# Comparative Survey of Multigraph Integration Methods for Holistic Brain Connectivity Mapping

arXiv: https://arxiv.org/abs/2204.05110 · HTML: https://mlsanity.com/paper/2204.05110/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 133 references that are in the map.
- First posted on arXiv 5 Apr 2022.

## Builds on (most cited first, 13 of 13)

- [How Powerful are Graph Neural Networks?](https://mlsanity.com/paper/1810.00826/) (2018)
- [Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networks](https://mlsanity.com/paper/1810.02244/) (2018)
- [Graph Neural Networks in Network Neuroscience](https://mlsanity.com/paper/2106.03535/) (2021)
- [Deep Graph Normalizer: A Geometric Deep Learning Approach for Estimating Connectional Brain Templates](https://mlsanity.com/paper/2012.14131/) (2020)
- [Multi-View Multi-Graph Embedding for Brain Network Clustering Analysis](https://mlsanity.com/paper/1806.07703/) (2018)
- [Hierarchical Graph Clustering using Node Pair Sampling](https://mlsanity.com/paper/1806.01664/) (2018)
- [Foreseeing Brain Graph Evolution Over Time Using Deep Adversarial Network Normalizer](https://mlsanity.com/paper/2009.11166/) (2020)
- [MGN-Net: a multi-view graph normalizer for integrating heterogeneous biological network populations](https://mlsanity.com/paper/2104.03895/) (2021)
- [Non-isomorphic Inter-modality Graph Alignment and Synthesis for Holistic Brain Mapping](https://mlsanity.com/paper/2107.06281/) (2021)
- [Supervised Multi-topology Network Cross-diffusion for Population-driven Brain Network Atlas Estimation](https://mlsanity.com/paper/2009.11054/) (2020)
- [Recurrent Multigraph Integrator Network for Predicting the Evolution of Population-Driven Brain Connectivity Templates](https://mlsanity.com/paper/2110.03453/) (2021)
- [One Representative-Shot Learning Using a Population-Driven Template with Application to Brain Connectivity Classification and Evolution Prediction](https://mlsanity.com/paper/2110.11238/) (2021)
- [SimiNet: a Novel Method for Quantifying Brain Network Similarity](https://mlsanity.com/paper/1709.07211/) (2017)

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

- [Predicting Brain Multigraph Population From a Single Graph Template for Boosting One-Shot Classification](https://mlsanity.com/paper/2209.06005/) (2022)
- [Multi-Sensory Cognitive Computing for Learning Population-level Brain Connectivity](https://mlsanity.com/paper/2508.11436/) (2025)
- [CogGNN: Cognitive Graph Neural Networks in Generative Connectomics](https://mlsanity.com/paper/2509.10864/) (2025)
- [Metadata-Driven Federated Learning of Connectional Brain Templates in Non-IID Multi-Domain Scenarios](https://mlsanity.com/paper/2403.09139/) (2024)

## Abstract (arXiv)

One of the greatest scientific challenges in network neuroscience is to create a representative map of a population of heterogeneous brain networks, which acts as a connectional fingerprint. The connectional brain template (CBT), also named network atlas, presents a powerful tool for capturing the most representative and discriminative traits of a given population while preserving its topological patterns. The idea of a CBT is to integrate a population of heterogeneous brain connectivity networks, derived from different neuroimaging modalities or brain views (e.g., structural and functional), into a unified holistic representation. Here we review current state-of-the-art methods designed to estimate well-centered and representative CBT for populations of single-view and multi-view brain networks. We start by reviewing each CBT learning method, then we introduce the evaluation measures to compare CBT representativeness of populations generated by single-view and multigraph integration methods, separately, based on the following criteria: centeredness, biomarker-reproducibility, node-level similarity, global-level similarity, and distance-based similarity. We demonstrate that the deep graph normalizer (DGN) method significantly outperforms other multi-graph and all single-view integration methods for estimating CBTs using a variety of healthy and disordered datasets in terms of centeredness, reproducibility (i.e., graph-derived biomarkers reproducibility that disentangle the typical from the atypical connectivity variability), and preserving the topological traits at both local and global graph-levels.