公开(公告)号：US20240078416A1

公开(公告)日：2024-03-07

申请号：US18271301

申请日：2023-01-30

Applicant: Visa International Service Association

Inventor： Jiarui Sun ,  Mengting Gu ,  Michael Yeh ,  Liang Wang ,  Wei Zhang

IPC: G06N3/049 ,  G06F17/16

CPC classification number: G06N3/049 ,  G06F17/16

Abstract: Described are a system, method, and computer program product for dynamic node classification in temporal-based machine learning classification models. The method includes receiving graph data of a discrete time dynamic graph including graph snapshots, and node classifications associated with all nodes in the discrete time dynamic graph. The method includes converting the discrete time dynamic graph to a time-augmented spatio-temporal graph and generating an adjacency matrix based on a temporal walk of the time-augmented spatio-temporal graph. The method includes generating an adaptive information transition matrix based on the adjacency matrix and determining feature vectors based on the nodes and the node attribute matrix of each graph snapshot. The method includes generating and propagating initial node representations across information propagation layers using the adaptive information transition matrix and classifying a node of the discrete time dynamic graph subsequent to the first time period based on final node representations.

公开(公告)号：US20250103884A1

公开(公告)日：2025-03-27

申请号：US18889563

申请日：2024-09-19

Applicant: Visa International Service Association

Inventor： Yujie Fan ,  Jiarui Sun ,  Michael Yeh ,  Wei Zhang

IPC: G06N3/08 ,  G06N3/0455

Abstract: Methods, systems, and computer program products are provided for spatial-temporal prediction using trained spatial-temporal masked autoencoders. An example system includes a processor configured to determine a structural dependency graph associated with a networked system. The processor is also configured to receive multivariate time-series data from a first time period associated with the networked system. The processor is further configured to mask the plurality of edges of the structural dependency graph and mask the multivariate time-series data. The processor is further configured to train a spatial-temporal autoencoder based on the masked structural representation and the masked temporal representation. The processor is further configured to generate a prediction using a spatial-temporal machine learning model including the trained spatial-temporal autoencoder, the prediction associated with an attribute of the networked system in a second time period subsequent to the first time period.

公开(公告)号：US12217157B2

公开(公告)日：2025-02-04

申请号：US18271301

申请日：2023-01-30

Applicant: Visa International Service Association

Inventor： Jiarui Sun ,  Mengting Gu ,  Michael Yeh ,  Liang Wang ,  Wei Zhang

IPC: G06N3/04 ,  G06F17/16 ,  G06N3/049

Abstract: Described are a system, method, and computer program product for dynamic node classification in temporal-based machine learning classification models. The method includes receiving graph data of a discrete time dynamic graph including graph snapshots, and node classifications associated with all nodes in the discrete time dynamic graph. The method includes converting the discrete time dynamic graph to a time-augmented spatio-temporal graph and generating an adjacency matrix based on a temporal walk of the time-augmented spatio-temporal graph. The method includes generating an adaptive information transition matrix based on the adjacency matrix and determining feature vectors based on the nodes and the node attribute matrix of each graph snapshot. The method includes generating and propagating initial node representations across information propagation layers using the adaptive information transition matrix and classifying a node of the discrete time dynamic graph subsequent to the first time period based on final node representations.

公开(公告)号：US20250117635A1

公开(公告)日：2025-04-10

申请号：US18987305

申请日：2024-12-19

Applicant: Visa International Service Association

Inventor： Jiarui Sun ,  Mengting Gu ,  Michael Yeh ,  Liang Wang ,  Wei Zhang

IPC: G06N3/049 ,  G06F17/16

Abstract: Described are a system, method, and computer program product for dynamic node classification in temporal-based machine learning classification models. The method includes receiving graph data of a discrete time dynamic graph including graph snapshots, and node classifications associated with all nodes in the discrete time dynamic graph. The method includes converting the discrete time dynamic graph to a time-augmented spatio-temporal graph and generating an adjacency matrix based on a temporal walk of the time-augmented spatio-temporal graph. The method includes generating an adaptive information transition matrix based on the adjacency matrix and determining feature vectors based on the nodes and the node attribute matrix of each graph snapshot. The method includes generating and propagating initial node representations across information propagation layers using the adaptive information transition matrix and classifying a node of the discrete time dynamic graph subsequent to the first time period based on final node representations.

公开(公告)号：US20230351215A1

公开(公告)日：2023-11-02

申请号：US18044736

申请日：2021-09-17

Applicant: VISA INTERNATIONAL SERVICE ASSOCIATION

Inventor： Jiarui Sun ,  Mengting Gu ,  Junpeng Wang ,  Yanhong Wu ,  Liang Wang ,  Wei Zhang

IPC: G06N5/022

CPC classification number: G06N5/022

Abstract: A method includes extracting, by an analysis computer, a plurality of first datasets from a plurality of graph snapshots using a graph structural learning module. The analysis computer can then extract a plurality of second datasets from the plurality of first datasets using a temporal convolution module across the plurality of graph snapshots. The analysis computer can then perform graph context prediction with the plurality of second datasets