# Region2Vec: Community Detection on Spatial Networks Using Graph Embedding with Node Attributes and Spatial Interactions

arXiv: https://arxiv.org/abs/2210.08041 · HTML: https://mlsanity.com/paper/2210.08041/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 16 references that are in the map.
- First posted on arXiv 10 Oct 2022.

## Builds on (most cited first, 5 of 5)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [node2vec: Scalable Feature Learning for Networks](https://mlsanity.com/paper/1607.00653/) (2016)
- [DeepWalk: Online Learning of Social Representations](https://mlsanity.com/paper/1403.6652/) (2014)
- [LINE: Large-scale Information Network Embedding](https://mlsanity.com/paper/1503.03578/) (2015)
- [Multiscale Dynamic Human Mobility Flow Dataset in the U.S. during the COVID-19 Epidemic](https://mlsanity.com/paper/2008.12238/) (2020)

## Built on by (most cited first, 5 of 5)

- [Causal invariant geographic network representations with feature and structural distribution shifts](https://mlsanity.com/paper/2503.19382/) (2025)
- [Geometry over Density: Few-Shot Cross-Domain OOD Detection](https://mlsanity.com/paper/2605.03410/) (2026)
- [GeoAI-Enhanced Community Detection on Spatial Networks with Graph Deep Learning](https://mlsanity.com/paper/2411.15428/) (2024)
- [Optimizing Luxury Vehicle Dealership Networks: A Graph Neural Network Approach to Site Selection](https://mlsanity.com/paper/2408.13961/) (2024)
- [Community Search in Time-dependent Road-social Attributed Networks](https://mlsanity.com/paper/2505.12309/) (2025)

## Abstract (arXiv)

Community Detection algorithms are used to detect densely connected components in complex networks and reveal underlying relationships among components. As a special type of networks, spatial networks are usually generated by the connections among geographic regions. Identifying the spatial network communities can help reveal the spatial interaction patterns, understand the hidden regional structures and support regional development decision-making. Given the recent development of Graph Convolutional Networks (GCN) and its powerful performance in identifying multi-scale spatial interactions, we proposed an unsupervised GCN-based community detection method "region2vec" on spatial networks. Our method first generates node embeddings for regions that share common attributes and have intense spatial interactions, and then applies clustering algorithms to detect communities based on their embedding similarity and spatial adjacency. Experimental results show that while existing methods trade off either attribute similarities or spatial interactions for one another, "region2vec" maintains a great balance between both and performs the best when one wants to maximize both attribute similarities and spatial interactions within communities.