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公开(公告)号：US20210076224A1
公开(公告)日：2021-03-11
申请号：US16945834
申请日：2020-08-01
Inventor： Shengwen Yang , Wenwu Zhu , Mingyang Dai
Abstract: A network convergence method and device, an electronic apparatus, a storage medium are provided. The method includes: taking online information obtained at least from network data as first nodes, and constructing a first network with the first nodes based on an association among the online information, the online information including at least one account and at least one piece of network media information; taking offline information obtained at least from mobile communication data as second nodes, and constructing a second network with the second nodes based on an association among the offline information, the offline information including at least one terminal identifier and location information of a terminal corresponding to the at least one terminal identifier; and converging the first network and the second network by using an association between the account in the online information and the terminal identifier in the offline information, to construct a heterogeneous network.
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Patent Agency Ranking
公开(公告)号：US20210209446A1
公开(公告)日：2021-07-08
申请号：US17210034
申请日：2021-03-23
Inventor： Yixuan Shi , Mingyang Dai , Zixiang Liu
Abstract: The present application discloses a method for generating a user interactive information processing model and a method for processing user interactive information, relates to the technical field of graph neural networks, and particularly relates to a user interactive information processing technology. The method comprises the following steps: determining node representations of network nodes of each layer in a graph neural network in an iterative manner, wherein the node representation of a single-layer node is obtained by representations of neighbor nodes of the single-layer node; adding the attribute feature of each node in each layer of network nodes to the node representation of each layer of network nodes to obtain a graph neural network to be trained; and training the graph neural network to be trained based on existing user interactive information, to obtain a user interactive information processing model, wherein the user interactive information processing model comprises adjusted node representation.
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公开(公告)号：US12141674B2
公开(公告)日：2024-11-12
申请号：US17210034
申请日：2021-03-23
Inventor： Yixuan Shi , Mingyang Dai , Zixiang Liu
Abstract: The present application discloses a method for generating a user interactive information processing model and a method for processing user interactive information, relates to the technical field of graph neural networks, and particularly relates to a user interactive information processing technology. The method comprises the following steps: determining node representations of network nodes of each layer in a graph neural network in an iterative manner, wherein the node representation of a single-layer node is obtained by representations of neighbor nodes of the single-layer node; adding the attribute feature of each node in each layer of network nodes to the node representation of each layer of network nodes to obtain a graph neural network to be trained; and training the graph neural network to be trained based on existing user interactive information, to obtain a user interactive information processing model, wherein the user interactive information processing model comprises adjusted node representation.
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4.发明授权公开(公告)号：US11449558B2
公开(公告)日：2022-09-20
申请号：US17208211
申请日：2021-03-22
Inventor： Mingyang Dai
IPC: G06F16/9035 , G06F16/906 , G06F16/9038 , G06F16/9536 , G06F16/901
Abstract: A relationship network generation method and device, electronic apparatus, and a storage medium are provided, which are related to big data processing. In an implementation, at least one historical text data corresponding to N users within a preset duration is acquired, where N is an integer greater than or equal to 1; sampling is performed on at least one historical text data corresponding to the N users to obtain the sampled text data respectively corresponding to the N users; semantic vectors corresponding to the N users respectively are determined based on the sampled text data corresponding to the N users respectively, and a semantic relationship network involving the N users is generated based on the semantic vectors corresponding to the N users respectively.
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公开(公告)号：US11368855B2
公开(公告)日：2022-06-21
申请号：US16945834
申请日：2020-08-01
Inventor： Shengwen Yang , Wenwu Zhu , Mingyang Dai
Abstract: A network convergence method and device, an electronic apparatus, a storage medium are provided. The method includes: taking online information obtained at least from network data as first nodes, and constructing a first network with the first nodes based on an association among the online information, the online information including at least one account and at least one piece of network media information; taking offline information obtained at least from mobile communication data as second nodes, and constructing a second network with the second nodes based on an association among the offline information, the offline information including at least one terminal identifier and location information of a terminal corresponding to the at least one terminal identifier; and converging the first network and the second network by using an association between the account in the online information and the terminal identifier in the offline information, to construct a heterogeneous network.
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6.发明申请公开(公告)号：US20210209166A1
公开(公告)日：2021-07-08
申请号：US17208211
申请日：2021-03-22
Inventor： Mingyang Dai
IPC: G06F16/9035 , G06F16/901 , G06F16/9038 , G06F16/906 , G06F16/9536
Abstract: A relationship network generation method and device, electronic apparatus, and a storage medium are provided, which are related to big data processing. In an implementation, at least one historical text data corresponding to N users within a preset duration is acquired, where N is an integer greater than or equal to 1; sampling is performed on at least one historical text data corresponding to the N users to obtain the sampled text data respectively corresponding to the N users; semantic vectors corresponding to the N users respectively are determined based on the sampled text data corresponding to the N users respectively, and a semantic relationship network involving the N users is generated based on the semantic vectors corresponding to the N users respectively.
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