# Measuring Proximity in Attributed Networks for Community Detection

arXiv: https://arxiv.org/abs/2111.03089 · HTML: https://mlsanity.com/paper/2111.03089/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 37 references that are in the map.
- First posted on arXiv 4 Nov 2021.

## Builds on (most cited first, 3 of 3)

- [Community Detection in Networks with Node Attributes](https://mlsanity.com/paper/1401.7267/) (2014)
- [Community detection in node-attributed social networks: a survey](https://mlsanity.com/paper/1912.09816/) (2019)
- [Clustering attributed graphs: models, measures and methods](https://mlsanity.com/paper/1501.01676/) (2015)

## Abstract (arXiv)

Proximity measures on graphs have a variety of applications in network analysis, including community detection. Previously they have been mainly studied in the context of networks without attributes. If node attributes are taken into account, however, this can provide more insight into the network structure. In this paper, we extend the definition of some well-studied proximity measures to attributed networks. To account for attributes, several attribute similarity measures are used. Finally, the obtained proximity measures are applied to detect the community structure in some real-world networks using the spectral clustering algorithm.