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Benchmark data sets are an indispensable ingredient of the evaluation ofgraph-based machine learning methods. We release a new data set, compiled fromInternational Planning Competitions (IPC), for benchmarking graphclassification, regression, and related tasks. Apart from the graphconstruction (based on AI planning problems) that is interesting in its ownright, the data set possesses distinctly different characteristics frompopularly used benchmarks. The data set, named IPC, consists of twoself-contained versions, grounded and lifted, both including graphs of largeand skewedly distributed sizes, posing substantial challenges for thecomputation of graph models such as graph kernels and graph neural networks.The graphs in this data set are directed and the lifted version is acyclic,offering the opportunity of benchmarking specialized models for directed(acyclic) structures. Moreover, the graph generator and the labeling arecomputer programmed; thus, the data set may be extended easily if a largerscale is desired. The data set is accessible from\url{https://github.com/IBM/IPC-graph-data}.

IPC: A Benchmark Data Set for Learning with Graph-Structured Data