Urban Region Profiling via A Multi-Graph Representation Learning Framework

• URL: http://arxiv.org/abs/2202.02074v1
• Date: Fri, 4 Feb 2022 11:05:37 GMT
• Title: Urban Region Profiling via A Multi-Graph Representation Learning Framework
• Authors: Y. Luo, F. Chung, K. Chen
• Abstract summary: We propose a multi-graph representative learning framework, called Region2Vec, for urban region profiling. Experiments on real-world datasets show that Region2Vec can be employed in three applications and outperforms all state-of-the-art baselines.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Urban region profiling can benefit urban analytics. Although existing studies have made great efforts to learn urban region representation from multi-source urban data, there are still three limitations: (1) Most related methods focused merely on global-level inter-region relations while overlooking local-level geographical contextual signals and intra-region information; (2) Most previous works failed to develop an effective yet integrated fusion module which can deeply fuse multi-graph correlations; (3) State-of-the-art methods do not perform well in regions with high variance socioeconomic attributes. To address these challenges, we propose a multi-graph representative learning framework, called Region2Vec, for urban region profiling. Specifically, except that human mobility is encoded for inter-region relations, geographic neighborhood is introduced for capturing geographical contextual information while POI side information is adopted for representing intra-region information by knowledge graph. Then, graphs are used to capture accessibility, vicinity, and functionality correlations among regions. To consider the discriminative properties of multiple graphs, an encoder-decoder multi-graph fusion module is further proposed to jointly learn comprehensive representations. Experiments on real-world datasets show that Region2Vec can be employed in three applications and outperforms all state-of-the-art baselines. Particularly, Region2Vec has better performance than previous studies in regions with high variance socioeconomic attributes.

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