Integrated graph neural network for supervised non-obvious relationship detection

A computer program product for non-obvious relationship detection, the computer program product comprising:

• one or more computer readable storage medium, and program instructions stored on the one or more computer readable storage media, the program instructions comprising:
• program instructions to receive a knowledge graph, wherein the knowledge graph includes a plurality of nodes representing entities connected by edges representing relationships;
• program instructions to input a first node and a second node from the knowledge graph into a twin neural network, the twin neural network including two identical neural networks with each having a node attribute embedding layer, at least one neighbor layer, and at least one position layer;
• program instructions to embed attributes of the first node and attributes of the second node separately to generate a first node embedding and a second node embedding using the node attribute embedding layer;
• program instructions to aggregate neighborhood information onto the first node embedding and the second node embedding using the neighbor layer;
• program instructions to aggregate position information onto the first node embedding and the second node embedding using the position layer;
• program instructions to concatenate the neighborhood information and the position information of the first node and the second node, wherein a first output vector and a second output vector is formed;
• and
• program instructions to generate a final score by comparing using a cosine distance of the first output vector to the second output vector, wherein the final score indicates a probability of a non-obvious relationship between the first node and the second node.

A system for non-obvious relationship detection, the system comprising:

• a memory;
• a processor;
• local data storage having stored thereon computer executable code;
• a twin neural network including two identical neural networks configured to detect a non-obvious relationship between a first node and a second node in a knowledge graph;
• a node attribute embedding layer in each of the two identical neural networks configured to entities and their corresponding entity attributes into fixed-length vectors;
• at least one neighbor layer in each of the two identical neural networks configured to aggregate neighborhood information from surrounding the first node and the second node;
• at least one position layer in each of the two identical neural networks configured to capture global position information for each node in the knowledge graph, wherein the position layer produces an output vector of a node embedding containing the global position information;
• a merging component in each of the two identical neural networks configured to concatenate the neighborhood information and the global position information into an output vector;
• and
• a scoring component configured to analyze the output vector of the first node and the second node and produce a final score indicating a likelihood of a non-obvious relationship between the first node and the second node.