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    MEMORY-AUGMENTED GRAPH CONVOLUTIONAL NEURAL NETWORKS
    1.
    发明申请
    MEMORY-AUGMENTED GRAPH CONVOLUTIONAL NEURAL NETWORKS 有权

    公开(公告)号：US20230027427A1

    公开(公告)日：2023-01-26

    申请号：US17370889

    申请日：2021-07-08

    Applicant: Liheng MA ,  Yingxue ZHANG ,  Mark COATES

    Inventor： Liheng MA ,  Yingxue ZHANG ,  Mark COATES

    IPC: G06N3/08 ,  G06K9/62

    Abstract: System and method for processing a graph that defines a set of nodes and a set of edges, the nodes each having an associated set of node attributes, the edges each representing a relationship that connects two respective nodes, comprising: generating a first node embedding for each node by: generating, for the node and each of a plurality of neighbour nodes, a respective first edge attribute defining a respective relationship type between the node and the neighbour node based on the node attributes of the node and the node attributes of the neighbour node; generating a first neighborhood vector that aggregates information from the generated first edge attributes and the node attributes of the neighbour nodes; generating the first node embedding based on the node attributes of the node and the generated first neighborhood vector.

    RECOMMENDER SYSTEM USING BAYESIAN GRAPH CONVOLUTION NETWORKS
    4.
    发明申请
    RECOMMENDER SYSTEM USING BAYESIAN GRAPH CONVOLUTION NETWORKS 有权

    公开(公告)号：US20210248449A1

    公开(公告)日：2021-08-12

    申请号：US16789325

    申请日：2020-02-12

    Applicant: Jianing SUN ,  Yingxue ZHANG ,  Florence ROBERT-RÉGOL ,  Mark COATES

    Inventor： Jianing SUN ,  Yingxue ZHANG ,  Florence ROBERT-RÉGOL ,  Mark COATES

    IPC: G06N3/04 ,  G06N3/08 ,  G06F16/901 ,  G06F17/16 ,  G06F17/18

    Abstract: System and method for processing an observed bipartite graph that has a plurality of user nodes, a plurality of item nodes, and an observed graph topology that defines edges connecting at least some of the user nodes to some of the item nodes such that at least some nodes have node neighbourhoods comprising edge connections to one or more other nodes. A plurality of random graph topologies are derived that are realizations of the observed graph topology by replacing the node neighbourhoods of at least some nodes with the node neighbourhoods of other nodes. A non-linear function is trained using the plurality of user nodes, plurality of item nodes and plurality of random graph topologies to learn user node embeddings and item node embeddings for the plurality of user nodes and plurality of item nodes, respectively.

    GRAPH PROCESSING SYSTEM AND METHOD USING SPARSE DECOMPOSITION
    8.
    发明申请
    GRAPH PROCESSING SYSTEM AND METHOD USING SPARSE DECOMPOSITION 有权

    公开(公告)号：US20250165751A1

    公开(公告)日：2025-05-22

    申请号：US18516880

    申请日：2023-11-21

    Applicant: Yaochen HU ,  Yingxue ZHANG ,  Mark COATES

    Inventor： Yaochen HU ,  Yingxue ZHANG ,  Mark COATES

    IPC: G06N3/0455 ,  G06N3/08

    Abstract: System, method, and computer readable medium for operating a computer system to process a graph, including receiving a prediction request for a subject node of the graph; obtaining a sparse node approximation for the subject node, the sparse node approximation defining a weighted combination of a subset of nodes of the graph as receptive nodes for the subject node; applying a neural network based transformation function based on the sparse node approximation to generate a node representation for the subject node; performing a prediction task based on the generated node representation to generate a prediction for the subject node; and outputting the prediction.

    METHOD, SYSTEM AND COMPUTER READABLE MEDIUM FOR PROBABILISTIC SPATIOTEMPORAL FORECASTING
    9.
    发明公开
    METHOD, SYSTEM AND COMPUTER READABLE MEDIUM FOR PROBABILISTIC SPATIOTEMPORAL FORECASTING 审中-公开

    公开(公告)号：US20240012875A1

    公开(公告)日：2024-01-11

    申请号：US18365568

    申请日：2023-08-04

    Applicant: Soumyasundar PAL ,  Yingxue ZHANG ,  Mark COATES

    Inventor： Soumyasundar PAL ,  Yingxue ZHANG ,  Mark COATES

    IPC: G06F17/17 ,  G08G1/08

    CPC classification number: G06F17/17 ,  G08G1/08

    Abstract: Probabilistic spatiotemporal forecasting comprising acquiring a time series of observed states from a real-world system, each observed state corresponding to a respective time-step in the time series and including a set of data observations of the real-world system for the respective time-step. For each of a plurality of the time steps in the time series of observed states, a hidden state is generated for the time-step based on an observed state for a prior time-step and an approximated posterior distribution generated for a hidden state for the prior time-step. The use of an approximated posterior distribution can enable improved forecasting in complex, high dimensional settings.

    RECOMMENDATION SYSTEM WITH ADAPTIVE THRESHOLDS FOR NEIGHBORHOOD SELECTION
    10.
    发明申请
    RECOMMENDATION SYSTEM WITH ADAPTIVE THRESHOLDS FOR NEIGHBORHOOD SELECTION 有权

    公开(公告)号：US20220253722A1

    公开(公告)日：2022-08-11

    申请号：US17170647

    申请日：2021-02-08

    Applicant: Haolun WU ,  Chen MA ,  Yingxue ZHANG ,  Mark COATES

    Inventor： Haolun WU ,  Chen MA ,  Yingxue ZHANG ,  Mark COATES

    IPC: G06N5/04 ,  G06Q30/06 ,  G06N20/00 ,  G06F17/16

    Abstract: A recommendation system (RS) for processing an input dataset that identifies a set of users, a set of items, and user-item interaction data about historic interactions between users in the set of users and items in the set of items. The RS is configured to: generate, based on a user-item interaction dataset, a user-user similarity dataset and an item-item similarity dataset, filter the user-user similarity dataset based on a user similarity threshold vector that includes a respective user similarity threshold value for each user, filter the item-item similarity dataset based on an item similarity threshold vector including a respective item similarity threshold value for each item generate a set of user neighbour embeddings based on the filtered user-user similarity dataset, and generating a set of item neighbour embeddings based on the filtered item-item similarity dataset. The RS is also configured to generate a set of relevance scores based on the user neighbour embeddings and the item neighbour embeddings and generating a list of one or more recommended items for each user.

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