US 2019/0095806 A1 · SGCNN: Structural Graph Convolutional Neural Network

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.

Claims18

Dependent claims

A computer-implemented method for learning structural relationships between nodes of a graph, the method comprising:

generating a knowledge graph comprising a plurality of nodes representing a system;
applying a graph-based convolutional neural network (GCNN) to the knowledge graph to generate a plurality of feature vectors describing structural relationships between the nodes, wherein the GCNN comprises:
a graph feature compression layer configured to learn a plurality of subgraphs representing embeddings of the nodes of the knowledge graph into a vector space,
a neighbor nodes aggregation layer configured to (i) derive a plurality of neighbor node feature vectors for each subgraph and (ii) aggregate the neighbor node feature vectors with their corresponding subgraphs to yield a plurality of aggregated subgraphs,
a subgraph convolution layer configured to generate the plurality of feature vectors based on the aggregated subgraphs;
and
identifying functional groups of components included in the system based on the plurality of feature vectors.

4 more elements

Dependencies are inferred from the claim text.

SGCNN: Structural Graph Convolutional Neural Network

Life of this patent

SGCNN: Structural Graph Convolutional Neural Network

Life of this patent

Abstract

Claims18