# Brain network similarity:Methods and applications

arXiv: https://arxiv.org/abs/1908.10592 · HTML: https://mlsanity.com/paper/1908.10592/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Aug 2019.

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

- [Recurrent Multigraph Integrator Network for Predicting the Evolution of Population-Driven Brain Connectivity Templates](https://mlsanity.com/paper/2110.03453/) (2021)
- [VisGraphVar: A Benchmark Generator for Assessing Variability in Graph Analysis Using Large Vision-Language Models](https://mlsanity.com/paper/2411.14832/) (2024)
- [Cognitive modelling with multilayer networks: Insights, advancements and future challenges](https://mlsanity.com/paper/2210.00500/) (2022)
- [BOLDSimNet: Examining Brain Network Similarity between Task and Resting-State fMRI](https://mlsanity.com/paper/2504.01274/) (2025)

## Abstract (arXiv)

Graph theoretical approach has proved an effective tool to understand, characterize and quantify the complex brain network. However, much less attention has been paid to methods that quantitatively compare two graphs, a crucial issue in the context of brain networks. Comparing brain networks is indeed mandatory in several network neuroscience applications. Here, we discuss the current state of the art, challenges, and a collection of analysis tools that have been developed in recent years to compare brain networks. We first introduce the graph similarity problem in brain network application. We then describe the methodological background of the available metrics and algorithms of comparing graphs, their strengths and limitations. We also report results obtained in concrete applications from normal brain networks. More precisely, we show the potential use of brain network similarity to build a 'network of networks' that may give new insights into the object categorization in the human brain. Additionally, we discuss future directions in terms of network similarity methods and applications.