This study explores the global structure of multilingual interactions through the lens of network analysis, aiming to determine whether language relationships are more accurately captured by decentralized networks than by models centered on a single dominant language. Using three complementary datasets—OpenSubtitles movie translations, Wikipedia interlanguage links, and spoken-at-home data from the World Values Survey—we construct weighted language networks and apply community detection, centrality metrics, and clustering analysis. Across all datasets, we observe consistent patterns of linguistic clustering that align with historical alliances, colonial legacies, and migration flows. While English consistently emerges as a central hub, regional clusters such as those in Eastern Europe, Latin America, and Southeast Asia demonstrate substantial structural autonomy. These findings suggest that multilingual network models offer a more faithful representation of linguistic and cultural dynamics than monolingual hierarchies, highlighting the value of graph-based approaches in studying global language systems.