With recent advances in sensing technologies, a myriad of spatio-temporal data has been
generated and recorded in smart cities. Forecasting the evolution patterns of spatio-temporal …

Graph Neural Networks (GNNs) have garnered considerable interest due to their
exceptional performance in a wide range of graph machine learning tasks. Nevertheless, the …

Being a kind of non-Euclidean data, spatiotemporal graph data exists everywhere from traffic
flow, air quality index to crime case, etc. Unlike the raster data, the irregular and disordered …

Graphs are structured data that models complex relations between real-world entities.
Heterophilic graphs, where linked nodes are prone to be with different labels or dissimilar …

Crime event analysis (CEA) has become increasingly important in assisting humans in
preventing future crimes. A fundamental challenge in the research community lies in the …

Spatial-temporal graph learning has emerged as the state-of-the-art solution for modeling
structured spatial-temporal data in learning region representations for various urban sensing …

Backdoor attacks in the traditional graph neural networks (GNNs) field are easily detectable
due to the dilemma of confusing labels. To explore the backdoor vulnerability of GNNs and …

Forecasting the number of visits to Points-of-Interest (POI) in an urban area is critical for
planning and decision making in various application domains, from urban planning and …

With the progress of human daily interaction activities and the development of industrial
society, a large amount of media data and sensor data become accessible. Humans collect …

Crime forecasting is a critical component of urban analysis and essential for stabilizing
society today. Unlike other time series forecasting problems, crime incidents are sparse …