Region2Vec: Community Detection on Spatial Networks Using Graph
Embedding with Node Attributes and Spatial Interactions
- URL: http://arxiv.org/abs/2210.08041v1
- Date: Mon, 10 Oct 2022 02:32:55 GMT
- Title: Region2Vec: Community Detection on Spatial Networks Using Graph
Embedding with Node Attributes and Spatial Interactions
- Authors: Yunlei Liang, Jiawei Zhu, Wen Ye, Song Gao
- Abstract summary: We propose 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.
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.
- Score: 2.1793134762413437
- License: http://creativecommons.org/licenses/by/4.0/
- 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 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.
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