Finding multiple temporal relationships among locations can benefit a bunch of urban applications, such as dynamic offline advertising and smart public transport planning. While some efforts have been made on finding static relationships among locations, little attention is focused on studying time-aware location relationships. Indeed, abundant location-based human activities are time-varying and the availability of these data enables a new paradigm for understanding the dynamic relationships in a period among connective locations. To this end, we propose to study a new problem, namely multi-Temporal relationship inference among locations (Trial for short), where the major challenge is how to integrate dynamic and geographical influence under the relationship sparsity constraint. Specifically, we propose a solution to Trial with a graph learning scheme, which includes a spatially evolving graph neural network (SEENet) with two collaborative components: spatially evolving graph convolution module (SEConv) and spatially evolving self-supervised learning strategy (SE-SSL). SEConv performs the intra-time aggregation and inter-time propagation to capture the multifaceted spatially evolving contexts from the view of location message passing. In addition, SE-SSL designs time-aware self-supervised learning tasks in a global-local manner with additional evolving constraint to enhance the location representation learning and further handle the relationship sparsity. Finally, experiments on four real-world datasets demonstrate the superiority of our method over several state-of-the-art approaches.

翻译：发现位置间的多重时态关系能够惠及一系列城市应用，例如动态线下广告和智能公共交通规划。尽管已有研究致力于挖掘位置间的静态关系，但针对时间感知的位置关系研究仍较少受到关注。事实上，丰富的位置相关人类活动具有时变特性，而这些数据的可得性为理解连接位置间周期性的动态关系提供了新范式。为此，我们提出研究一个新问题——即位置间的多重时态关系推断（简称Trial），其主要挑战在于如何在关系稀疏性约束下整合动态影响与地理影响。具体而言，我们提出了一种基于图学习方案的Trial解法，该方案包含一个空间演化图神经网络（SEENet），其由两个协同组件构成：空间演化图卷积模块（SEConv）与空间演化自监督学习策略（SE-SSL）。SEConv通过执行时内聚合与时际传播，从位置消息传递的视角捕获多层面的空间演化上下文。此外，SE-SSL在全局-局部框架下设计时间感知的自监督学习任务，并引入额外的演化约束以增强位置表征学习，从而进一步处理关系稀疏性问题。最终，在四个真实数据集上的实验表明，我们的方法相较于多个现有最优方法具有显著优越性。