Abuse on the Internet represents a significant societal problem of our time. Previous research on automated abusive language detection in Twitter has shown that community-based profiling of users is a promising technique for this task. However, existing approaches only capture shallow properties of online communities by modeling follower–following relationships. In contrast, we present the first approach that captures both the structure of online communities as well as the linguistic behavior of the users within them, based on graph convolutional networks (GCNs). We show that such heterogeneous graph-structured modeling of communities significantly advances the current state of the art in abusive language detection.