Remote sensing scene classification has gained increasing interest in remote sensing
image understanding and feature representation is the crucial factor for classification …

In this work, we introduce a kernel propagation method that enables graph neural networks
(GNNs) to leverage higher-order network structural information without increasing the …

Graph convolutional network (GCN) is an efficient network for learning graph
representations. However, it costs expensive to learn the high‐order interaction …

The medical image is characterized by the inter-class indistinction, high variability, and
noise, where the recognition of pixels is challenging. Unlike previous self-attention based …

With the higher‐order neighborhood information of a graph network, the accuracy of graph
representation learning classification can be significantly improved. However, the current …

The problem of community detection in a network with features at its nodes takes into
account both the graph structure and node features. The goal is to find relatively dense …

Graph convolutional network (GCN) algorithms have been employed to learn graph
embedding due to its inductive inference property, which is extended to GCN with higher …

The information propagation mechanism in graph‐structured networks such as social
networks is the foundation of network security. The graph convolutional network (GCN) is a …

Abstract Graph Networks (GNs) have emerged as essential machine learning tools for
reasoning about how entities in complex systems interact. They support dynamical systems …

Graph structured data is pervasive and generated by online human interactions at an
unprece-dented velocity. Sophisticated features encoded by relational data, such as …