论文标题
有效的城市地区代表性学习使用异质城市图注意网络（HUGAT）
Effective Urban Region Representation Learning Using Heterogeneous Urban Graph Attention Network (HUGAT)
论文作者
论文摘要
揭示塑造城市环境的隐藏模式对于了解其动态并使城市变得更聪明至关重要。最近的研究表明，学习城市地区的表示可能是揭示城市地区内在特征的有效策略。但是，现有的研究缺乏将多样性纳入城市数据源的。在这项工作中，我们提出了异构的城市图注意网络（HUGAT），该网络包含了各种城市数据集的异质性。在Hugat中，异质的Urban图（HUG）将单个图形结构中的地理空间和暂时者运动变化融合在一起。鉴于一个拥抱，一组元路径旨在将丰富的城市语义作为节点之间的综合关系捕获。使用异质图注意网络（HAN）进行区域嵌入。 Hugat旨在同时考虑城市地理空间和移动性变化的多个学习目标。在我们对纽约市数据的广泛实验中，Hugat的表现优于所有最新模型。此外，它在犯罪，平均个人收入和自行车流以及空间聚类任务的各种预测任务中表现出了强大的概括能力。
Revealing the hidden patterns shaping the urban environment is essential to understand its dynamics and to make cities smarter. Recent studies have demonstrated that learning the representations of urban regions can be an effective strategy to uncover the intrinsic characteristics of urban areas. However, existing studies lack in incorporating diversity in urban data sources. In this work, we propose heterogeneous urban graph attention network (HUGAT), which incorporates heterogeneity of diverse urban datasets. In HUGAT, heterogeneous urban graph (HUG) incorporates both the geo-spatial and temporal people movement variations in a single graph structure. Given a HUG, a set of meta-paths are designed to capture the rich urban semantics as composite relations between nodes. Region embedding is carried out using heterogeneous graph attention network (HAN). HUGAT is designed to consider multiple learning objectives of city's geo-spatial and mobility variations simultaneously. In our extensive experiments on NYC data, HUGAT outperformed all the state-of-the-art models. Moreover, it demonstrated a robust generalization capability across the various prediction tasks of crime, average personal income, and bike flow as well as the spatial clustering task.
