By learning the relevant features of clinical images along with the relationships between them, the neural network can outperform more traditional methods.

GREmLN leverages a graph-based architecture to represent gene-gene interactions to predict cell behavior for therapeutic ...

The Graph, the open, universal data layer for web3, announced today a strategic integration with the TRON blockchain network ...

Effective graph visualization is crucial for various fields, including social network analysis, bioinformatics, cybersecurity, and software engineering. By providing a visual understanding of complex ...

So what is ActivityPub? It’s a technology through which social networks can be made interoperable, connecting everything to a single social graph and content-sharing system.

Deep learning techniques are used for data with an underlying non-Euclidean structure, such as graphs or manifolds, and are known as deep geometric learning. These techniques have previously been used ...

What is this book about? Graph neural networks are a highly effective tool for analyzing data that can be represented as a graph, such as social networks, chemical compounds, or transportation ...

Modern digital twins are built atop knowledge graphs, a platform which can not only scale to the vast amounts of data accrued by assets and people, but also deal with the intricate structures and ...