GNNs (Graph Neural Networks) are neural networks used to extract data from graphs. GNN learns a latent representation for each node in an input graph, with each node’s representation being an ...

In this article, we will discuss the graph representation fundamentals in detail along with how machine learning can be performed using graph data. The major points that we will cover in this article ...

A graph structure is a powerful mathematical abstraction, which can not only represent information about individuals but also capture the interactions between individuals for reasoning. Geometric ...

In this paper, we propose a novel two-stage framework for the representation of chemical molecule graphs based on the strengths of Graph Isomorphism Networks (GINs) and Siamese autoencoders. In the ...

Consequently, there are many applications of graph representation learning to model and analyze massive and multimodal biological data generated from high-throughput omics technology, epidemiological ...