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Benchmark data sets are an indispensable ingredient of the evaluation of graph-based machine learning methods.
Complexity results for SAS +
Bäckström, C. and Nebel, B · 1995
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The 1998 AI Planning Systems competition
McDermott, D · 2000
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Concise finite-domain representations for PDDL planning tasks
Helmert, M · 2009
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Exploiting problem symmetries in state-based planners
Pochter, N., Zohar, A., and Rosenschein, J. S · 2011
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Heuristics and symmetries in classical planning
Shleyfman, A., Katz, M., Helmert, M., Sievers, S., and Wehrle, M · 2015
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Benchmark data sets for graph kernels, 2016
Kersting, K., Kriege, N. M., Morris, C., Mutzel, P., and Neumann, M · 2016
Cited alongside, same era.
Gated graph sequence neural networks
Li, Y., Tarlow, D., Brockschmidt, M., and Zemel, R · 2016
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Semi-supervised classification with graph convolutional networks
Kipf, T. N. and Welling, M · 2017
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Delfi: Online planner selection for cost-optimal planning
Katz, M., Sohrabi, S., Samulowitz, H., and Sievers, S · 2018
Cited alongside, same era.
Deeper insights into graph convolutional networks for semi-supervised learning
Li, Q., Han, Z., and Wu, X.-M · 2018
Cited alongside, same era.
Representation learning on graphs with jumping knowledge networks
Xu, K., Li, C., Tian, Y., Sonobe, T., ichi Kawarabayashi, K., and Jegelka, S · 2018
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Predict then propagate: Graph neural networks meet personalized pagerank
Klicpera, J., Bojchevski, A., and Günnemann, S · 2019
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Deep learning for cost-optimal planning: Task-dependent planner selection
Sievers, S., Katz, M., Sohrabi, S., Samulowitz, H., and Ferber, P · 2019
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Theoretical foundations for structural symmetries of lifted pddl tasks
Sievers, S., Röger, G., Wehrle, M., and Katz, M · 2019
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