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arXiv · 1901.01354

Metrics matter in community detection

Arya D. McCarthy·Tongfei Chen·Rachel Rudinger·David W. Matula
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Abstract

We present a critical evaluation of normalized mutual information (NMI) as an evaluation metric for community detection. NMI exaggerates the leximin method's performance on weak communities: Does leximin, in finding the trivial singletons clustering, truly outperform eight other community detection methods? Three NMI improvements from the literature are AMI, rrNMI, and cNMI. We show equivalences under relevant random models, and for evaluating community detection, we advise one-sided AMI under the $\mathbb{M}_{\mathrm{all}}$ model (all partitions of $n$ nodes). This work seeks (1) to start a conversation on robust measurements, and (2) to advocate evaluations which do not give "free lunch".

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Arya D. McCarthy, Tongfei Chen, Rachel Rudinger, David W. Matula. 2019-01-05. Metrics matter in community detection. https://doi.org/10.1007/978-3-030-36687-2_14

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