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公开(公告)号：US20210382945A1
公开(公告)日：2021-12-09
申请号：US17406314
申请日：2021-08-19
Applicant: Advanced New Technologies Co., Ltd.
Inventor： Shaosheng Cao , Qing Cui
IPC: G06F16/901 , G06F17/16 , G06N20/00
Abstract: Implementations of this disclosure provide for obtaining dynamic embedding vectors of nodes in relationship graphs. An example method includes determining N neighboring nodes of a first node of a plurality of nodes; obtaining respective input embedding vectors of the first node and the N neighboring nodes, the input embedding vector of each node being determined based on a respective static embedding vector and a respective positional embedding vector of the node; inputting the respective input embedding vectors of the first node and the N neighboring nodes into a pre-trained embedding model that includes one or more sequentially connected computing blocks, each computing block including a corresponding self-attention layer that outputs N+1 output vectors corresponding to N+1 input vectors; and receiving respective dynamic embedding vectors of the first node and the N neighboring nodes output by the pre-trained embedding model.
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Patent Agency Ranking
公开(公告)号：US11074246B2
公开(公告)日：2021-07-27
申请号：US16805079
申请日：2020-02-28
Applicant: Advanced New Technologies Co., Ltd.
Inventor： Shaosheng Cao , Xinxing Yang , Jun Zhou
Abstract: Implementations of the present specification disclose method, apparatus, and device for processing graph data using a random walk-based process. The process is applicable to either a cluster of machines, a stand-alone machine, or both. In one aspect, the method includes: obtaining, by a cluster, data describing a graph that has nodes and edges between the nodes, wherein the cluster comprises (i) a server cluster that includes a plurality of server machines and (ii) a working machine cluster that includes a plurality of working machines; generating a two-dimensional array based on the data, wherein generating the two-dimensional array comprises generating, for each node included in the graph, a row comprising respective identifiers of adjacent nodes of the node; and generating, based on the two-dimensional array, a random sequence that represents a random walk processing of the data by the cluster.
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公开(公告)号：US11257007B2
公开(公告)日：2022-02-22
申请号：US16587977
申请日：2019-09-30
Applicant: Advanced New Technologies Co., Ltd.
Inventor： Xinxing Yang , Shaosheng Cao , Jun Zhou , Xiaolong Li
Abstract: An N×M dimensional target matrix is generated based on N data samples and M dimensional data features respectively corresponding to the N data samples. Encryption calculation is performed on the N×M dimensional target matrix based on a Principal Component Analysis (PCA) algorithm to obtain an N×K dimensional encryption matrix K is less than M. The N×K dimensional encryption matrix is transmitted to a modeling server. The modeling server trains a machine learning model by using the N×K dimensional encryption matrix as a training sample.
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公开(公告)号：US11288318B2
公开(公告)日：2022-03-29
申请号：US17406314
申请日：2021-08-19
Applicant: Advanced New Technologies Co., Ltd.
Inventor： Shaosheng Cao , Qing Cui
IPC: G06F17/00 , G06F16/901 , G06F17/16 , G06N20/00
Abstract: Implementations of this disclosure provide for obtaining dynamic embedding vectors of nodes in relationship graphs. An example method includes determining N neighboring nodes of a first node of a plurality of nodes; obtaining respective input embedding vectors of the first node and the N neighboring nodes, the input embedding vector of each node being determined based on a respective static embedding vector and a respective positional embedding vector of the node; inputting the respective input embedding vectors of the first node and the N neighboring nodes into a pre-trained embedding model that includes one or more sequentially connected computing blocks, each computing block including a corresponding self-attention layer that outputs N+1 output vectors corresponding to N+1 input vectors; and receiving respective dynamic embedding vectors of the first node and the N neighboring nodes output by the pre-trained embedding model.
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公开(公告)号：US11100167B2
公开(公告)日：2021-08-24
申请号：US16809219
申请日：2020-03-04
Applicant: Advanced New Technologies Co., Ltd.
Inventor： Shaosheng Cao , Qing Cui
IPC: G06F17/00 , G06F16/901 , G06F17/16 , G06N20/00
Abstract: Implementations of this disclosure provide for obtaining dynamic embedding vectors of nodes in relationship graphs. An example method includes determining N neighboring nodes of a first node of a plurality of nodes; obtaining respective input embedding vectors of the first node and the N neighboring nodes, the input embedding vector of each node being determined based on a respective static embedding vector and a respective positional embedding vector of the node; inputting the respective input embedding vectors of the first node and the N neighboring nodes into a pre-trained embedding model that includes one or more sequentially connected computing blocks, each computing block including a corresponding self-attention layer that outputs N+1 output vectors corresponding to N+1 input vectors; and receiving respective dynamic embedding vectors of the first node and the N neighboring nodes output by the pre-trained embedding model.
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公开(公告)号：US10901971B2
公开(公告)日：2021-01-26
申请号：US16736603
申请日：2020-01-07
Applicant: ADVANCED NEW TECHNOLOGIES CO., LTD.
Inventor： Shaosheng Cao , Xinxing Yang , Jun Zhou
IPC: G06F16/00 , G06F16/22 , G06F16/27 , G06F16/901 , H04L29/08
Abstract: Embodiments of the present specification disclose random walking and a cluster-based random walking method, apparatus and device. A solution includes: obtaining information about each node included in graph data, generating, according to the information about each node, a hash table reflecting a correspondence between the node and an adjacent node of the node, and generating a random sequence according to the hash table, to implement random walking in the graph data. The solution is applicable to clusters and single machines.
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