Traffic forecasting plays a critical role in intelligent transportation systems (ITS) in smart
cities. Travelers as well as urban managers rely on reliable traffic information to make their …

In the toxicological testing of new small-molecule compounds, it is desirable to establish in
silico test methods to predict toxicity instead of relying on animal testing. Since quantitative …

Deep graph learning (DGL) has achieved remarkable progress in both business and
scientific areas ranging from finance and e-commerce to drug and advanced material …

To reduce the risk of unexpected cost of rework (COR), a variety of predictive models have
been developed in the construction management literature. However, they primarily focus on …

Graph neural networks (GNNs) are widely utilized to capture the information spreading
patterns in graphs. While remarkable performance has been achieved, there is a new …

Structural adversarial attack methods that attack a graph neural network by perturbing the
edges of the input graph are well-known for their strong effectiveness. However, most …

Accurate traffic forecasting is crucial for the effective functioning of intelligent transportation
systems (ITS). It helps in urban traffic planning, traffic management, and traffic control …

Spatiotemporal Graph Convolutional Neural Network for Robust and Accurate Traffic Flow
Prediction Page 1 Spatiotemporal Graph Convolutional Neural Network for Robust and Accurate …

Graph convolutional networks (GCNs) are fundamental graph neural networks used for
solving node classification problems in graph-structured data. GCNs have been reported to …