OODA heterogeneous network link prediction method, device, equipment and medium

Abstract

The present application discloses a method, apparatus, device and medium for link prediction in an OODA heterogeneous network, and relates to the field of OODA heterogeneous network link prediction technology. The method includes: constructing a teacher-student model based on the CROTS algorithm in the OODA heterogeneous network; mapping the nodes in the OODA heterogeneous network to a low-dimensional vector space, and predicting the OODA heterogeneous network using a probability propagation algorithm through the teacher model to obtain a prediction result, and training the student model based on the prediction result to perform link prediction. The present invention significantly improves the link prediction speed by introducing the idea of ​​teacher-student learning into the OODA heterogeneous network link prediction. The teacher model effectively avoids the problem of long prediction time of high-accuracy link prediction algorithms while maintaining a high accuracy; after completing the teacher-student learning process, the student model performs OODA heterogeneous network link prediction based on topological features, thereby being able to have a faster prediction speed while retaining a high accuracy.

Description

Technical Field

[0001] The present application relates to the technical field of OODA heterogeneous network link prediction, and more specifically, to a method, apparatus, device and medium for OODA heterogeneous network link prediction. Background Art

[0002] Due to the rapid changes in information, the network topology of OODA heterogeneous networks is often missing. Link prediction is a complementary method to network topology, so there is an urgent need for a link prediction method suitable for OODA heterogeneous networks. Current link prediction methods are mainly divided into three categories:

[0003] 1. Traditional link prediction methods: These methods primarily rely on link similarity. For example, the local similarity metric, the Common Neighbor (CN) index, calculates the likelihood of a link based on the number of common neighbors between two nodes; the global similarity metric, the Katz index, considers all paths between network nodes. This method has certain advantages in speed, but is often less accurate.

[0004] 2. Methods based on network representation learning: This method mainly uses graph embedding or node embedding methods to represent the network for further link prediction. For example, the metapath2vec model combines the Skip-Gram model with a meta-path-based random walk to construct a heterogeneous neighborhood of nodes, and then uses a heterogeneous jump model as a network representation method for node embedding. LINE uses a breadth-first search (BFS) random walk to sample nodes in the graph to construct a node embedding vector, which further calculates the node similarity for link prediction. However, this method is mainly used for homogeneous networks, has low accuracy in heterogeneous networks, and takes a long time.

[0005] 3. Machine Learning-Based Approaches: Traditional machine learning algorithms primarily treat link prediction as a supervised learning task, using classification algorithms to categorize existing or potential links in a network, as well as nonexistent links, to achieve link prediction. However, with breakthroughs in machine learning technology, many researchers have begun using graph embedding techniques combined with classifiers to address link prediction in complex networks. For example, NGCF designed a neural network approach to recursively implement graph embedding, thereby constructing a recommendation system framework based on graph neural networks. This approach has certain advantages in terms of accuracy, but due to the requirement for large amounts of data for pre-training, it can suffer from underfitting in small networks.

[0006] Therefore, the existing link prediction algorithms are not tightly integrated with OODA heterogeneous networks. Carrying out link prediction research for OODA heterogeneous networks and accurately, reasonably and quickly predicting possible links in the network are the objective needs for a deep understanding of the internal mechanism of system network evolution and mining potential links in the network. It is a practical need to promote the construction and development of system link prediction capabilities in the information age and an urgent need to break through the technical bottleneck of OODA heterogeneous network link prediction research. Summary of the Invention

[0007] In response to at least one defect or improvement need in the prior art, the present invention provides an OODA heterogeneous network link prediction method, device, equipment and medium to address the defects of the prior art. By analyzing and utilizing the characteristic information of nodes and links in the network, a specific quantitative evaluation is performed on the similarity or connection possibility between possible pairs of nodes that are not yet directly connected in the network, and the links that may appear between the nodes are predicted, thereby achieving accurate, reasonable and fast link prediction in the OODA heterogeneous network.

[0008] To achieve the above-mentioned object, according to a first aspect of the present invention, a method for link prediction in an OODA heterogeneous network is provided, characterized in that it includes:

[0009] Based on the OODA heterogeneous network, the CROTS algorithm is used to build a teacher-student model;

[0010] The nodes in the OODA heterogeneous network are mapped to a low-dimensional vector space, and the probability propagation algorithm is used to predict the OODA heterogeneous network through the teacher model to obtain the prediction results;

[0011] A student model is trained based on the prediction results to perform link prediction.

[0012] As described in the OODA heterogeneous network link prediction method, the method predicts the OODA heterogeneous network using a probability propagation algorithm through a teacher model to obtain a prediction result, including:

[0013] The Meta-Probs algorithm is used to construct the teacher model and obtain the node topology structure characteristics;

[0014] Based on the node topology structure characteristics, the degree, H-index and density (DHC) theorem are used to represent the network and obtain the node embedding vector;

[0015] Based on the DHC theorem, the convergence process of the H index sequence is used as the node embedding vector;

[0016] The cosine distance between node embedding vectors is used to quantify the similarity between nodes and used as edge weight to control the resource diffusion of the ProbS framework to obtain the link weight matrix;

[0017] Link prediction is performed according to the link weight matrix to obtain a prediction result.

[0018] In the OODA heterogeneous network link prediction method, the step of training a student model based on the prediction result to perform link prediction includes:

[0019] Constructing the adjacency matrix of the OODA heterogeneous network;

[0020] constructing a similarity function based on the adjacency matrix;

[0021] A ranking result is determined based on the similarity function to obtain a predicted link.

[0022] In the OODA heterogeneous network link prediction method, constructing a similarity function based on the adjacency matrix includes:

[0023] Initializing optimal parameter values ​​and corresponding maximum predicted link numbers based on the adjacency matrix;

[0024] Analyze the teacher model prediction results based on the optimal parameter values ​​and the maximum number of predicted links to obtain the updated optimal parameter values;

[0025] A similarity function is determined based on the updated optimal parameter value.

[0026] As described in the OODA heterogeneous network link prediction method, the similarity function is specifically:

[0027]

[0028] Among them, S ij represents the similarity between node i and node j, CN and CN_L3 represent the second-order path and third-order path indicators of node i and node j respectively; the α value is obtained through model learning; T(i) and T(j) represent the types of node i and node j respectively.

[0029] As described in the OODA heterogeneous network link prediction method, the adjacency matrix includes a second-order path matrix and a third-order path matrix.

[0030] As described in the OODA heterogeneous network link prediction method, the CROTS algorithm is used in the OODA heterogeneous network model to construct a teacher-student model, including:

[0031] Conduct network modeling on OODA heterogeneous network and build OODA heterogeneous network model;

[0032] A teacher-student model is constructed based on the OODA heterogeneous network model.

[0033] According to the second aspect of the present invention, an OODA heterogeneous network link prediction device is also provided, including: a construction unit, used to construct a teacher-student model based on the OODA heterogeneous network model using the CROTS algorithm; a first processing unit, used to map the nodes in the OODA heterogeneous network to a low-dimensional vector space, and predict the OODA heterogeneous network using a probability propagation algorithm through the teacher model to obtain a prediction result; a second processing unit, used to train the student model based on the prediction result to perform link prediction.

[0034] According to a third aspect of the present invention, a computer-readable storage medium is further provided, in which a computer program is stored, wherein the computer program is configured to execute the above-mentioned OODA heterogeneous network link prediction method when running.

[0035] According to a fourth aspect of the present invention, an electronic device is also provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the OODA heterogeneous network link prediction method through the computer program.

[0036] In general, the above technical solutions conceived by the present invention can achieve the following beneficial effects compared with the prior art:

[0037] (1) The present invention provides an OODA heterogeneous network link prediction method. This method successfully introduces the idea of ​​teacher-student learning into OODA heterogeneous network link prediction by targeting the problem of insufficient computing power of devices and high real-time requirements in reality. By using the CROTS algorithm based on the OODA heterogeneous network model, a teacher-student model is constructed; based on the mapping of nodes in the OODA heterogeneous network to a low-dimensional vector space, the OODA heterogeneous network is predicted by the teacher model using a probability propagation algorithm to obtain a prediction result, and then the student model is trained based on the prediction result to perform link prediction. The teacher model uses prior knowledge for learning, effectively avoiding the problem of long prediction time of high-accuracy link prediction algorithms, while maintaining high accuracy; after completing the teacher-student learning process, the student model performs OODA heterogeneous network link prediction based on topological features, thereby being able to have a faster prediction speed while retaining high accuracy, meeting the real-time requirements of the OODA heterogeneous network, and providing a guarantee for individuals or organizations to grasp the situation and make correct commands in a relatively short time.

[0038] (2) This application uses a highly accurate link prediction algorithm as a teacher model and a faster link prediction algorithm as a student model, and complements the advantages of the two through teacher-student learning. In order to promote the teacher-student learning process and ensure the robustness of the student model, the similarity calculation index is improved by combining the second-order path and the third-order path. The current mainstream network structure similarity calculation index is the CN index, but the prediction accuracy of this index will drop sharply in OODA heterogeneous networks with high sparsity (that is, there are few or even no second-order neighbors). Therefore, this application adds the third-order path to the similarity calculation index, which to a certain extent alleviates the situation where there are too few second-order paths and accurate link prediction cannot be performed, thereby improving the robustness of prediction when the system environment changes. At the same time, according to the characteristics of OODA heterogeneous networks, such as decision makers are often in a central position, the similarity of some node links is appropriately modified so that the similarity calculation index can be more in line with the characteristics of the OODA heterogeneous network model. Since this indicator cannot interact with the teacher model, this application adopts the method of optimal parameter value learning, that is, using the learning process to obtain the optimal parameter value to balance the second-order path and third-order path indicators, so that the student model can obtain the parameter value that is most suitable for the current OODA heterogeneous network after learning, and achieve the purpose of accurate and rapid prediction. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0040] Figure 1 A flowchart of an optional OODA heterogeneous network link prediction method provided in an embodiment of the present application;

[0041] Figure 2 A schematic diagram of the structure of an optional OODA heterogeneous network link prediction device provided in an embodiment of the present application;

[0042] Figure 3 A schematic diagram of the network topology of an optional OODA heterogeneous network provided in an embodiment of the present application.

[0043] Figure 4 A schematic diagram of the structure of an optional link prediction algorithm framework provided in an embodiment of the present application.

[0044] Figure 5 A schematic structural diagram of an optional teacher model framework provided in an embodiment of the present application.

[0045] Figure 6A schematic structural diagram of an optional electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0046] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely for the purpose of explaining the present invention and are not intended to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.

[0047] The terms "first," "second," "third," and the like in the specification and claims of this application and the accompanying drawings are used to distinguish between different objects, not to describe a particular order. Furthermore, the terms "including," "having," and any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or apparatus comprising a series of steps or elements is not limited to the listed steps or elements, but may optionally include steps or elements not listed, or may optionally include other steps or elements inherent to the process, method, product, or apparatus.

[0048] The following is a brief introduction to the technologies involved in the present invention.

[0049] The Observe-Orient-Decide-Act (OODA) loop, proposed by Colonel John Boyd of the US Air Force, is widely used in complex systems in the military, business, and other fields. It covers the entire process from observing the environment (Observe), determining the goal (Orient), making a decision (Decide), and taking an action (Act). It can provide effective decision-making and rationally guide the actions of individuals or organizations within the system. These systems are composed of entities that perform different functions. When abstracting these systems, we can view these entities as nodes in a network and the links between them as links on the network. In this way, the system can be transformed into a heterogeneous network, that is, a network composed of different types of nodes and links.

[0050] Since these systems follow the decision-making and execution patterns in the OODA loop and can be abstracted as heterogeneous networks, these heterogeneous networks with OODA loop characteristics are defined as OODA heterogeneous networks. Their network structure not only reflects the complexity of the system, but also shows the dynamic interaction and decision-making process between the various parts of the system.

[0051] In reality, heterogeneous OODA networks exist across a wide range of domains. For example, in police systems, case handling follows the logic of the OODA loop: first, observe the crime scene and gather relevant information; then, based on this information, determine the nature of the case and key clues. Decisions are then made and tasks assigned; finally, actions are taken to resolve the case. Similarly, in enterprise systems, opportunity management follows a process of observing market dynamics: first, observe the market environment; then determine the direction of market demand; then formulate business strategies; and finally, implement corresponding action plans. In-depth research on heterogeneous OODA network structures is not only crucial for solving police cases and gaining insight into enterprise markets, but also holds profound implications for many fields, possessing both academic value and practical significance.

[0052] In a heterogeneous OODA network, each node carries a specific function, and the links between nodes are key to understanding the network's structure and dynamic characteristics. However, in real-world applications, due to the dynamic nature of information, the node structure in the network is often missing, such as hidden criminal clues or encrypted trade secrets. The concealment or lack of this potential information not only hinders our comprehensive understanding of the network structure but also hinders our insight into the underlying relationships between nodes. With the emergence of numerous real-life heterogeneous OODA network scenarios, it is crucial to obtain the complete topology of heterogeneous OODA networks. Link prediction can help police quickly understand case situations and help companies seize marketing opportunities.

[0053] Link prediction aims to use known network structure information to determine and predict the likelihood of potential links between nodes, thereby improving network structure and revealing potential relationships. Currently, mainstream link prediction methods have shown some effectiveness in complex network analysis. For example, graph embedding-based methods such as Deepwalk, Node2vec, LINE, and SDNE, as well as neural network-based methods such as GCN, GAT, and NGCF, have demonstrated promising results. However, these algorithms are primarily designed for homogeneous networks, while the complexity of heterogeneous network structures and relationships significantly limits their application.

[0054] Although some studies have proposed link prediction algorithms for heterogeneous networks, such as Meta AIProbs and metapath2vec, they still have certain limitations when applied to OODA heterogeneous networks. These include slow prediction speed, which makes it difficult to meet the time-sensitive requirements of OODA heterogeneous networks (such as in commercial competition, where business opportunities are fleeting); and they do not have the ability to process prior knowledge and are not suitable for OODA heterogeneous networks with prior knowledge characteristics (such as in police networks, where police have a fingerprint database of all citizens).

[0055] Therefore, the existing link prediction algorithms are not tightly integrated with OODA heterogeneous networks, and further research is still needed on the link prediction methods of OODA heterogeneous networks. This patent intends to introduce the teacher-student model (TSFNLP) based on the idea of ​​the CROTS algorithm, taking into account the high precision and efficiency of link prediction, and provide a link prediction calculation model that conforms to the structural characteristics of the OODA heterogeneous network. First, the OODA heterogeneous network is networked and the OODA heterogeneous network model is constructed. Then, based on the CROTS algorithm, a teacher-student model (TSFNLP) is proposed to map the nodes in the OODA heterogeneous network to a low-dimensional vector space. The teacher model uses a probability propagation algorithm to predict the OODA heterogeneous network and trains the student model through the prediction results. Finally, the student model is used to perform link prediction to solve the link prediction problem in the OODA heterogeneous network.

[0056] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0057] According to one aspect of an embodiment of the present application, a method for predicting links in an OODA heterogeneous network is provided. Figure 1 This is a flow chart of an optional OODA heterogeneous network link prediction method provided in an embodiment of the present application, such as Figure 1 As shown, the process of the method may include the following steps (STEP, abbreviated as S):

[0058] S102, based on the OODA heterogeneous network, the CROTS algorithm is used to build a teacher-student model;

[0059] S104, mapping the nodes in the OODA heterogeneous network to a low-dimensional vector space, and using a probability propagation algorithm through a teacher model to predict the OODA heterogeneous network to obtain a prediction result;

[0060] S106: Training a student model based on the prediction result to perform link prediction.

[0061] In an optional embodiment, the CROTS algorithm is used in the OODA heterogeneous network model to construct a teacher-student model, including:

[0062] Conduct network modeling on OODA heterogeneous network and build OODA heterogeneous network model;

[0063] A teacher-student model is constructed based on the OODA heterogeneous network model.

[0064] In the network modeling of OODA heterogeneous networks, based on the OODA loop, OODA heterogeneous networks are a complex heterogeneous network formed by abstracting network entities into nodes and abstracting the information, material, and energy exchanges between entities into links. Entities are abstracted into three types of nodes: OO, D, and A, as described below:

[0065] 1. OO Node (Sensor): As a sensor node, the primary responsibility of an OO node is to receive observable phenomena or information from network entities. These phenomena can be signals, data, or events in the physical world, or state changes in other network nodes. The OO node integrates the received information and sends it to the D node for subsequent analysis and decision-making.

[0066] 2. D-nodes (Decision-makers): As information judgment and decision-making nodes, D-nodes play a core role in heterogeneous networks. They receive information from O-nodes and conduct in-depth analysis and processing to understand the network's status and changes. Based on this information, D-nodes determine the actions of other nodes, including how to respond to external events, adjust the network structure, and optimize network performance.

[0067] 3. A Node (Influencer): As a participant, A Node receives instructions or commands from D Node and interacts with other nodes based on these instructions. A Node's actions can have a direct impact on the network, including changing network state, transmitting information, or performing specific tasks. They act as bridges and links in the network, ensuring that the decisions made by D Node are effectively executed.

[0068] In OODA heterogeneous networks, information flow between entities is the core mechanism of network operation. This information flow is typically abstracted as directed edges between nodes. Traditional OODA heterogeneous network models assume that OO nodes serve only as information providers and do not directly participate in operations, thus excluding direct links from OO nodes to A nodes. However, with the development of wireless sensor network technology, this assumption is no longer fully applicable. Wireless sensor networks enable OO nodes (sensors) to directly control A nodes (influencers) via wireless connections, thereby improving the responsiveness and flexibility of the entire network system. This direct control link is particularly important in scenarios with high real-time requirements, such as automatic control and intelligent environmental monitoring. Furthermore, given the position of A nodes in the network information flow, they are typically located at the "nerve endings," responsible for executing specific operations or influencing other entities. Therefore, this paper assumes that there are no direct links between A nodes in OODA heterogeneous networks.

[0069] Based on the above analysis, this paper abstracts and models the links in the OODA heterogeneous network. The links are divided into 8 categories, as shown in Table 1:

[0070]

[0071]

[0072] Model construction based on OODA heterogeneous network, Figure 2 This is an example diagram of the OODA heterogeneous network topology. Figure 2 The left figure shows an example network topology of an OODA heterogeneous network, and the right figure shows three representative nodes and the links between them: solid lines represent currently existing links, and dashed lines represent possible links based on the OODA heterogeneous network link prediction.

[0073] In the construction of the link prediction algorithm framework based on the CROTS algorithm, the CROTS (Cross-domain Teacher-Student Learning for Passive Domain Adaptive Semantic Segmentation) algorithm has significant application value in the field of image learning. The core idea is to introduce a teacher model to generate pseudo labels and provide learning goals for the student model. At the same time, the prediction results of the student model also provide feedback for the optimization of the teacher model. The performance of the teacher model is evaluated by comparing the difference between the prediction results of the student model and the actual labels, and further optimized accordingly. This interaction and feedback mechanism between teachers and students enables the CROTS algorithm to gradually improve its performance in the continuous learning process. The algorithm takes into account the pseudo labels of class imbalance and the limitations of the minority class that the target model easily forgets. It adopts a rare class patch mining strategy to alleviate the imbalance phenomenon. The CROTS algorithm process is as follows Figure 3 shown.

[0074] The link prediction algorithm framework proposed based on the CROTS algorithm is as follows: Figure 4 As shown in the figure, the nodes in the OODA heterogeneous network are first mapped to a low-dimensional vector space using a deterministic and interpretable network representation method. The teacher model then uses a probability propagation algorithm to make predictions about the OODA heterogeneous network, and the prediction results are used to train the student model. Finally, the student model uses the topological characteristics of the OODA heterogeneous network nodes to perform link prediction to achieve the desired effect.

[0075] In an optional embodiment, the OODA heterogeneous network link prediction results are generated based on the teacher model, and the Meta-Probs algorithm can be used as the teacher model algorithm. In the Meta Probs algorithm, the OODA heterogeneous network uses a network representation method based on the degree, H-index and density (DHC) theorem to further obtain the node embedding vector. The centrality of a node refers to the importance of the node in the network structure or function generated by the connection between the node and other nodes. The node degree, H-index and core number are both key indicators in the DHC theorem and commonly used indicators for judging the centrality of a node.

[0076] Node degree refers to the number of neighbors a node has. An example of the definition of node degree is:

[0077]

[0078] Among them, Γ (i) Indicates v i The set of neighbor nodes of a. ij is the adjacency matrix A n×n Elements in .

[0079] The H-index is the maximum value of at least h neighbors with degree not less than h in the neighbor node set. Define node v i H-index:

[0080] h i =H(d1,d2,d3,…,d j ) (3-2)

[0081] Define the nth-order H-index:

[0082]

[0083] The coreness of a node describes its central position in the network. The coreness is calculated using k-shell decomposition.

[0084] According to the DHC theorem, during network decomposition, the node degree, H-index, and core number correspond to the initial, intermediate, and stable states, respectively. Any node in a complex network will converge to the core number within a finite number of steps. The convergence step of this sequence reflects the influence of the node: the more important the node, the slower its H-index decays and the larger the convergence step.

[0085] Using node v in the Meta-Probs algorithm i The convergence vector of is used as the node embedding vector, and the vector dimension is the finite step length s of its convergence. The node embedding vector thus formed can well judge the centrality of the node.

[0086] Construct node similarity matrix S using node embedding vector n×n , element s ij ∈S n×n Represents a node pair (v i ,v j ) similarity measure. Define the node pair (v i ,v j ) similarity S ij The -cosine distance of its corresponding node embedding vector:

[0087]

[0088] Where x is v i The node embedding vector of y is v j The node embedding vector of .

[0089] Normalize the matrix elements:

[0090]

[0091] Among them, min is S n×n The smallest element in S, max is n×n The largest element in .

[0092] In order to consider the impact of information flow in OODA heterogeneous networks and the difference in the influence of each node,

[0093] The algorithm uses the cosine distance of node embedding vectors as edge weights under the Probs framework.

[0094] Definition ω ij is the edge weight between node i and node j:

[0095]

[0096] The resource diffusion process of the ProbS framework is controlled by edge weights, including forward propagation and backward diffusion.

[0097] According to the link construction of OODA heterogeneous network, in the forward propagation, we use the edge weight as the carrying resource according to and The two meta-paths propagate without backpropagation. After all decision nodes and sensor nodes have completed resource propagation, the reverse diffusion process between sensor nodes and response nodes begins. During the reverse diffusion process, sensor nodes and response nodes transfer resources along the meta-paths OO→D←A and A→OO to their connected nodes.

[0098] Finally, according to the type and quantity of resources carried by the decision nodes, the relationship weight matrix of the decision nodes is established, and based on the matrix, it is predicted whether there is an unobserved link between the two nodes.

[0099] In summary, the teacher model studies the topological structure characteristics of nodes based on the Meta-Probs algorithm and uses the convergence process of the H-index sequence as the node embedding vector based on the DHC theorem; the vector obtained by this network representation method has the advantages of determinable dimension and interpretable elements.

[0100] Then, the cosine distance between node embedding vectors is used to quantify the similarity between nodes, and this distance is used as the edge weight to control the resource diffusion process of the ProbS framework.

[0101] Finally, link prediction is performed based on the ranking of the scores (or setting a threshold). The algorithm flow is shown in Figure 5.

[0102] In training a student model based on the teacher model's predictions to generate link predictions, the student model strives to achieve relatively accurate link predictions in the shortest possible time. To achieve this, this study employed a link prediction algorithm based on network topology characteristics within the student model. Furthermore, to further improve link prediction accuracy, we conducted in-depth analysis of the teacher model's output and continuously adjusted the student model's similarity calculation formula based on these results.

[0103] Through this process, we successfully determined the corresponding optimal parameter values ​​for the corresponding OODA heterogeneous network and applied them to the link prediction of the student model.

[0104] The connections between nodes on a real network are localized, so using the local topology information between nodes for prediction will achieve better results. Currently, the representative indicators include the common neighbor index (CN index).

[31] , Resource Allocation Index (RA Index)

[33] and Adamic-Adar indicator (AA indicator)

[32] Given a network G = (V, E, T), where V is the set of nodes, E is the set of edges, and T is the set of node or edge types. ij ] N×N represents the adjacency matrix of G. If there is a link between node i and node j, then a ij =1, otherwise a ij = 0. The calculation formula of CN index is as follows:

[0105]

[0106] in is the set of neighbor nodes of node i, is the set of neighbor nodes of node j.

[0107] However, the second-order path method has obvious limitations when dealing with sparse networks, and tends to over-prefer certain specific connections in dense network structures. In order to overcome this challenge and specifically target the structural characteristics of protein networks, scholars such as KOVK CS [8] A similarity calculation strategy based on third-order pathways was proposed. This algorithm significantly improved the accuracy of predicting potential interactions in protein networks.

[0108] Inspired by this study, ZHOU et al.

[60] further integrated the three key indicators of CN, AA and RA into the framework of the third-order path and constructed a new local similarity algorithm. The research data showed that in some network scenarios, the prediction method using the third-order path showed higher prediction accuracy than the second-order path method, thus proving its superior performance. Similar to the second-order path indicator, Martínez et al.

[14] Three third-order path similarity indices, CN_L3, AA_L3, and RA_L3, are proposed. In this paper, the CN_L3 index is used to represent the third-order path similarity, which is defined as follows:

[0109]

[0110] in is the set of neighbor nodes of node i, is the set of neighbor nodes of node j, a xy is an element in the adjacency matrix.

[0111] In order to make the similarity calculation formula adaptable to sparse networks and perform well in dense networks, this paper uses a combination of third-order paths and second-order paths to quantify similarity:

[0112] S ij =CN+α×CN_L3 (4-3)

[0113] Among them S ij represents the similarity between node i and node j, CN and CN_L3 represent the second-order path and third-order path indicators of node i and node j, respectively. The α value is obtained through model learning.

[0114] At the same time, considering the heterogeneity of OODA heterogeneous networks and analyzing the characteristics of OODA heterogeneous networks, it can be found that the D node is located at the central node position of the network, so the link similarity between the D node is often greater than that between other nodes. At the same time, since the A node is located at the nerve endings of the network, there is no possibility of links between the A nodes.

[0115] Taking all factors into consideration, we adjust the similarity formula:

[0116]

[0117] Where T(i) and T(j) represent the types of node i and node j respectively.

[0118] The student model needs to make fine adjustments and optimizations to the similarity formula with the help of the teacher-student learning mechanism to ensure that it can more accurately adapt to the characteristics of the OODA heterogeneous network and thus achieve better link prediction results.

[0119] During this learning process, the student model analyzes the teacher model's predictions and then adjusts the parameters and weights of its own similarity calculation formula. This allows the student model to gradually improve the accuracy and stability of its link predictions, ultimately achieving the goal of efficient and accurate link prediction in heterogeneous OODA networks.

[0120] According to another aspect of the embodiments of the present application, a macro model construction device for implementing the above-mentioned OODA heterogeneous network link prediction method is also provided. The device may include:

[0121] A construction unit for constructing a teacher-student model based on the OODA heterogeneous network model using the CROTS algorithm;

[0122] A first processing unit is configured to map nodes in the OODA heterogeneous network to a low-dimensional vector space, and predict the OODA heterogeneous network using a probability propagation algorithm through a teacher model to obtain a prediction result;

[0123] The second processing unit is used to train a student model based on the prediction result to perform link prediction.

[0124] It should be noted here that the examples and scenarios implemented by the above modules and corresponding steps are the same, but are not limited to the contents disclosed in the above embodiments. It should be noted that the above modules as part of the device can run in a hardware environment, can be implemented by software, and can also be implemented by hardware, where the hardware environment includes a network environment.

[0125] According to another aspect of the embodiments of the present application, a storage medium is further provided. Optionally, in this embodiment, the storage medium can be used to execute the program code of any of the above-mentioned OODA heterogeneous network link prediction methods in the embodiments of the present application.

[0126] Optionally, in this embodiment, the storage medium is configured to store program codes for executing the following steps:

[0127] S1, obtaining S-parameter data of the target system and constructing an initial macro model using a rational function form in the frequency domain, wherein the rational function form is a partial expansion;

[0128] S1, based on the OODA heterogeneous network, adopts the CROTS algorithm to build a teacher-student model;

[0129] S2, maps the nodes in the OODA heterogeneous network to a low-dimensional vector space, and uses the probability propagation algorithm through the teacher model to predict the OODA heterogeneous network and obtain the prediction results;

[0130] S3: Training a student model based on the prediction results to perform link prediction.

[0131] Optionally, the specific examples in this embodiment may refer to the examples described in the above embodiments, which will not be described in detail in this embodiment.

[0132] Among them, computer-readable storage media may include, but are not limited to, any type of disk, including floppy disks, optical disks, DVDs, CD-ROMs, microdrives and magneto-optical disks, ROMs, RAMs, EPROMs, EEPROMs, DRAMs, VRAMs, flash memory devices, magnetic or optical cards, nanosystems (including molecular memory ICs), or any type of medium or device suitable for storing instructions and / or data.

[0133] According to another aspect of an embodiment of the present application, an electronic device for implementing the above-mentioned OODA heterogeneous network link prediction method is also provided. The electronic device can be a server, a terminal, or a combination thereof.

[0134] Figure 6 is a schematic structural diagram of an optional electronic device according to an embodiment of the present application, such as Figure 6 As shown, it includes a processor 602, a communication interface 604, a memory 606 and a communication bus 608, wherein the processor 602, the communication interface 604, and the memory 606 communicate with each other via the communication bus 608, wherein,

[0135] Memory 606, for storing computer programs;

[0136] The processor 602 is configured to execute the computer program stored in the memory 606 to implement the following steps:

[0137] S1, based on the OODA heterogeneous network, adopts the CROTS algorithm to build a teacher-student model.

[0138] S2, maps the nodes in the OODA heterogeneous network to a low-dimensional vector space, and uses the probability propagation algorithm through the teacher model to predict the OODA heterogeneous network and obtain the prediction results.

[0139] S3: Training a student model based on the prediction results to perform link prediction.

[0140] Optionally, the communication bus may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus. The communication bus may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 6The communication interface is used for communication between the electronic device and other devices.

[0141] The memory may include RAM, or may include non-volatile memory, such as at least one disk memory. Alternatively, the memory may also be at least one storage device located away from the aforementioned processor.

[0142] The above-mentioned processor can be a general-purpose processor, including but not limited to: CPU (Central Processing Unit), NP (Network Processor), etc.; it can also be DSP (Digital Signal Processing), ASIC (Application Specific Integrated Circuit), FPGA (Field-Programmable Gate Array) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0143] Optionally, the specific examples in this embodiment may refer to the examples described in the above embodiments, and this embodiment will not be described in detail here.

[0144] It should be noted that for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that this application is not limited by the order of the actions described, because according to this application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by this application.

[0145] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0146] In the several embodiments provided in this application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some service interface, and the indirect coupling or communication connection of the device or unit can be electrical or other forms.

[0147] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0148] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0149] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a memory, including a number of instructions for enabling a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned memory includes: various media that can store program codes, such as a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk or an optical disk.

[0150] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable memory, which may include: a flash drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0151] The above is only an exemplary embodiment of the present disclosure and cannot be used to limit the scope of the present disclosure. That is, any equivalent changes and modifications made according to the teachings of the present disclosure are still within the scope of the present disclosure. After considering the specification and practicing the disclosure herein, those skilled in the art will easily think of the implementation scheme of the present disclosure. This application is intended to cover any variation, use or adaptation of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary technical means in the art that are not recorded in the present disclosure. The description and examples are to be regarded as exemplary only, and the scope and spirit of the present disclosure are defined by the claims.

[0152] The technical features of the above embodiments can be combined arbitrarily. 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.

[0153] It will be easily understood by those skilled in the art that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A OODA heterogeneous network link prediction method, characterized in that: include: Based on the OODA heterogeneous network model, the CROTS algorithm is used to build a teacher-student model; The Meta-Probs algorithm is used to construct the teacher model and obtain the node topology structure characteristics; Based on the node topology structure characteristics, the degree, H-index and density (DHC) theorem are used to represent the network and obtain the node embedding vector; Based on the DHC theorem, the convergence process of the H-index sequence is used as the node embedding vector; The cosine distance between node embedding vectors is used to quantify the similarity between nodes and used as edge weight to control the resource diffusion of the ProbS framework to obtain the link weight matrix. Perform link prediction according to the link weight matrix to obtain a prediction result; A student model is trained based on the prediction results to perform link prediction.

2. The OODA heterogeneous network link prediction method according to claim 1, characterized in that: The training of the student model based on the prediction result to perform link prediction includes: Constructing the adjacency matrix of the OODA heterogeneous network; constructing a similarity function based on the adjacency matrix; A ranking result is determined based on the similarity function to obtain a predicted link.

3. The OODA heterogeneous network link prediction method according to claim 2, characterized in that: The constructing a similarity function based on the adjacency matrix includes: Initializing optimal parameter values ​​and corresponding maximum predicted link numbers based on the adjacency matrix; Analyze the teacher model prediction results based on the optimal parameter values ​​and the maximum number of predicted links to obtain the updated optimal parameter values; A similarity function is determined based on the updated optimal parameter value.

4. The OODA heterogeneous network link prediction method according to claim 2, wherein: The similarity function is specifically: ； in, Representation node and nodes The similarity of and Represents nodes respectively and nodes The second-order and third-order path indicators; The value is learned by the model; and Represent the types of node i and node j respectively.

5. The OODA heterogeneous network link prediction method according to claim 2, characterized in that: The adjacency matrix includes a second-order path matrix and a third-order path matrix.

6. The OODA heterogeneous network link prediction method according to claim 1, characterized in that: The CROTS algorithm is used in the OODA heterogeneous network model to construct a teacher-student model, including: Conduct network modeling on OODA heterogeneous network and build OODA heterogeneous network model; A teacher-student model is constructed based on the OODA heterogeneous network model.

7. An OODA heterogeneous network link prediction device, characterized in that: include: A construction unit for constructing a teacher-student model based on the OODA heterogeneous network model using the CROTS algorithm; a first processing unit configured to map nodes in the OODA heterogeneous network to a low-dimensional vector space, and predict the OODA heterogeneous network using a probability propagation algorithm through a teacher model to obtain a prediction result; said mapping the nodes in the OODA heterogeneous network to the low-dimensional vector space, and predicting the OODA heterogeneous network using a probability propagation algorithm through a teacher model to obtain a prediction result, including performing a network representation based on the node topology structure characteristics using the degree, H-index, and density (DHC) theorem to obtain a node embedding vector; and using the convergence process of the H-index sequence as the node embedding vector based on the DHC theorem; The cosine distance between node embedding vectors is used to quantify the similarity between nodes and used as the edge weight to control the resource diffusion of the ProbS framework to obtain a link weight matrix; link prediction is performed based on the link weight matrix to obtain a prediction result; The second processing unit is used to train a student model based on the prediction result to perform link prediction.

8. An electronic device comprising a memory and a processor, characterized in that: A computer program is stored in the memory, and the processor is configured to execute the method according to any one of claims 1 to 6 through the computer program.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored program, wherein the program executes the method according to any one of claims 1 to 6 when executed.

Images