US 11,853,903 B2
SGCNN: structural graph convolutional neural network
Arquimedes Martinez Canedo, Plainsboro, NJ (US); Jiang Wan, Irvine, CA (US); and Blake Pollard, Jupiter, FL (US)
Assigned to SIEMENS AKTIENGESELLSCHAFT, Munich (DE)
Filed by Siemens Aktiengesellschaft, Munich (DE)
Filed on Jun. 26, 2018, as Appl. No. 16/018,232.
Claims priority of provisional application 62/630,294, filed on Feb. 14, 2018.
Claims priority of provisional application 62/613,548, filed on Jan. 4, 2018.
Claims priority of provisional application 62/564,318, filed on Sep. 28, 2017.
Prior Publication US 2019/0095806 A1, Mar. 28, 2019
Int. Cl. G06N 5/02 (2023.01); G06N 5/04 (2023.01); G06F 17/15 (2006.01); G06N 20/00 (2019.01); G06F 16/90 (2019.01); G06Q 10/04 (2023.01); G06N 3/04 (2023.01); G06N 5/022 (2023.01); G06N 5/046 (2023.01); G06F 16/901 (2019.01); G06F 18/21 (2023.01); G06N 3/045 (2023.01); G06Q 50/00 (2012.01)
CPC G06N 5/022 (2013.01) [G06F 16/9024 (2019.01); G06F 17/15 (2013.01); G06F 18/21 (2023.01); G06N 3/045 (2023.01); G06N 5/046 (2013.01); G06N 20/00 (2019.01); G06Q 10/04 (2013.01); G06Q 50/01 (2013.01)] 16 Claims
OG exemplary drawing
 
1. 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, the plurality of subgraphs being selected from a heterogeneous knowledge graph as a selected number of neighboring nodes from each node in the knowledge graph to define a path associated with the node, and a second selected number of paths associated with the node,
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 context generator for generating context of each node in the knowledge graph by:
for each target node, selecting all nodes within a certain radius of the target node;
randomly selecting a number n nodes from the selected nodes to generate a context of the target node; and
selecting a number m contexts for each target node, each context having a size n;
a subgraph convolution layer configured to generate the plurality of feature vectors based on the aggregated subgraphs and contexts; and
identifying functional groups of components included in the system based on the plurality of feature vectors.