SGCNN: structural graph convolutional neural network
US11853903B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Jun 26, 2018 |
| Grant date | Dec 26, 2023 |
| Priority date | — |
| Expiry date | Jul 28, 2042 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06Q50/01
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A computer-implemented method for learning structural relationships between nodes of a graph includes generating a knowledge graph comprising nodes representing a system and applying a graph-based convolutional neural network (GCNN) to the knowledge graph to generate feature vectors describing structural relationships between the nodes. The GCNN comprises: (i) a graph feature compression layer configured to learn subgraphs representing embeddings of the nodes of the knowledge graph into a vector space, (ii) a neighbor nodes aggregation layer configured to derive neighbor node feature vectors for each subgraph and aggregate the neighbor node feature vectors with their corresponding subgraphs to yield aggregated subgraphs, and (iii) a subgraph convolution layer configured to generate the feature vectors based on the aggregated subgraphs. Functional groups of components included in the system may then be identified based on the plurality of feature vectors.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.