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    Method, System, and Computer Program Product for Spatial-Temporal Prediction Using Trained Spatial-Temporal Masked Autoencoders
    21.
    发明申请
    Method, System, and Computer Program Product for Spatial-Temporal Prediction Using Trained Spatial-Temporal Masked Autoencoders 有权

    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.

    System, method, and computer program product for dynamic node classification in temporal-based machine learning classification models
    22.
    发明授权
    System, method, and computer program product for dynamic node classification in temporal-based machine learning classification models 有权

    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.

    System, method, and computer program product for user network activity anomaly detection
    23.
    发明授权
    System, method, and computer program product for user network activity anomaly detection 有权

    公开(公告)号：US12074893B2

    公开(公告)日：2024-08-27

    申请号：US18202405

    申请日：2023-05-26

    Applicant: Visa International Service Association

    Inventor： Bo Dong ,  Yuhang Wu ,  Yu-San Lin ,  Michael Yeh ,  Hao Yang

    IPC: H04L9/40

    CPC classification number: H04L63/1425 ,  H04L63/1416 ,  H04L63/1475

    Abstract: Disclosed are a system, method, and computer program product for user network activity anomaly detection. The method includes generating a multilayer graph from network resource data, and generating an adjacency matrix associated with each layer of the multilayer graph to produce a plurality of adjacency matrices. The method further includes assigning a weight to each adjacency matrix to produce a plurality of weights, and generating a merged single layer graph by merging the plurality of layers based on a weighted sum of the plurality of adjacency matrices using the plurality of weights. The method further includes generating a set of anomaly scores by generating, for each node in the merged single layer graph, an anomaly score. The method further includes determining a set of anomalous users based on the set of anomaly scores, detecting fraudulent network activity based on the set of anomalous users, and executing a fraud mitigation process.

    TIME SERIES PREDICTIVE MODEL FOR ESTIMATING METRIC FOR A GIVEN ENTITY
    24.
    发明公开
    TIME SERIES PREDICTIVE MODEL FOR ESTIMATING METRIC FOR A GIVEN ENTITY 审中-公开

    公开(公告)号：US20240127035A1

    公开(公告)日：2024-04-18

    申请号：US18275598

    申请日：2022-02-01

    Applicant: VISA INTERNATIONAL SERVICE ASSOCIATION

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

    IPC: G06N3/0455

    CPC classification number: G06N3/0455

    Abstract: A method performed by a computer is disclosed. The method comprises receiving interaction data between electronic devices of a plurality of entities. The interaction data is used to form an entity interaction vector containing a number of interactions between the electronic devices of a chosen entity and an entity time series containing a plurality of metrics per unit time of the interactions. An interaction encoder of the computer can generate an interaction hidden representation of the entity interaction vector using embeddings of the plurality of entities. A temporal encoder of the computer can generate a temporal hidden representation of the entity time series. The interaction hidden representation and the temporal hidden representation can be used to generate a predicted scale and a shape estimation of a target interaction metric. The computer can then generate an estimated interaction metric of a time period using the predicted scale and the shape estimation.

    STRUCTURED GRAPH CONVOLUTIONAL NETWORKS WITH STOCHASTIC MASKS FOR NETWORK EMBEDDINGS
    27.
    发明公开
    STRUCTURED GRAPH CONVOLUTIONAL NETWORKS WITH STOCHASTIC MASKS FOR NETWORK EMBEDDINGS 审中-公开

    公开(公告)号：US20240046075A1

    公开(公告)日：2024-02-08

    申请号：US18264052

    申请日：2021-07-02

    Applicant: VISA INTERNATIONAL SERVICE ASSOCIATION

    Inventor： Huiyuan Chen ,  Yu-San Lin ,  Lan Wang ,  Michael Yeh ,  Fei Wang ,  Hao Yang

    IPC: G06N3/0464 ,  G06N3/047

    CPC classification number: G06N3/0464 ,  G06N3/047 ,  G06Q30/0282

    Abstract: A method includes receiving a first data set comprising embeddings of first and second types, generating a fixed adjacency matrix from the first dataset, and applying a first stochastic binary mask to the fixed adjacency matrix to obtain a first subgraph of the fixed adjacency matrix. The method also includes processing the first subgraph through a first layer of a graph convolutional network (GCN) to obtain a first embedding matrix, and applying a second stochastic binary mask to the fixed adjacency matrix to obtain a second subgraph of the fixed adjacency matrix. The method includes processing the first embedding matrix and the second subgraph through a second layer of the GCN to obtain a second embedding matrix, and then determining a plurality of gradients of a loss function, and modifying the first stochastic binary mask and the second stochastic binary mask using at least one of the plurality of gradients.

    System, method, and computer program product for user network activity anomaly detection
    28.
    发明授权
    System, method, and computer program product for user network activity anomaly detection 有权

    公开(公告)号：US11711391B2

    公开(公告)日：2023-07-25

    申请号：US17763282

    申请日：2021-10-18

    Applicant: Visa International Service Association

    Inventor： Bo Dong ,  Yuhang Wu ,  Yu-San Lin ,  Michael Yeh ,  Hao Yang

    IPC: H04L9/40

    CPC classification number: H04L63/1425 ,  H04L63/1416 ,  H04L63/1475

    Abstract: Described are a system, method, and computer program product for user network activity anomaly detection. The method includes receiving network resource data associated with network resource activity of a plurality of users and generating a plurality of layers of a multilayer graph from the network resource data. Each layer of the plurality of layers may include a plurality of nodes, which are associated with users, connected by a plurality of edges, which are representative of node interdependency. The method also includes generating a plurality of adjacency matrices from the plurality of layers and generating a merged single layer graph based on a weighted sum of the plurality of adjacency matrices. The method further includes generating anomaly scores for each node in the merged single layer graph and determining a set of anomalous users based on the anomaly scores.

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