Graph data, essential in fields like knowledge representation and social networks, often involves large networks with many nodes and edges. Transmitting these graphs can be highly inefficient due to ...

This system takes an unstructured text document, and uses an LLM of your choice to extract knowledge in the form of Subject-Predicate-Object (SPO) triplets, and visualizes the relationships as an ...

Graph neural network (GNN) models are increasingly being used for the classification of electroencephalography (EEG) data. However, GNN-based diagnosis of neurological disorders, such as Alzheimer’s ...

Here, molecular graphs derived from the one-electron density matrix are introduced within a more general effort to explore whether incorporating electronic structure awareness allows a single model to ...

Data is at the heart of our thousands of services at LinkedIn. Services want to subscribe to data published by other services. These subscribers need to process all the data from the originating ...