  In order to advance the state of the art in graph learning algorithms, it is
necessary to construct large real-world datasets. While there are many
benchmark datasets for homogeneous graphs, only a few of them are available for
heterogeneous graphs. Furthermore, the latter graphs are small in size
rendering them insufficient to understand how graph learning algorithms perform
in terms of classification metrics and computational resource utilization. We
introduce, PDNS-Net, the largest public heterogeneous graph dataset containing
447K nodes and 897K edges for the malicious domain classification task.
Compared to the popular heterogeneous datasets IMDB and DBLP, PDNS-Net is 38
and 17 times bigger respectively. We provide a detailed analysis of PDNS-Net
including the data collection methodology, heterogeneous graph construction,
descriptive statistics and preliminary graph classification performance. The
dataset is publicly available at https://github.com/qcri/PDNS-Net. Our
preliminary evaluation of both popular homogeneous and heterogeneous graph
neural networks on PDNS-Net reveals that further research is required to
improve the performance of these models on large heterogeneous graphs.
