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 ...

At its core, the spectrum of algorithmic approaches facilitated with a trained behavior over topological features convert networks into vector spaces. <br/><br/>The amount of applications produced by ...

Such data can also be represented as a graph with nodes (such as user, protein) and branches connecting them. To apply the machine learning methods to graphs one needs to learn a representation of the ...

Graphs are usually represented pictorially using dots to represent vertices, and arcs to represent the edges between connected vertices. Arrows can be used to show the orientation of directed edges.

Graph Representation Learning This book is a foundational guide to graph representation learning, including state-of-the art advances, and introduces the highly successful graph neural network (GNN) ...

The representation of numbers in graphs should be proportional to the numbers themselves. Otherwise, they mislead or deceive. Examples of misleading graphs not drawn to scale and a figure with ...

Graph theory has played a vital role in implementation of many mathematical and computer applications [8]. The immensely critical role of graph theory calls for an efficient methodology of ...