IEEE

Graph Convolutional Networks Based on Aggregated Multilevel kNN Graph for Image Classification

Abstract: Graph Convolutional Networks (GCNs) rely heavily on the quality of the input graph, which is often defined by a fixed neighborhood size. This work explores two complementary strategies to ...
EurekAlert!

PolyU develops novel AI graph neural network models to unravel interdisciplinary complexities in image recognition and neuroscience

As an emerging technology in the field of artificial intelligence (AI), graph neural networks (GNNs) are deep learning models designed to process graph-structured data. Currently, GNNs are effective ...
IEEE

Spatio-Temporal Graph Convolutional Network Incorporating Knowledge Graph Embeddings for Hydrological Time Series Prediction

Abstract: This paper proposes a spatio-temporal graph convolutional network incorporating knowledge graph embeddings for hydrological time series prediction. A knowledge graph is constructed to ...
Scientific Research Publishing

Baldassarre, F. and Azizpour, H. (2019) Explainability Techniques for Graph Convolutional Networks. arXiv: 1905.13686.

ABSTRACT: Predicting molecular properties is essential for advancing for advancing drug discovery and design. Recently, Graph Neural Networks (GNNs) have gained prominence due to their ability to ...