论文标题
区域2VEC：使用带有节点属性和空间相互作用的图形嵌入在空间网络上的社区检测
Region2Vec: Community Detection on Spatial Networks Using Graph Embedding with Node Attributes and Spatial Interactions
论文作者
论文摘要
社区检测算法用于检测复杂网络中的密集连接组件，并揭示组件之间的基本关系。作为一种特殊类型的网络，空间网络通常由地理区域之间的连接产生。识别空间网络社区可以帮助揭示空间相互作用模式，了解隐藏的区域结构并支持区域发展决策。鉴于图形卷积网络（GCN）的最新发展及其在识别多尺度空间相互作用方面的强大性能，我们在空间网络上提出了一种基于无监督的GCN社区检测方法“ region2Vec”。我们的方法首先生成共享共同属性并具有强烈空间相互作用的区域的节点嵌入，然后将聚类算法应用于社区根据其嵌入的相似性和空间相邻性来检测社区。实验结果表明，尽管现有方法可以将属性相似性或空间互动互相交易，但“ region2vec”在两个人想要最大化属性相似性和社区内的空间相互作用时保持了巨大的平衡，并且在两者之间取得了巨大的平衡。
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.
