Multi-Temporal Relationship Inference in Urban Areas
Shuangli Li Jingbo Zhou Ji Liu Tong Xu Enhong Chen Hui Xiong
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.