Attentive Graph Enhanced Region Representation Learning

TL;DR

This paper introduces ATGRL, a novel model that captures comprehensive spatial dependencies from multiple graphs to generate rich urban region representations, improving urban analysis tasks.

Contribution

The paper proposes a multi-graph learning framework with noise filtering and a dual-stage fusion mechanism for enhanced urban region embedding.

Findings

Outperforms state-of-the-art methods on real-world datasets

Effectively captures both local and global spatial dependencies

Demonstrates superior performance across multiple downstream tasks

Abstract

Representing urban regions accurately and comprehensively is essential for various urban planning and analysis tasks. Recently, with the expansion of the city, modeling long-range spatial dependencies with multiple data sources plays an important role in urban region representation. In this paper, we propose the Attentive Graph Enhanced Region Representation Learning (ATGRL) model, which aims to capture comprehensive dependencies from multiple graphs and learn rich semantic representations of urban regions. Specifically, we propose a graph-enhanced learning module to construct regional graphs by incorporating mobility flow patterns, point of interests (POIs) functions, and check-in semantics with noise filtering. Then, we present a multi-graph aggregation module to capture both local and global spatial dependencies between regions by integrating information from multiple graphs. In addition, we design a dual-stage fusion module to facilitate information sharing between different views and efficiently fuse multi-view representations for urban region embedding using an improved linear attention mechanism. Finally, extensive experiments on real-world datasets for three downstream tasks demonstrate the superior performance of our model compared to state-of-the-art methods.