Graph neural network training methods and systems
US11227190B1 · kind B1 · utility
Assignee
Inventors
Key dates
| Filing date | Jun 29, 2021 |
| Grant date | Jan 18, 2022 |
| Priority date | — |
| Expiry date | Jun 29, 2041 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N5/022
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Methods, systems, and apparatus for training a graph neural network. An example method includes obtaining a complete graph; dividing the complete graph into a plurality of subgraphs; obtaining a training graph to participate in graph neural network training based on selecting at least one subgraph from the plurality of subgraphs; obtaining, based on the training graph, a node feature vector of each node in the training graph; obtaining a node fusion vector of each current node in the training graph; determining a loss function based on node labels and the node fusion vectors in the training graph; and iteratively training the graph neural network to update parameter values of the graph neural network based on optimizing the loss function.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.