The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.
The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.
Patent No.:
Date of Patent:
Jul. 12, 2022
Filed:
Sep. 23, 2019
International Business Machines Corporation, Armonk, NY (US);
Trustees of Tufts College, Medford, MA (US);
Ramot AT Tel-aviv University Ltd., Tel-Aviv, IL;
Lior Horesh, North Salem, NY (US);
Osman Asif Malik, Boulder, CO;
Shashanka Ubaru, Ossining, NY (US);
Misha E. Kilmer, Medfield, MA (US);
Haim Avron, Tel Aviv, IL;
INTERNATIONAL BUSINESS MACHINES CORPORATION, Armonk, NY (US);
Trustees of Tufts College, Medford, MA (US);
RAMOT AT TEL-AVIV UNIVERSITY LTD., Tele-Aviv, IL;
Abstract
A computer-implemented method for analyzing a time-varying graph is provided. The time-varying graph includes nodes representing elements in a network, edges representing transactions between elements, and data associated with the nodes and the edges. The computer-implemented method includes constructing, using a processor, adjacency and feature matrices describing each node and edge of each time-varying graph for stacking into an adjacency tensor and describing the data of each time-varying graph for stacking into a feature tensor, respectively. The adjacency and feature tensors are partitioned into adjacency and feature training tensors and into adjacency and feature validation tensors, respectively. An embedding model and a prediction model are created using the adjacency and feature training tensors. The embedding and prediction models are validated using the adjacency and feature validation tensors to identify an optimized embedding-prediction model pair.