Hierarchy aware graph representation learning

A computing device comprising:

• a processor;
• and
• memory comprising processor-executable instructions that when executed by the processor cause performance of operations for hierarchy-aware graph representation learning, the operations comprising:
• enhancing mutual information metrics for graph representation learning based on a parent-child link in a content hierarchy to generate enhanced mutual information metrics, wherein the enhanced mutual information metrics correspond to a combination of at least a first calculation associated with parent nodes sampled from a first set and a second calculation associated with non-parent nodes sampled from a second set;
• enhancing similarity metrics for graph representation learning based on a sibling-sibling link in the content hierarchy to generate enhanced similarity metrics, wherein the enhanced similarity metrics correspond to a combination of at least a third calculation associated with a similarity of first samples of a sibling set and a fourth calculation associated with a similarity of second samples of a non-sibling set;
• enhancing hop metrics based on nodes in the content hierarchy to generate enhanced hop metrics;
• inputting at least one of user feature data or one or more content hierarchies to a machine learning classifier comprising a neural network;
• using the machine learning classifier, comprising the neural network, to generate, at an edge network node of an edge computing network architecture providing coverage to one or more devices within a coverage area of the edge network node, a user content consumption metric based on the enhanced mutual information metrics, the enhanced similarity metrics, and the enhanced hop metrics;
• and
• providing, by the edge network node, a service for selecting content for presentation to the one or more devices within the coverage area of the edge network node using the user content consumption metric generated using the machine learning classifier comprising the neural network, wherein the edge network node is directly connected to the one or more devices.

A non-transitory machine readable medium having stored thereon processor-executable instructions that when executed cause performance of operations for hierarchy-aware graph representation learning, the operations comprising:

• generating a first enhancement function for graph representation learning based on a relationship between a first node in a first level of a content hierarchy and a second node in a second level of the content hierarchy below the first level, wherein the first enhancement function corresponds to a combination of at least a first calculation associated with parent nodes sampled from a first set and a second calculation associated with non-parent nodes sampled from a second set;
• generating a second enhancement function for graph representation learning based on hops between nodes in the content hierarchy, wherein the second enhancement function corresponds to a combination of at least a third calculation associated with a similarity of first samples of a sibling set and a fourth calculation associated with a similarity of second samples of a non-sibling set;
• inputting, at an edge network node of an edge computing network architecture providing coverage to one or more devices within a coverage area of the edge network node, at least one of user feature data or one or more content hierarchies to a machine learning classifier comprising a neural network;
• and
• providing, by the edge network node, a service for selecting content for presentation to the one or more devices within the coverage area of the edge network node using the machine learning classifier comprising the neural network, wherein the edge network node is directly connected to the one or more devices.