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 ...

People are increasingly turning to AI for answers, and publishers are scrambling to find ways to consistently be surfaced in ...

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 ...