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

Ferber, Patrick and Ma, Tengfei and Huo, Siyu and Chen, Jie and Katz, Michael. (2019) IPC: A Benchmark Data Set for Learning with Graph-Structured Data. Proceedings in the ICML 2019 Workshop on Learning and Reasoning with Graph-Structured Representations.

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Official URL: https://edoc.unibas.ch/74628/

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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 fromthe 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 dataset, 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 labelingare computer programmed; thus, the data set may be extended easily if a larger scale is desired.
Faculties and Departments:05 Faculty of Science > Departement Mathematik und Informatik > Informatik > Artificial Intelligence (Helmert)
UniBasel Contributors:Ferber, Patrick
Item Type:Other
Publisher:AAAI Press
Number of Pages:6
Note:Publication type according to Uni Basel Research Database: Other publications
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Last Modified:18 Feb 2020 10:30
Deposited On:18 Feb 2020 10:30

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