# Comparing Graph Clusterings: Set partition measures vs. Graph-aware measures

arXiv: https://arxiv.org/abs/1806.11494 · HTML: https://mlsanity.com/paper/1806.11494/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Jun 2018.

## Built on by (most cited first, 2 of 2)

- [A Pragmatic Method for Comparing Clusterings with Overlaps and Outliers](https://mlsanity.com/paper/2602.14855/) (2026)
- [Beyond the Academic Monoculture: A Unified Framework and Industrial Perspective for Attributed Graph Clustering](https://mlsanity.com/paper/2603.20829/) (2026)

## Abstract (arXiv)

In this paper, we propose a family of graph partition similarity measures that take the topology of the graph into account. These graph-aware measures are alternatives to using set partition similarity measures that are not specifically designed for graph partitions. The two types of measures, graph-aware and set partition measures, are shown to have opposite behaviors with respect to resolution issues and provide complementary information necessary to assess that two graph partitions are similar.