Abstract: With the rapid development of mobile Internet in recent years, a large scale of continuous arrival correlative data, namely dynamic streaming graph, are extensively generated in various ...

Graph neural networks have emerged as a leading paradigm for inferring node labels in complex relational data. By extending convolutional and attention operations to arbitrary graph structures, these ...

Abstract: Hyperspectral image (HSI) classification faces critical challenges in effectively modeling the intricate spectral–spatial structures and non-Euclidean relationships. Traditional methods ...

Classic Graph Convolutional Networks (GCNs) often learn node representation holistically, which would ignore the distinct impacts from different neighbors when aggregating their features to update a ...

We additionally note that given our adversarial attacks aim, we approach the GNNs through dense adjacency matrices. In case the user want to use sparse-matrix approach, the attacks should be adapted.