly with new ‎materials and applications, and data science tools are now ‎available to aid in the selection and prediction of properties for ‎specific applications. Among these tools, social network ‎analysis (SNA) has been widely used to represent data as a ‎graph of connected objects in various scientific fields. This ‎study focuses on applying a graph theory approach to analyze ‎metal-organic frameworks (MOFs) using SNA methods. By ‎creating a galaxy of MOFs and conducting community ‎detection, SNA can predict MOF properties more accurately ‎than conventional machine learning methods. The study shows ‎that SNA is particularly useful in predicting gas storage ‎properties, one of MOF's most popular applications. ‎