Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: IPC: A Benchmark Data Set for Learning with Graph-Structured Data   Benchmark data sets are an indispensable ingredient of the evaluation of
graph-based machine learning methods. We release a new data set, compiled from
International Planning Competitions (IPC), for benchmarking graph
classification, regression, and related tasks. Apart from the graph
construction (based on AI planning problems) that is interesting in its own
right, the data set possesses distinctly different characteristics from
popularly used benchmarks. The data set, named IPC, consists of two
self-contained versions, grounded and lifted, both including graphs of large
and skewedly distributed sizes, posing substantial challenges for the
computation 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 are
computer programmed; thus, the data set may be extended easily if a larger
scale is desired. The data set is accessible from
\url{https://github.com/IBM/IPC-graph-data}.

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The categories of this paper are: hep-ph
21
The categories of this paper are: adap-org
20
The categories of this paper are: acc-phys
27
The categories of this paper are: comp-gas
5
The categories of this paper are: gr-qc
31
The categories of this paper are: ao-sci
37
The categories of this paper are: supr-con
22
The categories of this paper are: chao-dyn
36
The categories of this paper are: cmp-lg
13
The categories of this paper are: quant-ph
4
The categories of this paper are: cond-mat
18
The categories of this paper are: econ
23
The categories of this paper are: solv-int
32
The categories of this paper are: atom-ph
26
The categories of this paper are: bayes-an
12
The categories of this paper are: nucl-th