Figure 2.Building blocks of geometric deep learning according to the study by Sivakumar (2023). Graph Convolutional Networks (GCNs) are a particular type of neural network that may be used to ...

Learn how to use functions to represent translations, sketches, compressions and reflections of graphs with GCSE Bitesize Maths.

Graph embedding is a very useful dimensionality reduction technique in pattern recognition. In this paper, we develop a novel discriminative dimensionality reduction technique entitled sparsity and ...

Existing approaches can not process geometry polygons with complex shapes, (multiple) holes and are sensitive to geometric transformations (e.g., rotations). We propose Contrastive Graph Autoencoder ...

Graphs have been widely used to model relationships among data. For large graphs, excessive edge crossings make the display visually cluttered and thus difficult to explore. In this paper, we propose a ...

Graph embedding is a very useful dimensionality reduction technique in pattern recognition. In this paper, we develop a novel discriminative dimensionality reduction technique entitled sparsity and ...