Knowledge graph analytics kernels in high performance computing

A method, performed by a computing system, of mining knowledge graphs at scale comprising:

• obtaining a knowledge graph including a plurality of vertices and a plurality of edges, each of the plurality of edges having an associated weight;
• wherein each of the plurality of vertices represent:
• i) one of a plurality of unique concepts;
• or
• ii) one of a plurality of documents from which one of the plurality of unique concepts was extracted;
• wherein each of the plurality of edges between the plurality of vertices represent:
• 1) a relationship between two or more of the plurality of unique concepts
• 2) a relationship between one or more of the plurality of unique concepts and one or more of the plurality of documents;
• or
• 3) a citation relationship between two documents
• determining shortest paths between all pairs of nodes of the knowledge graph using a scalable, distributed, accelerated, semiring all-pairs shortest path (APSP) algorithm (DSNAPSHOT).

A method, performed by a computing system, of mining knowledge graphs at scale comprising:

• obtaining a matrix representing a knowledge graph having a plurality of nodes and edges there between;
• determining shortest paths between all pairs of nodes of the knowledge graph by processing the matrix with the computing system, wherein the processing is distributed across multiple processors coordinated by a message processing interface that coordinates parallel diagonal updates, panel updates, and min-plus outer products;
• determining a ranking based on the determined shortest paths between all pairs of nodes;
• and
• outputting the ranking to a display.