This study aimed to delineate the specific characteristics of binge drinking habits by capitalizing on data-driven network analysis. Such an approach allowed us to consider binge drinking as a network system of interacting elements, thus identifying the key variables involved in this phenomenon. A total of 1,455 university students with excessive drinking habits were included in this study. We assessed the most critical features of binge drinking (i.e., the consumption of more than six alcohol units per occasion, drunkenness frequency, consumption speed), together with alcohol use and more general alcohol-related components of dysfunction and harm. All variables were considered in the network analysis. Centrality analysis identified drunkenness frequency as the most influential variable in the entire network. Community detection analysis showed three distinct subnetworks related to alcohol use, drunkenness, and dysfunction/harm components. Drunkenness frequency and blackout occurrence emerged as core bridge items in the binge drinking network. Drunkenness is recognized as the hallmark feature of binge drinking.