  Generating large synthetic attributed graphs with node labels is an important
task to support various experimental studies for graph analysis methods.
Existing graph generators fail to simultaneously simulate the relationships
between labels, attributes, and topology which real-world graphs exhibit.
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. Through extensive experiments, we demonstrate that
GenCAT can efficiently generate high-quality complex attributed graphs with
user-controlled relationships between labels, attributes, and topology.
