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公开(公告)号：US11966832B2
公开(公告)日：2024-04-23
申请号：US18264052
申请日：2021-07-02
发明人： Huiyuan Chen , Yu-San Lin , Lan Wang , Michael Yeh , Fei Wang , Hao Yang
IPC分类号： G06N3/0464 , G06N3/047 , G06Q30/0282 , G06Q30/0601
CPC分类号： G06N3/0464 , G06N3/047 , G06Q30/0282 , G06Q30/0631
摘要： 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.
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公开(公告)号：US20240177071A1
公开(公告)日：2024-05-30
申请号：US18281663
申请日：2022-03-30
发明人： Junpeng Wang , Liang Wang , Yan Zheng , Michael Yeh , Shubham Jain , Wei Zhang , Zhongfang Zhuang , Hao Yang
IPC分类号： G06N20/20 , G06F18/2415
CPC分类号： G06N20/20 , G06F18/2415
摘要： Systems, methods, and computer program products may compare machine learning models by identifying data instances with disagreed predictions and learning from the disagreement. Based on a model interpretation technique, differences between the compared machine learning models may be interpreted. Multiple metrics to prioritize meta-features from different perspectives may also be provided.
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3.发明公开公开(公告)号：US20230308464A1
公开(公告)日：2023-09-28
申请号：US18202405
申请日：2023-05-26
发明人： Bo Dong , Yuhang Wu , Yu-San Lin , Michael Yeh , Hao Yang
IPC分类号： H04L9/40
CPC分类号： H04L63/1425 , H04L63/1416 , H04L63/1475
摘要： 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.
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公开(公告)号：US20210224648A1
公开(公告)日：2021-07-22
申请号：US17148984
申请日：2021-01-14
发明人： Zhongfang Zhuang , Michael Yeh , Liang Wang , Wei Zhang , Junpeng Wang
摘要： Described are a system, method, and computer program product for multivariate event prediction using multi-stream recurrent neural networks. The method includes receiving event data from a sample time period and generating feature vectors for each subperiod of each day. The method also includes providing the feature vectors as inputs to a set of first recurrent neural network (RNN) models and generating first outputs for each RNN node. The method further includes merging the first outputs for each same subperiod to form aggregated time-series layers. The method further includes providing the aggregated time-series layers as an input to a second RNN model and generating final outputs for each RNN node of the second RNN model.
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公开(公告)号：US20210109951A1
公开(公告)日：2021-04-15
申请号：US17066852
申请日：2020-10-09
发明人： Michael Yeh , Liang Gou , Wei Zhang , Dhruv Gelda , Zhongfang Zhuang , Yan Zheng
摘要： Provided are systems for analyzing a relational database using embedding learning that may include at least one processor programmed or configured to generate one or more entity-relation matrices from a relational database and perform, for each entity-relation matrix of the one or more entity-relation matrices, an embedding learning process on an embedding associated with an entity. When performing the embedding learning process on the embedding associated with the entity, the at least one processor is programmed or configured to generate an updated embedding associated with the entity. Computer implemented methods and computer-program products are also provided.
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公开(公告)号：US12074893B2
公开(公告)日：2024-08-27
申请号：US18202405
申请日：2023-05-26
发明人： Bo Dong , Yuhang Wu , Yu-San Lin , Michael Yeh , Hao Yang
IPC分类号： H04L9/40
CPC分类号： H04L63/1425 , H04L63/1416 , H04L63/1475
摘要： 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.
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公开(公告)号：US20240127035A1
公开(公告)日：2024-04-18
申请号：US18275598
申请日：2022-02-01
发明人： Michael Yeh , Zhongfang Zhuang , Junpeng Wang , Yan Zheng , Javid Ebrahimi , Liang Wang , Wei Zhang
IPC分类号： G06N3/0455
CPC分类号： G06N3/0455
摘要： 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.
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8.发明公开公开(公告)号：US20240086422A1
公开(公告)日：2024-03-14
申请号：US18509465
申请日：2023-11-15
发明人： Michael Yeh , Liang Gou , Wei Zhang , Dhruv Gelda , Zhongfang Zhuang , Yan Zheng
CPC分类号： G06F16/284 , G06F16/2379 , G06N3/08
摘要： Provided are systems for analyzing a relational database using embedding learning that may include at least one processor programmed or configured to generate one or more entity-relation matrices from a relational database and perform, for each entity-relation matrix of the one or more entity-relation matrices, an embedding learning process on an embedding associated with an entity. When performing the embedding learning process on the embedding associated with the entity, the at least one processor is programmed or configured to generate an updated embedding associated with the entity. Computer-implemented methods and computer program products are also provided.
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公开(公告)号：US11922290B2
公开(公告)日：2024-03-05
申请号：US17919898
申请日：2022-05-24
发明人： Zhongfang Zhuang , Michael Yeh , Wei Zhang , Mengting Gu , Yan Zheng , Liang Wang
IPC分类号： G06N3/0464 , G06F17/14
CPC分类号： G06N3/0464 , G06F17/142
摘要： 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.
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公开(公告)号：US20240046075A1
公开(公告)日：2024-02-08
申请号：US18264052
申请日：2021-07-02
发明人： Huiyuan Chen , Yu-San Lin , Lan Wang , Michael Yeh , Fei Wang , Hao Yang
IPC分类号： G06N3/0464 , G06N3/047
CPC分类号： G06N3/0464 , G06N3/047 , G06Q30/0282
摘要： 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.
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