Skip to main content

Region2vec

Region2vec: Community Detection on Spatial Networks Using Graph Embedding with Node Attributes and Spatial Interactions GitHub Repo Region2vec

Abstract: 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 geographic 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.

Paper​

If you find our code useful for your research, you may cite our paper:

Liang, Y., Zhu, J., Ye, W., and Gao, S.. (2022). Region2vec: Community Detection on Spatial Networks Using Graph Embedding with Node Attributes and Spatial Interactions. In Proceedings of 30th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL 2022), November 1-4, 2022, Seattle, WA, USA. DOI: https://doi.org/10.1145/3557915.3560974

@inproceedings{liang2022regions2vec,
  title={Region2vec: Community Detection on Spatial Networks Using Graph Embedding with Node Attributes and Spatial Interactions},
  author={Liang, Yunlei and Zhu, Jiawei and Ye, Wen and Gao, Song },
  booktitle={Proceedings of 30th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
(ACM SIGSPATIAL 2022), November 1-4, 2022, Seattle, WA, USA},
  year={2022},
  pages={1--4},
  doi={10.1145/3557915.3560974}
}

Acknowledgement​

We acknowledge the funding support from the County Health Rankings and Roadmaps program of the University of Wisconsin Population Health Institute, Wisconsin Department of Health Services, and the National Science Foundation funded AI institute [Grant No. 2112606] for Intelligent Cyberinfrastructure with Computational Learning in the Environment (ICICLE). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the funders.