Graph neutral networks with attention
US12106217B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | May 16, 2019 |
| Grant date | Oct 1, 2024 |
| Priority date | — |
| Expiry date | Aug 31, 2041 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG16B40/20
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Methods and apparatus are provided for generating a graph neural network (GNN) model based on an entity-entity graph. The entity-entity graph comprising a plurality of entity nodes in which each entity node is connected to one or more entity nodes of the plurality of entity nodes by one or more corresponding relationship edges. The method comprising: generating an embedding based on data representative of the entity-entity graph for the GNN model, wherein the embedding comprises an attention weight assigned to each relationship edge of the entity-entity graph; and updating weights of the GNN model including the attention weights by minimising a loss function associated with at least the embedding; wherein the attention weights indicate the relevancy of each relationship edge between entity nodes of the entity-entity graph. The entity-entity graph may be filtered based on the attention weights of a trained GNN model. The filtered entity-entity graph may be used to update the GNN model or train another GNN model. The trained GNN model may be used to predict link relationship between a first entity and a second entity associated with the entity-entity graph.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.