# US 2025/0165751 A1 · Graph processing system and method using sparse decomposition > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2025/0165751 A1 - Document ID: US20250165751A1 - Status: Published application - Kind: A1 - Application number: 18/516,880 - Filed: 2023-11-21 - Published: 2025-05-22 - Earliest claimed: 2023-11-21 - Inventors: Yaochen HU; Yingxue ZHANG; Mark COATES - Web page: https://patentgrep.com/patent/US20250165751A1 ## CPC - G06N 3/0455: Auto-encoder networks; Encoder-decoder networks - G06N 3/08: Learning methods - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS ## 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. ## Claims ### Claim 1 A method of operating a computer system to process a graph, the graph comprising a data structure that defines nodes that represent entities and edges that represent relationships between the nodes, each node having an associated feature vector that specifies a set of feature values for the node, the method comprising: performing an inference process comprising: 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. ### Claim 2 (depends on 1) The method of claim 1 wherein: obtaining the sparse node approximation for the subject node comprises accessing a stored sparse data structure that identifies, for each of a plurality of the nodes of the graph including the subject node, a respective sparse vector that defines a respective subset of other nodes of the graph as the receptive nodes for that node and respective weight indices for the receptive nodes; and applying the neural network based transformation function to generate the node representation for the subject node comprises: (i) for each receptive node of the subject node, applying the neural network transformation function to the feature vector associated with the receptive node to generate a respective receptive node representation, and (ii) combining the respective receptive node representations according to the respective weight indices for the receptive nodes. ### Claim 3 (depends on 2) The method of claim 2 comprising, prior to performing the inference process, preforming a learning process to learn an approximation of a target graph neural network (GNN) encoder, comprising: collectively learning the respective sparse vectors for the nodes of the graph and a set of parameters for the neural network based transformation function with an objective of jointly optimizing (i) an accuracy of node representations generated by the neural network based transformation function relative to node representations generated by the target GNN encoder and (ii) a sparsity of the respective sparse vectors. ### Claim 4 (depends on 3) The method of claim 3 wherein the learning process is performed to enable a collective time required for obtaining the sparse node approximation and generating the node representation for the subject node during the inference process to be less than a time required for a corresponding inference by the target GNN encoder. ### Claim 5 (depends on 3) The method of claim 3 wherein collectively learning the sparse vectors and the set of parameters comprises: identifying, for each of the nodes, respective candidate nodes that are a subset of the nodes of the graph, wherein the respective sparse vector for each node is learned based on identifying a subset of the respective candidate nodes for the node as the receptive nodes for the node. ### Claim 6 (depends on 5) The method of claim 5 wherein identifying the respective candidate nodes for each node comprises a random walk selection process. ### Claim 7 (depends on 3) The method of claim 3 wherein collectively learning the sparse vectors and the set of parameters comprises performing a plurality of training iterations that each comprise a first phase during which the respective sparse vectors are updated and a second phase during which the set of parameters for the neural network based transformation function are updated. ### Claim 8 (depends on 7) The method of claim 7 wherein a mini-batch of the nodes are processed during each of the training iterations. ### Claim 9 (depends on 3) The method of claim 3 wherein preforming the learning process comprises training a neural network based decoder to perform the prediction task based on node representations generated by the neural network based transformation function subsequent to completion of the collective learning of the respective sparse vectors and the set of parameters. ### Claim 10 (depends on 1) The method of claim 1 wherein the method is performed at a server, the prediction request is received through a network from a user devices, and outputting the prediction comprises sending the prediction through the network to the user device. ### Claim 11 A system comprising one or more processing devices and one or more memories, and being configured for processing a graph, the graph comprising a data structure that defines nodes that represent entities and edges that represent relationships between the nodes, each node having an associated feature vector that specifies a set of feature values for the node, the processing comprising: performing an inference process comprising: 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. ### Claim 12 (depends on 11) The system of claim 11 wherein: obtaining the sparse node approximation for the subject node comprises accessing a stored sparse data structure that identifies, for each of a plurality of the nodes of the graph including the subject node, a respective sparse vector that defines a respective subset of other nodes of the graph as the receptive nodes for that node and respective weight indices for the receptive nodes; and applying the neural network based transformation function to generate the node representation for the subject node comprises: (i) for each receptive node of the subject node, applying the neural network transformation function to the feature vector associated with the receptive node to generate a respective receptive node representation, and (ii) combining the respective receptive node representations according to the respective weight indices for the receptive nodes. ### Claim 13 (depends on 12) The system of claim 12, the processing comprising, prior to performing the inference process, preforming a learning process to learn an approximation of a target graph neural network (GNN) encoder, comprising: collectively learning the respective sparse vectors for the nodes of the graph and a set of parameters for the neural network based transformation function with an objective of jointly optimizing (i) an accuracy of node representations generated by the neural network based transformation function relative to node representations generated by the target GNN encoder and (ii) a sparsity of the respective sparse vectors. ### Claim 14 (depends on 13) The system of claim 13 wherein the learning process is performed to enable a collective time required for obtaining the sparse node approximation and generating the node representation for the subject node during the inference process to be less than a time required for a corresponding inference by the target GNN encoder. ### Claim 15 (depends on 13) The system of claim 13 wherein collectively learning the sparse vectors and the set of parameters comprises: identifying, for each of the nodes, respective candidate nodes that are a subset of the nodes of the graph, wherein the respective sparse vector for each node is learned based on identifying a subset of the respective candidate nodes for the node as the receptive nodes for the node. ### Claim 16 (depends on 15) The system of claim 15 wherein identifying the respective candidate nodes for each node comprises a random walk selection process. ### Claim 17 (depends on 13) The system of claim 13 wherein collectively learning the sparse vectors and the set of parameters comprises performing a plurality of training iterations that each comprise a first phase during which the respective sparse vectors are updated and a second phase during which the set of parameters for the neural network based transformation function are updated. ### Claim 18 (depends on 17) The system of claim 17 wherein a mini-batch of the nodes are processed during each of the training iterations. ### Claim 19 (depends on 13) The system of claim 13 wherein preforming the learning process comprises training a neural network based decoder to perform the prediction task based on node representations generated by the neural network based transformation function subsequent to completion of the collective learning of the respective sparse vectors and the set of parameters. ### Claim 20 A computer readable medium storing non-transient instructions for configuring a computer system to process a graph, the graph comprising a data structure that defines nodes that represent entities and edges that represent relationships between the nodes, each node having an associated feature vector that specifies a set of feature values for the node, the processing comprising: performing an inference process comprising: 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.