Generating large synthetic attributed graphs with node labels is an important task to support various experimental studies for graph analytic methods. Existing graph generators fail to simultaneously simulate core/border and homophily/heterophily phenomena which real-world graphs exhibit, i.e., the relationships between labels, attributes, and topology. Motivated by this limitation, we propose GenCAT, an attributed graph generator for controlling those relationships, which has the following advantages. (i) GenCAT generates graphs with user-specified node degrees and flexibly controls the relationship between nodes and labels by incorporating the connection proportion for each node to classes. (ii) Generated attribute values follow user-specified distributions, and users can flexibly control the correlation between the attributes and labels. (iii) Graph generation scales linearly to the number of edges. GenCAT is the first generator to support all three of these practical features, i.e., it can capture both core/border and homophily/heterophily phenomena while ensuring its scalability. Through extensive experiments, we demonstrate that GenCAT can efficiently generate high-quality complex attributed graphs with user-controlled relationships between labels, attributes, and topology.

We propose a novel edge generation procedure, Community-aware Edge Generation (CEG), which controls the internal structure of communities: hub dominance and clustering coefficient. CEG is designed to be adaptable to existing graph generators. We demonstrate the effectiveness of CEG from three aspects. First, we validate that CEG generates graphs with similar internal structures to given real-world graphs. Second, we show how the parameters of CEG control the internal structure of communities. Finally, we show that CEG can generate various types of internal structures of communities by visualizing generated graphs.