# Conformity: A Path-Aware Homophily Measure for Node-Attributed Networks

arXiv: https://arxiv.org/abs/2012.05195 · HTML: https://mlsanity.com/paper/2012.05195/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Dec 2020.

## Built on by (most cited first, 4 of 4)

- [Y Social: an LLM-powered Social Media Digital Twin](https://mlsanity.com/paper/2408.00818/) (2024)
- [Feature-rich multiplex lexical networks reveal mental strategies of early language learning](https://mlsanity.com/paper/2201.05061/) (2022)
- [Rethinking Graph Contrastive Learning through Relative Similarity Preservation](https://mlsanity.com/paper/2505.05533/) (2025)
- [From Perils to Possibilities: Understanding how Human (and AI) Biases affect Online Fora](https://mlsanity.com/paper/2403.14298/) (2024)

## Abstract (arXiv)

Unveil the homophilic/heterophilic behaviors that characterize the wiring patterns of complex networks is an important task in social network analysis, often approached studying the assortative mixing of node attributes. Recent works underlined that a global measure to quantify node homophily necessarily provides a partial, often deceiving, picture of the reality. Moving from such literature, in this work, we propose a novel measure, namely Conformity, designed to overcome such limitation by providing a node-centric quantification of assortative mixing patterns. Differently from the measures proposed so far, Conformity is designed to be path-aware, thus allowing for a more detailed evaluation of the impact that nodes at different degrees of separations have on the homophilic embeddedness of a target. Experimental analysis on synthetic and real data allowed us to observe that Conformity can unveil valuable insights from node-attributed graphs.