# US 2022/0286366 A1 · Method and apparatus for graph neural network based virtual network management > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2022/0286366 A1 - Document ID: US20220286366A1 - Status: Published application - Kind: A1 - Application number: 17/685,301 - Filed: 2022-03-02 - Published: 2022-09-08 - Earliest claimed: 2021-03-02 - Assignees: Postech Research and Business Development Foundation - Applicants: POSTECH Research and Buisiness Development Foundation - Inventors: Won Ki HONG; Jae Hyoung YOO; Hee Gon KIM - Web page: https://patentgrep.com/patent/US20220286366A1 ## CPC - H04L 41/16: using machine learning or artificial intelligence - G06N 3/045: Combinations of networks - H04L 41/40: using virtualisation of network functions or resources, e.g. SDN or NFV entities - H04L: TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION (arrangements common to telegraphic and telephonic communication H04M) - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS ## Abstract A method for graph neural network-based virtual network management may comprise: preprocessing collected network data; converting node and edge data among the data for each category generated in the preprocessing step into a graph; learning the network data expressed in a matrix generated in the step of converting into the graph using a graph neural network (GNN); and learning node state information for each node generated through the learning using the GNN using a feedforward neural network (FNN) together with service list data. ## Claims ### Claim 1 A method for graph neural network-based virtual network management, comprising the steps of: preprocessing collected network data; converting node and edge data among the data for each category generated in the preprocessing step into a graph; learning the network data expressed in a matrix generated in the step of converting into the graph using a graph neural network (GNN); and learning node state information for each node generated through the learning using the GNN using a feedforward neural network (FNN) together with service list data. ### Claim 2 (depends on 1) The method for graph neural network-based virtual network management according to claim 1, wherein in the step of learning using the FNN, when a number of data in a first class is smaller than a number of data in another second class, a first weight given to learn the first class is set to be greater than a second weight given to learn the second class in order to prevent class imbalance of learning data. ### Claim 3 (depends on 1) The method for graph neural network-based virtual network management according to claim 1, further comprising, after the step of learning using the FNN, the step of generating a virtual network function (VNF) management decision including adding, removing, or leaving as is for all learned nodes. ### Claim 4 (depends on 1) The method for graph neural network-based virtual network management according to claim 1, further comprising, before the preprocessing step, the step of defining data used for machine learning. ### Claim 5 (depends on 4) The method for graph neural network-based virtual network management according to claim 4, wherein in the defining step, network data composed of node and link and service data are defined as VNF (virtual network function) sequence, wherein the node has information on a number of available central processing unit (CPU) cores and available bandwidth, the link includes information on delay and the available bandwidth, and the service data includes information on a node where a service starts, a node where a service arrives, a service type, service duration, cost due to service delay, maximum allowable delay, and demand bandwidth. ### Claim 6 (depends on 4) The method for graph neural network-based virtual network management according to claim 4, further comprising, before the preprocessing step and after the defining step, the step of generating a service information list indicating information of services required for the network at the time of list generation based on the definition of the data, wherein the service information list is generated by adding the services defined using service start time and service execution time with a service list information ID and a most recently generated service. ### Claim 7 (depends on 6) The method for graph neural network-based virtual network management according to claim 6, further comprising, before the preprocessing step and after the generating step, the step of collecting physical network information and virtual network function (VNF) information when the service information list is generated. ### Claim 8 (depends on 7) The method for graph neural network-based virtual network management according to claim 7, further comprising, before the preprocessing step and after the collecting step, the step of preparing ground truth data to be used for machine learning based on the physical network information, the VNF information, and service information. ### Claim 9 (depends on 8) The method for graph neural network-based virtual network management according to claim 8, wherein the step of preparing the ground truth data includes generating a ground truth data set including an installation cost of the VNF, an energy cost, a traffic delivery cost, a service delay cost, or a combination thereof, or using a prestored ground truth data set. ### Claim 10 (depends on 1) The method for graph neural network-based virtual network management according to claim 1, wherein in the preprocessing step, all numerical data of the network data is normalized and expressed, and data for each category among the network data is expressed by converting it into a number or a vector. ### Claim 11 (depends on 1) The method for graph neural network-based virtual network management according to claim 1, wherein in the step of converting into the graph, a node matrix, an edge matrix and a connection matrix, which have node information, edge information, and connection information between the nodes and between the node and the edge, respectively, are generated, the node matrix is a matrix of a number of nodes×a node feature, the edge matrix is a matrix of the number of nodes×an edge feature, the connection matrix is a matrix of the number of nodes×the number of nodes, and has a value of 1 when there is a link between the nodes, and has a value of 0 when there is no link between the nodes. ### Claim 12 An apparatus for graph neural network-based virtual network management, comprising: a preprocessing unit to preprocess collected network data; a conversion unit to convert node and edge data among the data for each category generated in the preprocessing unit into a graph; a first learning unit to learn the network data expressed in a matrix generated in the conversion unit using a graph neural network (GNN); and a second learning unit to learn node state information for each node generated through the first learning unit using a feedforward neural network (FNN) together with service list data. ### Claim 13 (depends on 12) The apparatus for graph neural network-based virtual network management according to claim 12, wherein the second learning unit includes a weight adjustment unit to set a first weight given to learn a first class to be greater than a second weight given to learn another second class in order to prevent class imbalance of learning data when a number of data in the first class is smaller than a number of data in the second class. ### Claim 14 (depends on 12) The apparatus for graph neural network-based virtual network management according to claim 12, wherein a loss function used in the second learning unit is able to be changed according to a purpose of a user, and a learning model of the second learning unit is provided to learn specific information according to the loss function. ### Claim 15 (depends on 12) The apparatus for graph neural network-based virtual network management according to claim 12, wherein the second learning unit includes an output unit to generate a virtual network function (VNF) management decision including adding, removing, or leaving as is for all learned nodes output as a learning result in the second learning unit. ### Claim 16 (depends on 12) The apparatus for graph neural network-based virtual network management according to claim 12, wherein the preprocessing unit normalizes and expresses all numerical data of the network data, and converts and expresses data for each category among the network data into a number or a vector. ### Claim 17 (depends on 12) The apparatus for graph neural network-based virtual network management according to claim 12, wherein the conversion unit generates a node matrix, an edge matrix and a connection matrix, which have node information, edge information, and connection information between the nodes and between the node and the edge, respectively, the node matrix is a matrix of a number of nodes×a node feature, the edge matrix is a matrix of the number of nodes×an edge feature, the connection matrix is a matrix of the number of nodes×the number of nodes, and has a value of 1 when there is a link between the nodes, and has a value of 0 when there is no link between the nodes. ### Claim 18 (depends on 12) The apparatus for graph neural network-based virtual network management according to claim 12, further comprising: a data definition unit to define data used for machine learning; a data generation unit to generate a service information list indicating information of services required for the network at the time of list generation based on the definition of the data; and a data collection unit to collect physical network information and virtual network function (VNF) information when the service information list is generated, wherein the service information list is generated by adding the services defined using service start time and service execution time with a service list information ID and a most recently generated service. ### Claim 19 (depends on 18) The apparatus for graph neural network-based virtual network management according to claim 18, wherein the data definition unit expresses and defines network data composed of node and link and service data as VNF (virtual network function) sequence, the node has information on a number of available central processing unit (CPU) cores and available bandwidth, the link includes information on delay and the available bandwidth, and the service data includes information on a node where a service starts, a node where a service arrives, a service type, service duration, cost due to service delay, maximum allowable delay, and demand bandwidth. ### Claim 20 (depends on 18) The apparatus for graph neural network-based virtual network management according to claim 18, wherein the data generation unit generates a ground truth data set including an installation cost of the VNF, an energy cost, a traffic delivery cost, a service delay cost, other network management cost or a combination thereof, or uses a prestored ground truth data set, as ground truth data to be used for machine learning based on the physical network information, the VNF information, and service information.