# Urban delineation through the lens of commute networks: Leveraging graph embeddings to distinguish socioeconomic groups in cities

arXiv: https://arxiv.org/abs/2507.11057 · HTML: https://mlsanity.com/paper/2507.11057/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 48 references that are in the map.
- First posted on arXiv 15 Jul 2025.

## Builds on (most cited first, 19 of 19)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [How Powerful are Graph Neural Networks?](https://mlsanity.com/paper/1810.00826/) (2018)
- [Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learning](https://mlsanity.com/paper/1801.07606/) (2018)
- [Recipe for a General, Powerful, Scalable Graph Transformer](https://mlsanity.com/paper/2205.12454/) (2022)
- [A Generalization of Transformer Networks to Graphs](https://mlsanity.com/paper/2012.09699/) (2020)
- [Graph Neural Networks with Learnable Structural and Positional Representations](https://mlsanity.com/paper/2110.07875/) (2021)
- [A Review of Stochastic Block Models and Extensions for Graph Clustering](https://mlsanity.com/paper/1903.00114/) (2019)
- [Rewiring with Positional Encodings for Graph Neural Networks](https://mlsanity.com/paper/2201.12674/) (2022)
- [Efficient Monte Carlo and greedy heuristic for the inference of stochastic block models](https://mlsanity.com/paper/1310.4378/) (2013)
- [Learning 3D Representations of Molecular Chirality with Invariance to Bond Rotations](https://mlsanity.com/paper/2110.04383/) (2021)
- [A General Optimization Technique for High Quality Community Detection in Complex Networks](https://mlsanity.com/paper/1308.3508/) (2013)
- [Community detection in networks using graph embeddings](https://mlsanity.com/paper/2009.05265/) (2020)
- [Community Detection via Maximization of Modularity and Its Variants](https://mlsanity.com/paper/1507.00787/) (2015)
- [A Community Detection and Graph Neural Network Based Link Prediction Approach for Scientific Literature](https://mlsanity.com/paper/2401.02542/) (2024)
- [Information theoretic network approach to socioeconomic correlations](https://mlsanity.com/paper/2009.13825/) (2020)
- [Implicit SVD for Graph Representation Learning](https://mlsanity.com/paper/2111.06312/) (2021)
- [Delineating geographical regions with networks of human interactions in an extensive set of countries](https://mlsanity.com/paper/1310.1829/) (2013)
- [Commute Networks as a Signature of Urban Socioeconomic Performance: Evaluating Mobility Structures with Deep Learning Models](https://mlsanity.com/paper/2507.04027/) (2025)

## Abstract (arXiv)

Delineating areas within metropolitan regions stands as an important focus among urban researchers, shedding light on the urban perimeters shaped by evolving population dynamics. Applications to urban science are numerous, from facilitating comparisons between delineated districts and administrative divisions to informing policymakers of the shifting economic and labor landscapes. In this study, we propose using commute networks sourced from the census for the purpose of urban delineation, by modeling them with a Graph Neural Network (GNN) architecture. We derive low-dimensional representations of granular urban areas (nodes) using GNNs. Subsequently, nodes' embeddings are clustered to identify spatially cohesive communities in urban areas. Our experiments across the U.S. demonstrate the effectiveness of network embeddings in capturing significant socioeconomic disparities between communities in various cities, particularly in factors such as median household income. The role of census mobility data in regional delineation is also noted, and we establish the utility of GNNs in urban community detection, as a powerful alternative to existing methods in this domain. The results offer insights into the wider effects of commute networks and their use in building meaningful representations of urban regions.