用地理智能增强图学习，提升空间网络社区发现精度
GeoAI-Enhanced Community Detection on Spatial Networks with Graph Deep Learning
- 融合属性相似性、地理邻近与空间交互，生成节点嵌入
- 在同时优化属性相似性和空间互动强度上优于多个基线方法
- 适合需兼顾地理特征与属性信息的区域划分任务
空间网络可用于建模地理现象中空间相互作用起关键作用的情况。为分析空间网络及其内部结构，基于图的方法如社区检测被广泛使用。社区检测旨在从网络中提取强连接成分，并揭示节点间的隐藏关系，但通常不考虑属性信息。为同时考虑边级交互和节点属性，本研究提出一类基于图注意力网络（GAT）和图卷积网络（GCN）的地理人工智能增强无监督社区检测方法，称为region2vec。region2vec方法基于属性相似性、地理邻近性和空间交互生成节点神经嵌入，并通过凝聚聚类提取网络社区。所提出的GeoAI方法在多个基线中表现最佳，尤其在同时最大化节点属性相似性和空间交互强度方面。该方法进一步应用于公共卫生中的短缺区域划定问题，展现出在区域化任务中的潜力。
原文摘要 · Abstract (English)
Spatial networks are useful for modeling geographic phenomena where spatial interaction plays an important role. To analyze the spatial networks and their internal structures, graph-based methods such as community detection have been widely used. Community detection aims to extract strongly connected components from the network and reveal the hidden relationships between nodes, but they usually do not involve the attribute information. To consider edge-based interactions and node attributes together, this study proposed a family of GeoAI-enhanced unsupervised community detection methods called region2vec based on Graph Attention Networks (GAT) and Graph Convolutional Networks (GCN). The region2vec methods generate node neural embeddings based on attribute similarity, geographic adjacency and spatial interactions, and then extract network communities based on node embeddings using agglomerative clustering. The proposed GeoAI-based methods are compared with multiple baselines and perform the best when one wants to maximize node attribute similarity and spatial interaction intensity simultaneously within the spatial network communities. It is further applied in the shortage area delineation problem in public health and demonstrates its promise in regionalization problems.
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