The mockery about “chart crimes” — big boo-boos with data graphics — nearly overshadowed the technology upgrades announced by ...

Graph-theoretical (GT) representations, conceptually analogous to chemical formulas, offer a powerful and versatile framework for describing the structure of nanomaterials─including complex assemblies ...

In graphical-based chemistry machine learning (ML) models, a precise atomic correlation depiction, known as molecular topology, can significantly increase the model’s performance. However, owing to ...

Both graph databases and knowledge graphs “have similarities but serve different purposes,” said Shalvi Singh, senior product manager at Amazon AI. “Graph databases serve as the underlying ...

With the necessary specifications for a new representation of the latter as directed Euler graphs, we were able to develop an iterative algorithm that allows to determine a shortest path in a graph, ...

These pairs are extracted from a graph model of residue–residue interactions. Communities of important residue groups are detected, and critical sites are identified by their eigenvector centrality in ...

Graph neural networks (GNNs) (45) represent a significant advancement as they directly operate on graph data, aggregating each node's features with those of its neighbors. Building on GNNs, certain ...

In this stage, the filtered audio is represented as a graph using the proposed spiral pattern information extraction method. Graph coloring algorithms are applied to convert the graph into a visual ...

Contrastive graph clustering (CGC) has become a prominent method for self-supervised representation learning by contrasting augmented graph data pairs. However, the performance of CGC methods ...