Complex network disease transmission tracing method based on graph neural network

Abstract

The invention relates to a complex network disease transmission tracing method based on a graph neural network. The method comprises the following steps: firstly, acquiring an individual infection sample on a contact network by adopting a propagation dynamics model; then, node topological characteristics on the contact network are obtained based on a centrality method, and the node topological characteristics are added into the individual infection sample; then, performing data preprocessing on the individual infection sample, and dividing the individual infection sample into a training sample, a verification sample and a test sample; then, the training sample and the verification sample are put into a neural network model based on a message passing mechanism for training, and the neural network model based on the message passing mechanism comprises a feature coding module, an MPNN network, a propagation controller module and a priori knowledge module; and finally, inputting the test sample into a trained neural network model based on a message passing mechanism to obtain a prediction result of the propagation source of the test sample. And the propagation source on the complex network can be efficiently and accurately deduced.

Description

Technical Field

[0001] The present application relates to the technical field of infectious disease monitoring, and in particular to a method for tracing the spread of complex network diseases based on graph neural networks. Background Art

[0002] When responding to emerging infectious diseases and public health events, accurately tracing the source of disease transmission is the key to controlling the spread of the epidemic and formulating precise prevention and control measures. At present, the work of tracing and tracing the source of transmission mainly focuses on locating the source of sudden transmission information in the real world, aiming to implement effective control and risk assessment through this process. Given that the transmission phenomena caused by the source of transmission (such as the threat of major epidemics to human health) are common in real life, therefore, the rapid and accurate identification and location of the source of transmission is of vital importance to reducing social losses.

[0003] In the existing technology, traditional disease transmission models are usually used for source tracing, which mainly relies on the algorithm of infection source centrality, and identifies potential sources of transmission by calculating the node centrality of the infected subgraph in a complex network. This type of method assumes that the source node is usually located at a key position in the infected subgraph during the transmission process and has a high centrality value, so it can be used as a potential source candidate. However, when dealing with transmission tracing problems in complex network environments, it often faces problems such as low accuracy and low computational efficiency.

[0004] Therefore, in the relevant technologies, there is an urgent need for a method that can improve the accuracy and efficiency of tracing the spread of diseases in complex networks. Summary of the invention

[0005] Based on this, it is necessary to provide a complex network disease propagation tracing method based on graph neural network, which can improve the accuracy and efficiency of complex network disease propagation tracing in response to the above technical problems.

[0006] In the first aspect, the present application provides a complex network disease transmission tracing method based on graph neural network. The method comprises:

[0007] A transmission dynamics model is used to obtain individual infection samples on the contact network;

[0008] Obtaining node topological features on the contact network based on a centrality method and adding them to the individual infection sample;

[0009] Performing data preprocessing on the individual infection samples and dividing them into training samples, verification samples and test samples;

[0010] Putting the training samples and the verification samples into a neural network model based on a message passing mechanism for training, wherein the neural network model based on a message passing mechanism includes a feature encoding module, an MPNN network, a propagation controller module, and a priori knowledge module;

[0011] The test sample is input into a trained neural network model based on a message passing mechanism to obtain a prediction result of the propagation source of the test sample.

[0012] Optionally, in one embodiment of the present application, the propagation dynamics model adopts a propagation dynamics model based on individual SIR.

[0013] Optionally, in one embodiment of the present application, the contact network includes a random graph network, a scale-free network, a citation network, a dolphin social network, a karate club social network, and a Les Miserables character co-occurrence network.

[0014] Optionally, in one embodiment of the present application, the node topology feature includes betweenness centrality, closeness centrality, and degree centrality, and the calculation formula of the node topology feature is:

[0015]

[0016] Z i =[B i ∥C i ∥D i ]

[0017] Among them, B i represents the end centrality, σ st represents the number of shortest paths from node s to node t, σ st (i) represents the number of shortest paths from s to t passing through node i, C i represents close centrality, d(i,j) represents the shortest path distance between node i and node j, and D i represents degree centrality, k i represents the degree of node i, that is, the number of nodes connected to the node, A ij is an element in the contact network adjacency matrix, indicating whether node i and node j are connected. If so, then A ij =1, otherwise 0, Z i Represents the topological characteristics of the node, and ∥ represents the splicing operation.

[0018] Optionally, in one embodiment of the present application, the data preprocessing includes:

[0019] Unreasonable individual infection samples are eliminated, and the node information and edge information of the contact network are one-hot encoded.

[0020] Optionally, in one embodiment of the present application, the step of placing the training sample and the verification sample into a neural network model based on a message passing mechanism for training includes:

[0021] The cross entropy loss function is used as the optimization objective, and the formula of the optimization objective function is:

[0022]

[0023] in, represents the loss to be optimized, y i represents the true label of node i. If node i is the source node, then y i =1, otherwise y i =0, represents the prediction score of node i as the source, Represents the prediction score of node j as the source.

[0024] Optionally, in one embodiment of the present application, Top-1 accuracy, Top-5 accuracy and Hop-1 accuracy are used as evaluation indicators to evaluate the inference ability of the neural network model based on the message passing mechanism.

[0025] In the second aspect, the present application also provides a complex network disease transmission tracing device based on graph neural network. The device includes:

[0026] A data acquisition module, used to acquire individual infection samples on the contact network using a propagation dynamics model;

[0027] A node topology feature acquisition module, used to acquire node topology features on the contact network based on a centrality method, and add the features to the individual infection sample;

[0028] A data preprocessing module, used to perform data preprocessing on the individual infection samples and divide them into training samples, verification samples and test samples;

[0029] A model training module, used for putting the training samples and the verification samples into a neural network model based on a message passing mechanism for training, wherein the neural network model based on a message passing mechanism includes a feature encoding module, an MPNN network, a propagation controller module and a priori knowledge module;

[0030] The propagation tracing module is used to input the test sample into a trained neural network model based on the message passing mechanism to obtain the prediction result of the propagation source of the test sample.

[0031] In a third aspect, the present application further provides a computer device, wherein the computer device comprises a memory and a processor, wherein the memory stores a computer program, and the processor executes the steps of the methods described in the above embodiments.

[0032] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of the method described in each of the above embodiments are implemented.

[0033] The above-mentioned complex network disease transmission tracing method based on graph neural network, first, adopts the transmission dynamics model to obtain individual infection samples on the contact network; then, obtains the node topology features on the contact network based on the centrality method, and adds them to the individual infection samples; then, preprocesses the data of the individual infection samples and divides them into training samples, verification samples and test samples; then, puts the training samples and verification samples into the neural network model based on the message passing mechanism for training, wherein the neural network model based on the message passing mechanism includes a feature encoding module, an MPNN network, a transmission controller module and a priori knowledge module; finally, inputs the test sample into the trained neural network model based on the message passing mechanism to obtain the prediction result of the transmission source of the test sample. That is to say, by simulating the transmission process using the transmission dynamics model, recording the infection snapshot of the individual on the contact network, analyzing the network node characteristics through the topological features based on the centrality, considering the characteristics of the nodes and edges on the contact network, and using the neural network model based on the message passing mechanism for deep learning, it is possible to efficiently and accurately infer the transmission source on the complex network, thereby providing technical support for disease prevention. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 This is an application environment diagram of a complex network disease propagation tracing method based on a graph neural network in one embodiment;

[0035] Figure 2 A flowchart of a complex network disease propagation tracing method based on a graph neural network in one embodiment;

[0036] Figure 3 is a schematic diagram of the structure of a neural network model based on a message passing mechanism in one embodiment;

[0037] Figure 4 is a schematic diagram of the structure of a feature coding module in one embodiment;

[0038] Figure 5 It is a structural block diagram of a complex network disease transmission tracing device based on a graph neural network in one embodiment;

[0039] Figure 6 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0040] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0041] The present application embodiment provides a complex network disease transmission tracing method based on graph neural network, which can be applied to Figure 1 In the application environment shown. Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store the data that the server 104 needs to process. The data storage system can be integrated on the server 104, or it can be placed on the cloud or other network servers. Among them, the terminal 102 can be but is not limited to various personal computers, laptops, smart phones, tablets, Internet of Things devices and portable wearable devices. The Internet of Things devices can be smart speakers, smart TVs, smart air conditioners, smart car-mounted devices, etc. Portable wearable devices can be smart watches, smart bracelets, head-mounted devices, etc. The server 104 can be implemented with an independent server or a server cluster consisting of multiple servers.

[0042] In one embodiment, Figure 2 As shown in the figure, a complex network disease propagation tracing method based on graph neural network is provided. Figure 1 The server in the example is used to illustrate the following steps:

[0043] S201: Use the transmission dynamics model to obtain individual infection samples on the contact network.

[0044] In the embodiment of the present application, first, a propagation simulation is performed on the contact network using a propagation dynamics model, and individual infection situations on the contact network at different times are recorded to form individual infection samples. This process is called a snapshot. Optionally, the propagation dynamics model includes a SIR model, a SEIR model, a SIS model, and other variants.

[0045] In one embodiment of the present application, the propagation dynamics model adopts a propagation dynamics model based on individual SIR.

[0046] In one embodiment of the present application, the propagation dynamics model adopts a propagation dynamics model based on individual SIR. In the propagation dynamics model based on individual SIR, S represents susceptible individuals, I represents infected individuals, R represents recovered individuals, and the contacts between individuals are represented as edges on the contact network. When an individual in a susceptible state contacts an individual in an infected state, the individual will be infected with a probability of β and thus turn into an infected state. The individual in the infected state will recover with a probability of γ at each time step and thus turn into a recovered state.

[0047] The infectious disease simulation process based on the SIR propagation dynamics model is as follows: randomly initialize the nodes on the network as source nodes, and give the propagation time step t of this simulation, the basic reproduction number (R0) and recovery rate (γ) of the infectious disease parameters, and calculate the infectious disease parameter infection rate (β) by the following formula:

[0048]

[0049] Among them, β represents the infection rate, R0 represents the basic reproduction number, γ represents the recovery rate, and λ represents the spectral radius of the contact network, that is, the real part of the maximum eigenvalue of the contact network adjacency matrix.

[0050] An infectious disease simulation based on the SIR propagation dynamics model is performed at each time step and the infection state on the network at each moment is recorded until one of the following conditions is met: the number of propagation time steps reaches t or there are no infected individuals in the network.

[0051] In one embodiment of the present application, the contact network includes a random graph network, a scale-free network, a citation network, a dolphin social network, a karate club social network, and a Les Miserables character co-occurrence network.

[0052] In one embodiment of the present application, the contact network includes an ER network, a BA network, a Cora network, a Dolphin network, a Karate network and a Lesmis network, as shown in Table 1 below.

[0053]

[0054] S203: Obtain node topology features on the contact network based on a centrality method, and add them to the individual infection sample.

[0055] In one embodiment of the present application, the node topology features include betweenness centrality, closeness centrality, and degree centrality. The calculation formula of the node topology features is:

[0056]

[0057] Z i =[B i ∥Ci ∥D i ]

[0058] Among them, B i represents the end centrality, σ st represents the number of shortest paths from node s to node t, σ st (i) represents the number of shortest paths from s to t passing through node i, C u represents close centrality, d(i,j) represents the shortest path distance between node i and node j, and D i represents degree centrality, k i represents the degree of node i, that is, the number of nodes connected to the node, A ij is an element in the contact network adjacency matrix, indicating whether node i and node j are connected. If so, then A ij =1, otherwise 0, Z i Represents the topological characteristics of the node, and ∥ represents the splicing operation.

[0059] In one embodiment of the present application, the betweenness centrality B i Measures the intermediary role of nodes in the propagation path, explaining the criticality of nodes in information flow and infection spread; close centrality C i The average distance between the reaction node and other nodes indicates the efficiency of the node in the propagation process; degree centrality D i Indicates the number of direct connections of a node, reflecting the influence of the node in the infection network. These three centrality indicators provide a quantitative basis for understanding the propagation mechanism and node importance in the infection state graph. i As the topological feature of the node, the topological feature matrix of the node is obtained

[0060] S205: Perform data preprocessing on the individual infection samples and divide them into training samples, verification samples and test samples.

[0061] In the embodiment of the present application, the individual infection sample includes the infection state feature matrix, topology feature matrix and edge feature matrix of the nodes in the contact network, and the infection state includes susceptible state S, infected state I and recovered state R. After data preprocessing, the individual infection sample is divided into training sample, verification sample and test sample in a ratio of 8:1:1.

[0062] In one embodiment of the present application, the data preprocessing includes:

[0063] Unreasonable individual infection samples are eliminated, and the node information and edge information of the contact network are one-hot encoded.

[0064] In one embodiment of the present application, records of excessive infection and less infection status in individual infection samples are removed to improve the quality of data and the effectiveness of analysis. At the same time, the node information of the contact network in the individual infection sample is encoded, and the three different infection states of the nodes on the network (susceptible state, infected state, and recovered state) are uniquely encoded to obtain the node infection state feature matrix Where N is the number of nodes on the network; the edge information of the contact network in the individual infection sample is encoded, and the difference in infection status between the source node and the target node of each edge is processed by one-hot encoding. Considering that each node has multiple possible states, the edge information also corresponds to multiple potential state combinations, and the edge state feature is obtained. Where E is the number of edges on the network.

[0065] S207: Put the training samples and verification samples into a neural network model based on a message passing mechanism for training, wherein the neural network model based on a message passing mechanism includes a feature encoding module, an MPNN network, a propagation controller module, and a priori knowledge module.

[0066] In the present application embodiment, Figure 3 As shown in FIG. 1 , it is a schematic diagram of the structure of a neural network model based on a message passing mechanism, including a feature encoding module, an MPNN network, a propagation controller module, and a priori knowledge module. Figure 4 As shown in FIG. 1 , it is a schematic diagram of the structure of the feature encoding module. The feature encoding module is used to map the input feature information to the hidden layer. The mapping formula is as follows:

[0067] H N =ReLU(W N ·x N )

[0068] H E =ReLU(W E ·x E )

[0069] H Z =ReLU(W Z ·Z N )

[0070] Among them, H N The hidden layer representation of the node infection feature, W N Represents the weight matrix of the node infection feature map, x N represents the infection status characteristics of the node, H E The hidden layer representation of the edge state features, W E Represents the weight matrix of the edge state feature map, x E represents the edge state characteristics, H ZThe hidden layer representation representing the node topology features, W Z Represents the weight matrix of the node topology feature map, Z N Represents the node topology characteristics.

[0071] The MPNN network is a graph neural network (GNN) that transmits information between nodes in the graph through a message passing mechanism to capture the relationship between nodes and their neighbors. The MPNN network includes two main modules, an attention module and a message passing module. The formula of the attention module is as follows:

[0072]

[0073] Z i =H z [i,:]

[0074]

[0075] Among them, l represents the number of message aggregation layers of MPNN, and its maximum value is the preset hyperparameter L. and Represents the node feature representation at the lth layer of the MPNN network, and its initial value H is the hidden layer representation of node i in the infection feature N The corresponding features in Represents the edge feature representation at the lth layer of the MPNN network, and its initial value H is the hidden layer representation of edge ij in the edge state feature E The corresponding feature in index ij Represents the index value of edge ij, z i The hidden layer representation H of node i in the node topology feature Z The corresponding features in represents the attention coefficient of edge ij in the lth layer of the MPNN network, α represents the attention weight vector, Represents the attention calculation weight matrix of the lth layer of the MPNN network.

[0076] The message passing module contains aggregation and update operations, and its formula is as follows:

[0077]

[0078] in, Represents the information represented by edges and nodes aggregated in the lth layer of the MPNN network, W l represents the message passing weight matrix of the lth layer of the MPNN network, The weight matrix representing the node's own information update, represents the first-order neighbors of node i on the contact network, It indicates the prediction score of node i as the source, and its value is between 0 and 1. FC(·) is a fully connected network.

[0079] The propagation controller module is used to adaptively control the number of message aggregation layers l of different nodes. The formula is as follows:

[0080]

[0081] in, represents the binary aggregate control signal of the control node i at the lth layer of the MPNN network, indicates that node i participates in message passing at layer l, Indicates that node i does not participate in message passing at layer l, F controller represents a two-layer multilayer perceptron (MLP) with an output value between 0 and 1, θ represents the weight parameter in the controller module, and u represents the hyperparameter that controls the signal.

[0082] The prior knowledge module introduces knowledge in the field of infectious diseases in the network forward propagation, thereby improving the generalization ability and reasoning efficiency of the deep learning network. Considering that nodes in a susceptible state cannot be the source nodes of transmission, the prior knowledge is introduced to adjust the output results. The formula is as follows:

[0083]

[0084] Among them, ε represents a positive number that is infinitely close to 0 but not equal to 0, which is used to correct the prediction results based on prior knowledge. i Represents the infection status of node i.

[0085] In one embodiment of the present application, placing the training samples and the verification samples into a neural network model based on a message passing mechanism for training includes:

[0086] The cross entropy loss function is used as the optimization objective, and the formula of the optimization objective function is:

[0087]

[0088] in, represents the loss to be optimized, y i represents the true label of node i. If node i is the source node, then y S =1, otherwise y i =0, represents the prediction score of node i as the source, Represents the prediction score of node j as the source.

[0089] In one embodiment of the present application, the neural network model is trained using training samples, and the cross entropy loss function is used as the optimization target to minimize the error between the model prediction and the true label. The training is intended to ensure that the model can effectively distinguish between source nodes and non-source nodes, and by minimizing the above loss function, the neural network's ability to identify and infer source nodes is enhanced.

[0090] S209: Input the test sample into a trained neural network model based on a message passing mechanism to obtain a prediction result of a propagation source of the test sample.

[0091] In the embodiment of the present application, finally, the test sample is input into the trained neural network model based on the message passing mechanism, and the model outputs the prediction result of the propagation source of the test sample.

[0092] In one embodiment of the present application, Top-1 accuracy, Top-5 accuracy and Hop-1 accuracy are used as evaluation indicators to evaluate the inference ability of the neural network model based on the message passing mechanism.

[0093] In one embodiment of the present application, three types of indicators are selected to evaluate the model inference ability: Top-1 accuracy, Top-5 accuracy and Hop-1 accuracy. Top-1 accuracy is used to evaluate whether the model can predict the score in each inference. The highest node is correctly identified as the true source node (i.e., y i =1); Top-5 accuracy measures whether the model can predict the score in each inference. The top five nodes include the real source node. Even if the preferred node inferred by the model is incorrect, as long as the source node is in the top five prediction scores, the Top-5 prediction is still considered accurate. The Hop-1 accuracy is used to evaluate whether the model can accurately infer the source node through the neighborhood information within one hop range (including the node itself) in each inference.

[0094] In the above-mentioned complex network disease transmission source tracing method based on graph neural network, first, the transmission dynamics model is used to obtain individual infection samples on the contact network; then, the node topology features on the contact network are obtained based on the centrality method and added to the individual infection samples; then, the individual infection samples are preprocessed and divided into training samples, verification samples and test samples; then, the training samples and verification samples are put into the neural network model based on the message passing mechanism for training, wherein the neural network model based on the message passing mechanism includes a feature encoding module, an MPNN network, a transmission controller module and a priori knowledge module; finally, the test sample is input into the trained neural network model based on the message passing mechanism to obtain the prediction result of the transmission source of the test sample. That is to say, by simulating the transmission process using the transmission dynamics model, recording the infection snapshot of the individual on the contact network, and analyzing the network node characteristics through the topological features based on the centrality, considering the characteristics of the nodes and edges on the contact network, and using the neural network model based on the message passing mechanism for deep learning, the transmission source on the complex network can be efficiently and accurately inferred, thereby providing technical support for disease prevention.

[0095] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0096] Based on the same inventive concept, the embodiment of the present application also provides a device for tracing the spread of a disease in a complex network based on a graph neural network for implementing the above-mentioned method for tracing the spread of a disease in a complex network based on a graph neural network. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above-mentioned method, so the specific limitations in one or more embodiments of the device for tracing the spread of a disease in a complex network based on a graph neural network provided below can be referred to the limitations of the method for tracing the spread of a disease in a complex network based on a graph neural network above, and will not be repeated here.

[0097] In one embodiment, Figure 5As shown, a complex network disease transmission tracing device 500 based on graph neural network is provided, including: a data acquisition module 501, a node topology feature acquisition module 503, a data preprocessing module 505, a model training module 507 and a transmission tracing module 509, wherein:

[0098] The data acquisition module 501 is used to acquire individual infection samples on the contact network using a propagation dynamics model.

[0099] The node topology feature acquisition module 503 is used to acquire the node topology features on the contact network based on the centrality method and add them to the individual infection sample.

[0100] The data preprocessing module 505 is used to perform data preprocessing on the individual infection samples and divide them into training samples, verification samples and test samples.

[0101] The model training module 507 is used to put the training samples and verification samples into a neural network model based on a message passing mechanism for training, wherein the neural network model based on a message passing mechanism includes a feature encoding module, an MPNN network, a propagation controller module and a priori knowledge module.

[0102] The propagation tracing module 509 is used to input the test sample into a trained neural network model based on the message passing mechanism to obtain the prediction result of the propagation source of the test sample.

[0103] In one embodiment of the present application, the propagation dynamics model adopts a propagation dynamics model based on individual SIR.

[0104] In one embodiment of the present application, the contact network includes a random graph network, a scale-free network, a citation network, a dolphin social network, a karate club social network, and a Les Miserables character co-occurrence network.

[0105] In one embodiment of the present application, the node topology features include betweenness centrality, closeness centrality, and degree centrality. The calculation formula of the node topology features is:

[0106]

[0107] Z S =[B i ∥C i ∥D i ]

[0108] Among them, B i represents the end centrality, σ st represents the number of shortest paths from node s to node t, σ st(i) represents the number of shortest paths from s to t passing through node i, C i represents close centrality, d(i,j) represents the shortest path distance between node i and node j, and D i represents degree centrality, k i represents the degree of node i, that is, the number of nodes connected to the node, A ij is an element in the contact network adjacency matrix, indicating whether node i and node j are connected. If so, then A ij =1, otherwise 0, Z i Represents the topological characteristics of the node, and ∥ represents the splicing operation.

[0109] In one embodiment of the present application, the data preprocessing module is further used to:

[0110] Unreasonable individual infection samples are eliminated, and the node information and edge information of the contact network are one-hot encoded.

[0111] In one embodiment of the present application, the model training module is also used to:

[0112] The cross entropy loss function is used as the optimization objective, and the formula of the optimization objective function is:

[0113]

[0114] in, represents the loss to be optimized, y i represents the true label of node i. If node i is the source node, then y i =1, otherwise y i =0, represents the prediction score of node i as the source, Represents the prediction score of node j as the source.

[0115] In one embodiment of the present application, Top-1 accuracy, Top-5 accuracy and Hop-1 accuracy are used as evaluation indicators to evaluate the inference ability of the neural network model based on the message passing mechanism.

[0116] Each module in the above-mentioned complex network disease transmission tracing device based on graph neural network can be implemented in whole or in part by software, hardware and their combination. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the corresponding operations of the above modules.

[0117] In one embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as follows: Figure 6As shown. The computer device includes a processor, a memory, a communication interface, a display screen and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, a mobile cellular network, NFC (near field communication) or other technologies. When the computer program is executed by the processor, a complex network disease transmission tracing method based on a graph neural network is implemented. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covered on the display screen, or a button, trackball or touchpad set on the computer device housing, or an external keyboard, touchpad or mouse.

[0118] Those skilled in the art will understand that Figure 6 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0119] In one embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program.

[0120] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0121] In one embodiment, a computer program product is provided, including a computer program, which implements the steps in the above method embodiments when executed by a processor.

[0122] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties.

[0123] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited to this.

[0124] The technical features of the above embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0125] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A complex network disease transmission tracing method based on graph neural network, characterized in that: The method comprises: A transmission dynamics model is used to obtain individual infection samples on the contact network; Obtaining node topological features on the contact network based on a centrality method and adding them to the individual infection sample; Performing data preprocessing on the individual infection samples and dividing them into training samples, verification samples and test samples; Putting the training samples and the verification samples into a neural network model based on a message passing mechanism for training, wherein the neural network model based on a message passing mechanism includes a feature encoding module, an MPNN network, a propagation controller module, and a priori knowledge module; The test sample is input into a trained neural network model based on a message passing mechanism to obtain a prediction result of the propagation source of the test sample.

2. According to claim 1, a complex network disease propagation tracing method based on graph neural network is characterized in that: The propagation dynamics model adopts a propagation dynamics model based on individual SIR.

3. According to claim 1, a complex network disease propagation tracing method based on graph neural network is characterized in that: The contact networks include random graph networks, scale-free networks, citation networks, dolphin social networks, karate club social networks, and Les Miserables character co-occurrence networks.

4. According to claim 1, a complex network disease transmission tracing method based on graph neural network is characterized in that: The node topology features include betweenness centrality, closeness centrality, and degree centrality. The calculation formula of the node topology features is: Z i =[B i ∥C i ∥D i ] Among them, B i represents the end centrality, σ st represents the number of shortest paths from node s to node t, σ st (i) represents the number of shortest paths from s to t passing through node i, C u represents close centrality, d(i,j) represents the shortest path distance between node i and node j, and D i represents degree centrality, k i represents the degree of node i, that is, the number of nodes connected to the node, A ij is an element in the contact network adjacency matrix, indicating whether node i and node j are connected. If so, then A ij =1, otherwise 0, Z i Represents the topological characteristics of the node, and ∥ represents the splicing operation.

5. According to claim 1, a complex network disease transmission tracing method based on graph neural network is characterized in that: The data preprocessing includes: Unreasonable individual infection samples are eliminated, and the node information and edge information of the contact network are one-hot encoded.

6. According to claim 1, a complex network disease transmission tracing method based on graph neural network is characterized in that: The step of placing the training samples and the verification samples into a neural network model based on a message passing mechanism for training comprises: The cross entropy loss function is used as the optimization objective, and the formula of the optimization objective function is: Among them, L represents the loss to be optimized, y i represents the true label of node i. If node i is the source node, then y i =1, otherwise y i =0, represents the prediction score of node i as the source, Represents the prediction score of node j as the source.

7. According to claim 1, a complex network disease transmission tracing method based on graph neural network is characterized in that: The Top-1 accuracy, Top-5 accuracy and Hop-1 accuracy are used as evaluation indicators to evaluate the inference ability of the neural network model based on the message passing mechanism.

8. A complex network disease transmission tracing device based on graph neural network, characterized in that: The device comprises: A data acquisition module, used to acquire individual infection samples on the contact network using a propagation dynamics model; A node topology feature acquisition module, used to acquire node topology features on the contact network based on a centrality method, and add the features to the individual infection sample; A data preprocessing module, used to perform data preprocessing on the individual infection samples and divide them into training samples, verification samples and test samples; A model training module, used for putting the training samples and the verification samples into a neural network model based on a message passing mechanism for training, wherein the neural network model based on a message passing mechanism includes a feature encoding module, an MPNN network, a propagation controller module and a priori knowledge module; The propagation tracing module is used to input the test sample into a trained neural network model based on the message passing mechanism to obtain the prediction result of the propagation source of the test sample.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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