A method and system for complex network without difference propagation

Abstract

This invention relates to the field of control theory for complex systems, and discloses a method for indiscriminate propagation in complex networks. The method includes constructing a typical topological model of a complex system containing control flow and information flow, building a general response mechanism and logical model between information flow and control flow, proposing characteristic parameters to characterize indiscriminate propagation in complex networks, and verifying the effectiveness of the model. This method can eliminate the influence of restricted control flow or information flow, allowing information and control signals to propagate freely throughout the system. It solves the problem that indiscriminate propagation in complex systems containing control flow and information flow is limited or interfered with by specific nodes. The application of this method improves the reliability and robustness of the system.

Description

Technical Field

[0001] This invention relates to the field of control theory for complex systems, and more particularly to a method for indiscriminate propagation in complex networks. Background Technology

[0002] In recent years, to analyze the dynamic and static characteristics of complex systems, scholars both domestically and internationally have proposed a variety of new network models. Addressing the "six degrees of separation" phenomenon in real-world networks, Watts and Strogatz proposed the classic small-world network model. Based on the continuous growth and selective connections of real-world networks, Barabisi and Albert proposed the BA model for dynamic network evolution. Furthermore, considering the prevalent local-world phenomenon in real-world networks, a classic local-world evolution model has been proposed. A neighborhood evolution model has been established to define "neighborhood" with depth, further concretizing the concept of the local world. The proposal of these classic complex network models provides a concise and normalized approach for studying the general characteristics of complex systems.

[0003] Currently, driven by the digital transformation of various complex systems, previous complex systems have achieved and are trending towards achieving full integration of information flow and control flow to improve operational efficiency. Complex system theory is also facing a comprehensive update in response to the rapid development of big data and AI technologies. Traditional complex network theory employs a unified modeling theory. This invention proposes a two-layer network modeling method for information control and identifies corresponding network characteristic parameters to meet the analysis needs of increasingly information-driven and digitalized complex information control systems. This invention provides a fundamental reference for network characteristic analysis of information control systems and has significant supplementary value to complex network theory. Summary of the Invention

[0004] The purpose of this section is to outline some aspects of embodiments of the present invention and to briefly describe some preferred embodiments. Simplifications or omissions may be made in this section, as well as in the abstract and title of this application, to avoid obscuring the purpose of these documents; however, such simplifications or omissions should not be construed as limiting the scope of the invention.

[0005] In view of the aforementioned existing problems, the present invention is proposed.

[0006] Therefore, this invention provides a method for indiscriminate propagation in complex networks, which can solve the problem that traditional unified modeling cannot meet the needs of analyzing increasingly information-based and digitalized complex information control systems.

[0007] To address the aforementioned technical problems, this invention provides the following technical solution: a method for indiscriminate propagation in complex networks, comprising:

[0008] Construct a typical topology model of a complex system containing control flow and information flow;

[0009] Construct a general response mechanism and logical model between information flow and control flow;

[0010] We propose characteristic parameters to characterize indiscriminate propagation complex networks and verify the effectiveness of the model.

[0011] As a preferred embodiment of the method for indiscriminate propagation in complex networks described in this invention, the complex system topology model containing control flow and information flow includes an information publishing node rate defined as:

[0012] A = Nf / N

[0013] Where A represents the information publishing node rate, Nf represents the number of information publishing nodes in the information network, and N represents the total number of nodes in the information network;

[0014] Define the weight factor N of the region a containing node i relative to the information system. i :

[0015]

[0016] Where i represents an information node, N i∈a ∑N represents the number of nodes in region a where i is located in the information flow network. j Let k be the total number of nodes in the information flow network, 'a' represent the security domain number, and 'k' represent the number of nodes in the network. area The weighting coefficients for the security region.

[0017] As a preferred embodiment of the method for indiscriminate propagation in complex networks described in this invention, the complex system topology model containing control flow and information flow further includes defining the control weight of region a where node i is located relative to the control system, expressed as:

[0018]

[0019] Where, β i C represents the control weights of the control system. i∈a ∑C represents the number of control system nodes controlled by region a, where i is located in the information flow network. j This represents the total number of nodes in the control flow network;

[0020] Define the control weight of region a, where ordinary node i is located, relative to the entire system as follows:

[0021] μ ii =N i β i

[0022] Where, μ ii This indicates the control weight.

[0023] As a preferred embodiment of the method for indiscriminate propagation in complex networks described in this invention, the construction of a general response mechanism between information flow and control flow includes a near-zero resistance modeling method for information flow, wherein the information dissemination pressure of the information flow network is defined as:

[0024]

[0025] Where j represents the information flow node, This represents the information publishing pressure value of publishing node J.

[0026] As a preferred embodiment of the method for non-discriminatory propagation in complex networks described in this invention, the construction of a general logical model between information flow and control flow includes establishing an OPF-like model for the control flow using a flow-like approach. The information control network is a two-layer network system with multiple security domain partitions. Ignoring the mutual influence at the overlap of security domains, the information flow network nodes are divided into information publishing nodes and transmission nodes. Information publishing nodes located in a specific security domain publish information to all controllable nodes in real time without discrimination through the information flow network. The amount of information injected by the information publishing node is:

[0027] U = [u1, u2, ..., u] n ] T

[0028] Where T represents a branch, n represents a node, and U represents the amount of injected information;

[0029] The branch flow rate is obtained by the flow-like method as follows:

[0030] F l =B l AU

[0031] Among them, F l B represents the branch current. l Represents a node conduction array. A represents the incidence matrix.

[0032] As a preferred embodiment of the method for indiscriminate propagation of complex networks according to the present invention, the proposed feature parameters characterizing the indiscriminate propagation complex network include hyper-feature coefficients, expressed as follows:

[0033]

[0034] Where S represents the hypereigenvalue coefficient, g ij N represents the number of nodes traversed by the shortest path between information publishing node i and any transmission node j, and N represents the number of network nodes.

[0035] As a preferred embodiment of the method for indiscriminate propagation in complex networks according to the present invention, the proposed characteristic parameters characterizing indiscriminate propagation complex networks further include the number of medium centers of transmission nodes and the information control entropy, defined as:

[0036]

[0037]

[0038] Where M represents the number of medium centers of the transmission nodes, m represents the number of nodes, and d ij This represents the number of paths between any pair of nodes in the network, excluding node m. H represents the number of paths passing through node m in a node pair, and H represents the information control entropy. A vector function representing the degree of each node in an information network. A vector function representing the degree of each node in the control network.

[0039] A computer device includes a memory and a processor, the memory storing a computer program, characterized in that the processor executes the computer program to implement the steps of a method for indiscriminate propagation in a complex network.

[0040] A computer-readable storage medium having a computer program stored thereon, characterized in that, when the computer program is executed by a processor, it implements the steps of a method for indiscriminate propagation in a complex network.

[0041] Another objective of this invention is to provide a novel system for indiscriminate propagation in complex networks, which enables information control of a two-layer network modeling method and solves the problem of network stability compared to actual complex networks.

[0042] A system for a method of indiscriminate propagation in complex networks, characterized by comprising a network construction module, an information encoding and decoding module, an information propagation module, a propagation gradient calculation module, a propagation path recording module, and a propagation result evaluation module;

[0043] The network construction module constructs a network using concepts from graph theory, including the definition of nodes and edges, and the selection of network structure.

[0044] The information encoding and decoding module encodes the transmitted information, transmits it in the network, and simultaneously decodes it at the target node to obtain the original information.

[0045] The information propagation module transmits the encoded information along the network edges to adjacent nodes;

[0046] The propagation gradient calculation module calculates the propagation gradient of each node during the information propagation process.

[0047] The propagation path recording module records the path of information propagation;

[0048] The propagation result evaluation module evaluates and analyzes the propagation results, including the calculation and analysis of propagation range, propagation speed, and propagation impact.

[0049] The beneficial effects of this invention are as follows: By constructing a typical complex system topology model containing control flow and information flow, as well as a response mechanism and logic model, this invention can more accurately predict the propagation process of information or viruses in the network, and can intervene and control through control flow. It can be applied to the field of network security to improve network security and stability, and provides new ideas and methods for complex network research. Attached Figure Description

[0050] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Wherein:

[0051] Figure 1 This is a schematic flowchart of a method for indiscriminate propagation in complex networks, provided as an embodiment of the present invention.

[0052] Figure 2 This is a schematic diagram of a two-layer complex network equivalent model of a method for indiscriminate propagation in complex networks, provided as an embodiment of the present invention.

[0053] Figure 3 The control flow network topology diagram is provided for a method of indiscriminate propagation in complex networks according to an embodiment of the present invention.

[0054] Figure 4 This is an equivalent diagram illustrating a method for indiscriminate propagation in complex networks according to an embodiment of the present invention.

[0055] Figure 5 This is a security domain topology diagram of a method for indiscriminate propagation in complex networks, provided as an embodiment of the present invention.

[0056] Figure 6 This is an equivalent diagram of an information-control system for a method of indiscriminate propagation in complex networks, provided as an embodiment of the present invention.

[0057] Figure 7 The Thevenin equivalent diagram illustrates a method for indiscriminate propagation in complex networks according to an embodiment of the present invention.

[0058] Figure 8This is a schematic diagram of a typical switching structure for a method of indiscriminate propagation in complex networks, provided as an embodiment of the present invention.

[0059] Figure 9 This is a schematic diagram illustrating the workflow of a complex network indiscriminate propagation system according to an embodiment of the present invention. Detailed Implementation

[0060] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0061] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0062] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0063] This invention is described in detail with reference to the schematic diagrams. When detailing the embodiments of this invention, for ease of explanation, the cross-sectional views illustrating the device structure may be partially enlarged, not adhering to the usual scale. Furthermore, the schematic diagrams are merely examples and should not be construed as limiting the scope of protection of this invention. In actual fabrication, the three-dimensional spatial dimensions of length, width, and depth should be included.

[0064] Furthermore, in the description of this invention, it should be noted that the terms "upper," "lower," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. These terms are used solely for the convenience of describing the invention and for simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the invention. In addition, the terms "first," "second," or "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0065] Unless otherwise explicitly specified and limited, the terms "installation," "connection," and "joining" in this invention should be interpreted broadly. For example, they can refer to fixed connections, detachable connections, or integral connections; similarly, they can refer to mechanical connections, electrical connections, or direct connections, or indirect connections through an intermediate medium, or internal connections between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0066] Example 1

[0067] Reference Figures 1-4 This is the first embodiment of the present invention, which provides a method for indiscriminate propagation in complex networks, comprising:

[0068] S1: Construct a typical topology model of a complex system containing control flow and information flow.

[0069] Furthermore, the complex system topology model containing control flow and information flow includes an information publishing node rate defined as:

[0070] A = Nf / N

[0071] Where A represents the information publishing node rate, Nf represents the number of information publishing nodes in the information network, and N represents the total number of nodes in the information network;

[0072] Define the weight factor N of the region a containing node i relative to the information system. i :

[0073]

[0074] Where i represents an information node, N i∈a ∑N represents the number of nodes in region a where i is located in the information flow network. j Let k be the total number of nodes in the information flow network, 'a' represent the security domain number, and 'k' represent the number of nodes in the network. area The weighting coefficients for the security region.

[0075] Furthermore, the control weight of region a, where node i is located, relative to the control system is defined as follows:

[0076]

[0077] Where, β i C represents the control weights of the control system. i∈a ∑C represents the number of control system nodes controlled by region a, where i is located in the information flow network. j This represents the total number of nodes in the control flow network;

[0078] It should be noted that this expression characterizes the indifferent control characteristics of all information nodes in region a, that is, the control weight of node i is only related to the security region where node i is located and the number of control flow nodes that can be controlled in that security region.

[0079] Furthermore, the control weight of region a, where ordinary node i is located, relative to the entire system is defined as follows:

[0080] μ ii =N i β i

[0081] Where, μ ii Represents the control weight, μ ii The larger the value, the greater the control weight of information node i on the system (this includes information on the decreasing security domain, the number of control nodes, and the proportion of nodes within the security domain in the information system).

[0082] S2: Construct a general response mechanism and logical model between information flow and control flow.

[0083] Furthermore, the construction of a general response mechanism between information flow and control flow includes a near-zero resistance modeling method for information flow, whereby the information dissemination pressure of the information flow network is defined as:

[0084]

[0085] Where j represents the information flow node, This represents the information publishing pressure value of publishing node J.

[0086] It should be noted that, similarly, 1 / La represents the information flow conduction value controlled by the information flow publishing node a, and I f，a The required flow rate for information flow channel a is characterized by the increased control flow rate required by the more control nodes that the publishing node a can control. In this case, the transmission delay of the information flow network is ignored, and a near-zero resistance method is used: the equivalent circuit of a system with two information publishing nodes is shown in the figure above. La and Lb quantitatively characterize the control capability of the information nodes on the control network, and the transmission resistance of the information network is zero and negligible.

[0087] Furthermore, an OPF-like model is established using a flow-like approach for control flow. The information-control network is viewed as a two-layer network system with multiple security domains, ignoring the mutual influence at the overlap of security domains. Information flow network nodes are divided into information publishing nodes and transmission nodes. Information publishing nodes located in a specific security domain can publish information to all controllable nodes in real time without discrimination through the information flow network. The amount of information injected by the information publishing node is:

[0088] U = [u1, u2, ..., u] n ]

[0089] Where T represents a branch, n represents a node, and U represents the amount of injected information;

[0090] Furthermore, if the node is an information publishing node, then u i =u f，i If the node is an information transmission node, then u i =0, for information publishing nodes, there are upper and lower limits to the information injected into the node:

[0091]

[0092] in, Let i be the minimum amount of information to be published by information publishing node i. Let F be the minimum amount of information published by information publishing node i, and let F be the set of information publishing nodes.

[0093] If the node conduction matrix B, neglecting the performance of overlapping nodes, then:

[0094]

[0095] Furthermore, the branch power flow can be obtained using the power flow analogy method:

[0096] F l =B l AU

[0097] Among them, F l B represents the branch current. l Represents a node conduction array. A represents the incidence matrix.

[0098] Furthermore, the operation of information-control networks must balance security and economy. When performing optimal information flow, the actual injected information of the system can be solved using the following optimization method:

[0099]

[0100] In the formula, ∑ i∈F u i Indicates the total amount of information released. This represents the minimum and maximum information capacity of a branch.

[0101] It should be noted that the model established based on this invention can be used to evaluate and identify control loops (specific expressions) of information congestion, providing direction for network planning. Based on the above calculation results, information carrying capacity and information sufficiency are defined as follows:

[0102]

[0103]

[0104] It depends on the narrowest information channel on the information link.

[0105] S3: Propose characteristic parameters to characterize indiscriminate propagation complex networks and verify the effectiveness of the model.

[0106] Furthermore, the proposed characteristic parameters for characterizing indiscriminate propagation complex networks include hyperesthetic coefficients, expressed as:

[0107]

[0108] Where S represents the hypereigenvalue coefficient, g ij N represents the number of nodes traversed by the shortest path between information publishing node i and any transmission node j, and N represents the number of network nodes.

[0109] It should be noted that the super-eigenvalues ​​S ignore the influence of transmission rate (the influence of edges and distance) and characterize the tightness of the connection between information groups.

[0110] Furthermore, the proposed characteristic parameters for characterizing indiscriminate propagation complex networks also include the number of medium centers of the transmission nodes and the information control entropy, defined as:

[0111]

[0112]

[0113] Where M represents the number of medium centers of the transmission nodes, m represents the number of nodes, and d ij This represents the number of paths between any pair of nodes in the network, excluding node m. H represents the number of paths passing through node m in a node pair, and H represents the information control entropy. A vector function representing the degree of each node in an information network. A vector function representing the degree of each node in the control network.

[0114] It should be noted that the information-control entropy H follows the cosine theorem. The cosine value of the spatial angle between two vectors can characterize the similarity between the two vectors. H mainly provides the degree of order between the information network and the control network in the vertical direction. It is mainly used to deal with situations in actual networks where nodes with high degree in the control network have low degree in the corresponding nodes in the information network, thus ensuring the homogeneous development of the control network and the information network.

[0115] Example 2

[0116] Reference Figures 5-8 This is one embodiment of the present invention, which provides a method for indiscriminate propagation in complex networks. To verify the beneficial effects of the present invention, scientific demonstration is carried out through experiments.

[0117] Based on the model established by this invention, the response effect generated by the control loop can be quantitatively described through selectable excitations of Um. Furthermore, Um(t) can be a time-varying information variable, including reverse excitation, user-defined excitation, etc. At the same time, according to the above analysis, this model is applicable to the group excitation / response analysis of network hole control node groups based on security domains. In particular, the generalized definition of security domain is extended to include the group excitation / response analysis of local / neighborhood node groups defined by geography, topological depth, etc. in the same security domain.

[0118] Similarly, the model established based on this invention satisfies Thevenin equivalence and Norton equivalence, and satisfies the maximum power flow control threshold of the edges (in a management system, the control authority of nodes, the maximum connectivity of relationships in interpersonal relationships, the carrying capacity of traffic lines in traffic flow, the maximum amount of communication information in a communication network, etc.).

[0119] Assuming custom Setting it as a signal source with a tuned capacitor, the equivalent network can be represented by the Thevenin equivalent as follows: Figure 7 .

[0120] Ue is the equivalent source after Thevenin equivalence, Le is the equivalent impedance after using Thevenin, and R approaches 0 under ideal conditions without considering signal transmission delay. Obviously, by adjusting Cm, a resonant circuit can be generated under the action of the signal source Um, allowing the signal source Um, which crosses the dual safety domain, to propagate continuously in the network and generate oscillations at the same frequency. Obviously, the larger the resistance R is, the faster the oscillation decays. For the information publishing node, Um can be set to any condition that is beneficial to the stability of the circuit power flow and the voltage of each circuit component. Um can also be time-varying to meet the stability of the energy field in the circuit. The switching elements in the circuit can simulate the conversion and switching of the information energy field. Through this model, the transformation process of the information energy field under various operating conditions can be easily quantitatively analyzed to achieve a smooth transition of the energy field.

[0121] Switching rule: The switching rule is defined to characterize the non-abrupt change of the energy field at the moment of switching. Under the condition of following the switching rule, we try our best to keep the energy from being dissipated.

[0122] Based on the above model, a typical switching structure is as follows: Figure 8 As shown

[0123] Initial state: k1 is closed, k2 is open. At this time, Um acts on the original signal-control loop. Through the adjustment of Cm and Rold, the energy field of the l1 loop is minimized. Switching state: k1 is opened, k2 is closed, and the l2 loop is turned on, realizing the switching.

[0124] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

[0125] Example 3

[0126] The third embodiment of the present invention differs from the first two embodiments in that:

[0127] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0128] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0129] More specific examples of computer-readable media (a non-exhaustive list) include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0130] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0131] Example 4

[0132] Reference Figure 9 As an embodiment of the present invention, a system for a method of indiscriminate propagation in complex networks is provided, characterized in that it includes a network construction module, an information encoding and decoding module, an information propagation module, a propagation gradient calculation module, a propagation path recording module, and a propagation result evaluation module;

[0133] The network construction module constructs networks using concepts from graph theory, including the definition of nodes and edges, and the selection of network structures.

[0134] The information encoding and decoding module encodes the transmitted information, transmits it through the network, and simultaneously decodes it at the target node to obtain the original information.

[0135] The information propagation module transmits the encoded information along the network edges to adjacent nodes.

[0136] The propagation gradient calculation module calculates the propagation gradient of each node during the information propagation process.

[0137] The propagation path recording module records the path of information propagation.

[0138] The propagation result evaluation module evaluates and analyzes the propagation results, including the calculation and analysis of propagation range, propagation speed, and propagation impact.

[0139] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for indiscriminate propagation in complex networks, characterized in that: include, Construct a typical topology model of a complex system containing control flow and information flow; Construct a general response mechanism and logical model between information flow and control flow; For the control flow, an OPF-like model is established using a flow-like approach. The information control network is a two-layer network system with multiple security domains. Ignoring the mutual influence at the overlap of security domains, the information flow network nodes are divided into information publishing nodes and transmission nodes. Information publishing nodes located in a specific security domain publish information to all controllable nodes in real time without discrimination through the information flow network. The amount of information injected by the information publishing node is: Where T represents transpose, n represents node, and U represents the amount of injected information; The branch flow rate is obtained by the flow-like method as follows: = U in, Indicates the tidal current of the branch line. Represents a node conduction array. =diag A represents the correlation matrix; We propose characteristic parameters to characterize indiscriminate propagation complex networks and verify the effectiveness of the model. The proposed feature parameters characterizing indiscriminate propagation complex networks include hyper-feature coefficients, denoted as: Where S represents the hypereigenvalue coefficient. N represents the number of nodes traversed by the shortest path between information publishing node p and any transmission node t, N represents the number of network nodes, and Nf represents the number of information publishing nodes in the information flow network. It also includes the number of medium centers of the transmission nodes and the information control entropy, defined as: in, This represents the number of medium centers of the transmission nodes, where m represents the number of nodes. This represents the number of paths between any pair of nodes in the network, excluding node m. H represents the number of paths passing through node m in a node pair, and H represents the information control entropy. A vector function representing the degree of each node in an information flow network. A vector function representing the degree of each node in a control flow network.

2. A system employing the method for indiscriminate propagation in complex networks as described in claim 1, characterized in that: It includes a network construction module, an information encoding and decoding module, an information propagation module, a propagation gradient calculation module, a propagation path recording module, and a propagation result evaluation module; The network construction module constructs a network using concepts from graph theory, including the definition of nodes and edges, and the selection of network structure. The information encoding and decoding module encodes the transmitted information, transmits it in the network, and simultaneously decodes it at the target node to obtain the original information. The information propagation module transmits the encoded information along the network edges to adjacent nodes; The propagation gradient calculation module calculates the propagation gradient of each node during the information propagation process. The propagation path recording module records the path of information propagation; The propagation result evaluation module evaluates and analyzes the propagation results, including the calculation and analysis of propagation range, propagation speed, and propagation impact.

3. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method of claim 1.

4. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the method of claim 1.

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