Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Evaluation Metrics for Graph Generative Models: Problems, Pitfalls, and
  Practical Solutions   Graph generative models are a highly active branch of machine learning. Given
the steady development of new models of ever-increasing complexity, it is
necessary to provide a principled way to evaluate and compare them. In this
paper, we enumerate the desirable criteria for such a comparison metric and
provide an overview of the status quo of graph generative model comparison in
use today, which predominantly relies on the maximum mean discrepancy (MMD). We
perform a systematic evaluation of MMD in the context of graph generative model
comparison, highlighting some of the challenges and pitfalls researchers
inadvertently may encounter. After conducting a thorough analysis of the
behaviour of MMD on synthetically-generated perturbed graphs as well as on
recently-proposed graph generative models, we are able to provide a suitable
procedure to mitigate these challenges and pitfalls. We aggregate our findings
into a list of practical recommendations for researchers to use when evaluating
graph generative models.

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The categories of this paper are: dg-ga
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The categories of this paper are: atom-ph
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The categories of this paper are: ao-sci
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The categories of this paper are: nucl-th
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The categories of this paper are: math
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The categories of this paper are: mtrl-th
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The categories of this paper are: physics
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The categories of this paper are: supr-con
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The categories of this paper are: cmp-lg
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The categories of this paper are: hep-ex
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The categories of this paper are: hep-lat
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The categories of this paper are: nucl-ex
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The categories of this paper are: astro-ph
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The categories of this paper are: eess
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The categories of this paper are: funct-an
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The categories of this paper are: comp-gas