3 accepted papers
Graph anomaly detection has received remarkable research interests, and various techniques have been employed for enhancing detection performance. However, existing models tend to learn dataset-specific spurious correlations based on statistical associations. A well-trained model might suffer from p…
The success of graph neural networks (GNNs) has spurred numerous new works leveraging GNNs for modeling multivariate time series anomaly detection. Despite their achieved performance improvements, most of them only consider static graph to describe the spatial-temporal dependencies between time seri…
Inferring the citywide urban traffic flows is critical for numerous smart city applications such as urban planning, traffic control, and transportation management. Urban traffic flow inference problem aims to generate fine-grained flow maps from the coarse-grained ones. It is still challenging due t…