# Eva: Attribute-Aware Network Segmentation

arXiv: https://arxiv.org/abs/1910.06599 · HTML: https://mlsanity.com/paper/1910.06599/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Oct 2019.

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

- [HINTs: Sensemaking on large collections of documents with Hypergraph visualization and INTelligent agents](https://mlsanity.com/paper/2403.02752/) (2024)
- [A Community-Based Approach for Stance Distribution and Argument Organization](https://mlsanity.com/paper/2604.16852/) (2026)

## Abstract (arXiv)

Identifying topologically well-defined communities that are also homogeneous w.r.t. attributes carried by the nodes that compose them is a challenging social network analysis task. We address such a problem by introducing Eva, a bottom-up low complexity algorithm designed to identify network hidden mesoscale topologies by optimizing structural and attribute-homophilic clustering criteria. We evaluate the proposed approach on heterogeneous real-world labeled network datasets, such as co-citation, linguistic, and social networks, and compare it with state-of-art community discovery competitors. Experimental results underline that Eva ensures that network nodes are grouped into communities according to their attribute similarity without considerably degrading partition modularity, both in single and multi node-attribute scenarios