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：US20240378414A1

公开(公告)日：2024-11-14

申请号：US18692625

申请日：2022-09-20

Applicant: Visa International Service Association

Inventor： Michael Yeh ,  Yan Zheng ,  Huiyuan Chen ,  Zhongfang Zhuang ,  Junpeng Wang ,  Liang Wang ,  Wei Zhang ,  Mengting Gu ,  Javid Ebrahimi

IPC: G06N3/042

Abstract: A method performed by a server computer is disclosed. The method comprises generating a binary compositional code matrix from an input matrix. The binary compositional code matrix is then converted into an integer code matrix. Each row of the integer code matrix is input into a decoder, including plurality of codebooks, to output a summed vector for each row. The method then includes inputting a derivative of each summed vector into a downstream machine learning model to output a prediction.

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：US11922290B2

公开(公告)日：2024-03-05

申请号：US17919898

申请日：2022-05-24

Applicant: Visa International Service Association

Inventor： Zhongfang Zhuang ,  Michael Yeh ,  Wei Zhang ,  Mengting Gu ,  Yan Zheng ,  Liang Wang

IPC: G06N3/0464 ,  G06F17/14

CPC classification number: G06N3/0464 ,  G06F17/142

Abstract: Provided is a system for analyzing a multivariate time series that includes at least one processor programmed or configured to receive a time series of historical data points, determine a historical time period, determine a contemporary time period, determine a first time series of data points associated with a historical transaction metric from the historical time period, determine a second time series of data points associated with a historical target transaction metric from the historical time period, determine a third time series of data points associated with a contemporary transaction metric from the contemporary time period, and generate a machine learning model, wherein the machine learning model is configured to provide an output that comprises a predicted time series of data points associated with a contemporary target transaction metric. Methods and computer program products are also provided.

公开(公告)号：US20230143484A1

公开(公告)日：2023-05-11

申请号：US17919898

申请日：2022-05-24

Applicant: Visa International Service Association

Inventor： Zhongfang Zhuang ,  Michael Yeh ,  Wei Zhang ,  Mengting Gu ,  Yan Zheng ,  Liang Wang

IPC: G06N3/0464 ,  G06F17/14

CPC classification number: G06N3/0464 ,  G06F17/142

Abstract: Provided is a system for analyzing a multivariate time series that includes at least one processor programmed or configured to receive a time series of historical data points, determine a historical time period, determine a contemporary time period, determine a first time series of data points associated with a historical transaction metric from the historical time period, determine a second time series of data points associated with a historical target transaction metric from the historical time period, determine a third time series of data points associated with a contemporary transaction metric from the contemporary time period, and generate a machine learning model, wherein the machine learning model is configured to provide an output that comprises a predicted time series of data points associated with a contemporary target transaction metric. Methods and computer program products are also provided.

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

公开(公告)号：US20220398466A1

公开(公告)日：2022-12-15

申请号：US17836249

申请日：2022-06-09

Applicant: Visa International Service Association

Inventor： Yuhang Wu ,  Linyun He ,  Mengting Gu ,  Lan Wang ,  Shubham Agrawal ,  Yu-San Lin ,  Ishita Bindlish ,  Fei Wang ,  Hao Yang

IPC: G06N5/02

Abstract: Provided is a system for event forecasting using a graph-based machine-learning model that includes at least one processor programmed or configured to receive a dataset of data instances, where each data instance comprises a time series of data points, detect a plurality of motifs representing a plurality of events in the dataset of data instances using a matrix profile-based motif detection technique, generate a bipartite graph representation of the plurality of motifs in a time sequence, and generate a machine-learning model based on the bipartite graph representation of the plurality of motifs in the time sequence, where the machine-learning model is configured to provide an output and the output includes a prediction of whether an event will occur during a specified time interval. Methods and computer program products are also provided.