A network key node identification result evaluation method and device

Abstract

The application discloses a network key node identification result evaluation method and device, and the method comprises the following steps: S1, acquiring a target network and an identification result set; S2, performing standardization processing on the target network to obtain a standard network; S3, performing processing on the standard network and the identification result set to obtain a resilience sequence; the resilience sequence comprises N resilience values; and S4, utilizing a comprehensive evaluation model to perform processing on the resilience sequence to obtain a comprehensive evaluation value. The application can quantitatively evaluate the effect of key node identification.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of network key node identification result evaluation, and particularly relates to a network key node identification result evaluation method and device. BACKGROUND

[0002] In a communication network, key identification of nodes and obtaining of a key value corresponding to each node are the basis for guaranteeing stable operation and efficient management of the network. The communication network is composed of a plurality of nodes connected with each other, and different nodes play different roles in data transmission and network topology structure. Through key identification, core nodes that play a decisive role in network connectivity and data flow efficiency, such as hub nodes or bridge nodes, can be accurately located. Once these nodes fail, a large area of network may be paralyzed or transmission efficiency may be sharply reduced. Clear key values of each node are like a "key map" drawn for network management, which enables operation and maintenance personnel to clearly understand the importance difference of nodes.

[0003] In addition, in the network planning stage, according to the key value of the node, resources can be reasonably allocated, and hardware configuration and redundancy design of high key nodes can be preferentially strengthened to avoid network bottlenecks caused by resource mismatch; in network optimization, the traffic load of key nodes can be adjusted to improve the overall network performance; and in security protection, high key nodes can be monitored and protected to prevent malicious attacks or fault spread, so that the reliability, robustness and risk resistance of the communication network can be improved in a more scientific way, and the network can be stably and efficiently operated in various scenarios.

[0004] The resilience of a network refers to the ability of a communication network to maintain normal functions or quickly recover when facing various types of interference (such as hardware failure, malicious attack, and traffic surge). It not only reflects the "anti-interference" of the network when it is attacked, but also emphasizes the "self-healing ability" of the network to quickly recover services, adaptively adjust the topology structure or redistribute resources after being damaged. Unlike traditional reliability, resilience focuses more on the dynamic adaptation process of the network in dynamic changes and unexpected events, and covers the whole cycle of response ability from fault occurrence to system recovery.

[0005] However, different network key node identification methods identify different key nodes, and there is a lack of scientific and reasonable quantitative method and basis for the identification effect, especially the identification result. It is impossible to judge whether the identified nodes have a high impact on the network resilience, so it is impossible to select the best key node identification method for the actual communication network. SUMMARY

[0006] The technical problem solved by the present application is to provide a network key node identification result evaluation method and device, which can quantitatively evaluate the effect of key node identification.

[0007] To solve the above technical problem, the present application discloses a network key node identification result evaluation method, which comprises the following steps:

[0008] S1, obtaining a target network and an identification result set;

[0009] The target network comprises N nodes, N attribute sequences and M edges; the attribute sequence comprises T attribute values; N, M and T are all integers greater than 1;

[0010] The identification result set comprises N key values corresponding to the nodes;

[0011] S2, performing standardization processing on the target network to obtain a standard network; the standard network comprises N nodes, N comprehensive attribute values and M edges;

[0012] S3, processing the standard network and the identification result set to obtain a resilience sequence; the resilience sequence comprises N resilience values;

[0013] S4, processing the resilience sequence by using a comprehensive evaluation model to obtain a comprehensive evaluation value.

[0014] As an optional implementation, in the first aspect of the present application, processing the standard network and the identification result set to obtain a resilience sequence comprises:

[0015] S31, performing descending order sorting processing on the identification result set to obtain an identification result queue;

[0016] S32, initializing an initial resilience sequence as an empty sequence;

[0017] S33, performing resilience evaluation processing on the standard network to obtain the resilience value; inserting the resilience value into the end of the initial resilience sequence;

[0018] S34, performing de-queue updating on the identification result queue to obtain a current key value and the identification result queue after de-queue updating;

[0019] S35, based on the current key value, performing deletion updating on the standard network to obtain the standard network after deletion updating;

[0020] S36, repeating S33-S35 until the length of the identification result queue is 1; determining the resilience sequence as the initial resilience sequence.

[0021] As an optional implementation, in the first aspect of the embodiment of the present application, the processing of the standard network to obtain the resilience value comprises:

[0022] S331, performing segmentation processing on the standard network to obtain N label vectors;

[0023] The length of the label vector is P, P is an integer greater than 1; the value of each component in the label vector is 0 or 1;

[0024] S332, based on the standard network and N label vectors, constructing an inter-network adjacency matrix and P local feature matrices; the inter-network adjacency matrix is a P-order square matrix;

[0025] S333, performing eigenvalue decomposition processing on the inter-network adjacency matrix and P local feature matrices respectively to obtain a first eigenvalue set and P second eigenvalue sets;

[0026] The first eigenvalue set includes P first eigenvalues; the second eigenvalue set includes several second eigenvalues;

[0027] S334, using a resilience evaluation model to process the first eigenvalue set and P second eigenvalue sets to obtain the resilience value.

[0028] As an optional implementation, in the first aspect of the embodiment of the present application, based on the standard network and N label vectors, constructing an inter-network adjacency matrix and P local feature matrices comprises:

[0029] S3321, based on N label vectors, performing pruning processing on the standard network to obtain P sub-networks;

[0030] The sub-network includes several nodes, several comprehensive attribute values and several edges; the number of nodes of the sub-network is the same as the number of comprehensive attribute values;

[0031] S3322, using an inter-network adjacency matrix construction model to process N label vectors to obtain an inter-network adjacency matrix;

[0032] S3323, using a local feature matrix construction model to process P sub-networks to obtain P local feature matrices.

[0033] As an optional implementation, in the first aspect of the embodiment of the present application, the expression of the inter-network adjacency matrix construction model is:

[0034] b i，j= δ(i, j) + [1 - δ(i, j)] x [1 - δ(<vec i , vec j >, 0)]

[0035] wherein b i,j is the element value of the i-th row and j-th column of the inter-network adjacency matrix; vec i is a vector composed of all i-th elements of the N label vectors; vec j is a vector composed of all j-th elements of the N label vectors; i and j are integers from 1 to P; <vec i , vec j > represents the inner product of vec i and vec j ; and δ(*,*) is the Kronecker function.

[0036] As an optional implementation, in the first aspect of the embodiment of the present application, the expression of the toughness evaluation model is:

[0037]

[0038] wherein V is the toughness value; p is an integer from 1 to P; N p is the number of the second characteristic values of the p-th second characteristic value set; γ p,q is the q-th second characteristic value of the p-th second characteristic value set; q is an integer from 1 to N p ; α is a preset proportion coefficient; λ t is the t-th first characteristic value of the first characteristic value set; and t is an integer from 1 to N.

[0039] As an optional implementation, in the first aspect of the embodiment of the present application, the expression of the comprehensive evaluation model is:

[0040]

[0041] wherein EV is the comprehensive evaluation value; V kk+1 and V kk are the (kk+1)-th and the kk-th toughness values in the toughness sequence respectively; and kk is an integer from 1 to N.

[0042] The second aspect of the embodiment of the present application discloses a network key node identification result evaluation device, which comprises a result acquisition module, a standardization module, a toughness evaluation module and a comprehensive evaluation module.

[0043] The result acquisition module is used to acquire a target network and an identification result set.

[0044] The standardization module is configured to perform standardization processing on the target network to obtain a standard network.

[0045] The toughness evaluation module is configured to perform processing on the standard network and the set of identification results to obtain a toughness sequence.

[0046] The comprehensive evaluation module is configured to perform processing on the toughness sequence by using a comprehensive evaluation model to obtain a comprehensive evaluation value.

[0047] The third aspect of the embodiment of the present application discloses another network key node identification result evaluation device, and the device comprises:

[0048] A memory in which executable program codes are stored;

[0049] A processor coupled with the memory;

[0050] The processor invokes the executable program codes stored in the memory to perform part or all of the steps of the network key node identification result evaluation method disclosed in the first aspect of the embodiment of the present application.

[0051] The fourth aspect of the present application discloses a computer storage medium, and the computer storage medium stores computer instructions, and the computer instructions are used to perform part or all of the steps of the network key node identification result evaluation method disclosed in the first aspect of the embodiment of the present application when the computer instructions are invoked.

[0052] Compared with the prior art, the embodiment of the present application has the following beneficial effects:

[0053] The present application performs standardization based on a target network and a corresponding set of identification results, performs toughness evaluation in the process of dynamically deleting high criticality value nodes, and further obtains a comprehensive evaluation value, so that the effect of key node identification can be quantitatively evaluated from the perspective of network vulnerability, and a scientific and reasonable basis is provided for the selection of key node identification methods. BRIEF DESCRIPTION OF DRAWINGS

[0054] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings needed in the embodiment description. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0055] Figure 1 is a flowchart of a network key node identification result evaluation method disclosed by the embodiment of the present application.

[0056] Figure 2is a structural schematic view of a network key node identification result evaluation device disclosed by an embodiment of the present application.

[0057] Figure 3 is a structural schematic view of another network key node identification result evaluation device disclosed by an embodiment of the present application. DETAILED DESCRIPTION

[0058] The technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative effort fall within the protection scope of the present application.

[0059] In the description of the present application, it should be noted that the orientations or positional relationships indicated by the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc. are based on the orientations or positional relationships shown in the drawings, and are only for the convenience of describing the present application and simplifying the description, and thus cannot be understood as indicating or implying that the devices or elements indicated must have a particular orientation, be constructed and operated in a particular orientation, and thus cannot be understood as a limitation on the present application. In addition, the terms "first", "second", "third" are only for the purpose of description, and cannot be understood as indicating or implying relative importance.

[0060] In the description of the present application, it should be noted that, unless otherwise explicitly specified and limited, the terms "mounting", "connecting", "connection" should be understood in a broad sense, for example, it can be fixed connection, or detachable connection, or integrally connected; it can be mechanical connection, or electrical connection; it can be directly connected, or indirectly connected through an intermediate medium, or the communication inside two elements. For a person of ordinary skill in the art, the specific meaning of the above terms in the present application can be understood according to the specific circumstances.

[0061] Embodiment one

[0062] Please refer to Figure 1 . Figure 1 is a flowchart of a network key node identification result evaluation method disclosed by an embodiment of the present application. Wherein, Figure 1 The described network key node identification result evaluation method is applied to network key node identification result evaluation, such as quantitative evaluation of key node identification results in a network, a sensor network and a satellite communication network, and the present application is not limited to this. For example Figure 1 As shown in the figure, the method comprises:

[0063] S1, obtaining a target network and an identification result set.

[0064] The target network includes N nodes, N attribute sequences, and M edges; the attribute sequence includes T attribute values; N, M, and T are integers greater than 1.

[0065] It should be noted that the target network can be a control network, a sensor network, or a satellite communication network, and the embodiments of the present application are not limited thereto.

[0066] The identification result set includes N key values corresponding to the N nodes.

[0067] It should be noted that the N nodes and the N attribute sequences are in one-to-one correspondence. Each attribute sequence is used to describe the communication capability of the corresponding node.

[0068] Preferably, the attribute sequence includes an average uplink rate, an average downlink rate, a node bandwidth, a packet loss rate, and a node delay, and the corresponding T is 5.

[0069] It should be noted that the identification result set is obtained by identifying key nodes of a preset target network using an existing method.

[0070] S2, standardizing the target network to obtain a standard network; the standard network includes N nodes, N comprehensive attribute values, and M edges.

[0071] S3, processing the standard network and the identification result set to obtain a resilience sequence.

[0072] The resilience sequence includes N resilience values.

[0073] S4, processing the resilience sequence using a comprehensive evaluation model to obtain a comprehensive evaluation value.

[0074] It can be seen that the network key node identification result evaluation method described in the embodiments of the present application quantitatively evaluates the effect of key node identification based on a target network and a corresponding identification result set, and obtains a corresponding comprehensive evaluation value, thereby providing a scientific and reasonable basis for the selection of a key node identification method.

[0075] In an optional embodiment, the target network is standardized to obtain a standard network, including:

[0076] S21, processing the N attribute sequences using an attribute statistical model to obtain T mean values and T variance values.

[0077] The expression of the attribute statistical model is:

[0078]

[0079] In the formula, μmm and σ mm are the m-th mean and the m-th variance, respectively, m is an integer from 1 to T; a mm,nn is the m-th attribute value of the n-th attribute sequence, n is an integer from 1 to N.

[0080] S22, using the comprehensive attribute calculation model, processing the N attribute sequences, T means and T variances to obtain N comprehensive attribute values.

[0081] The expression of the above comprehensive attribute calculation model is:

[0082]

[0083] In the formula, b nn is the n-th comprehensive attribute value.

[0084] S23, replacing the N attribute sequences in the target network with N comprehensive attribute values respectively to obtain a standard network.

[0085] It can be seen that through the above standardization processing, the influence of the difference in dimension and order of magnitude between different attribute values on the final result can be overcome, and the comprehensive attribute value is used to replace multiple attribute values to simplify the evaluation process and reduce the calculation amount.

[0086] In another optional embodiment, the processing of the above standard network and the above set of identification results to obtain a resilience sequence includes:

[0087] S31, performing descending order sorting processing on the set of identification results to obtain an identification result queue.

[0088] S32, initializing the initial resilience sequence as an empty sequence.

[0089] S33, performing resilience evaluation processing on the standard network to obtain the resilience value; inserting the resilience value at the end of the initial resilience sequence.

[0090] S34, performing de-queue updating on the identification result queue to obtain the current criticality value and the identification result queue after de-queue updating.

[0091] It should be noted that the de-queue updating is to set the current criticality value as the criticality value at the head of the identification result queue, and delete the criticality value at the head from the identification result queue to obtain the identification result queue after de-queue updating.

[0092] S35, based on the current criticality value, performing deletion updating on the standard network to obtain the standard network after deletion updating.

[0093] It should be noted that the above deletion update is to delete the nodes and edges corresponding to the current criticality value in the standard network.

[0094] S36, repeating S33-S35 until the length of the above identification result queue is 1; determining that the above resilience sequence is the initial resilience sequence.

[0095] It can be seen that by repeatedly deleting nodes with high criticality values in the standard network and evaluating the resilience of the standard network after each deletion, the resilience sequence obtained can reflect the dynamic changes in the resilience of the standard network under the condition of continuous loss of high criticality nodes.

[0096] In yet another optional embodiment, the resilience evaluation process of the above standard network to obtain the above resilience value comprises:

[0097] S331, performing a segmentation process on the above standard network to obtain N label vectors.

[0098] The length of the above label vector is P, and P is an integer greater than 1; the value of each component in the above label vector is 0 or 1.

[0099] It should be noted that P represents the number of sub-networks obtained by segmenting the standard network. The N label vectors are used to represent the conditions of the N nodes corresponding to the sub-networks respectively. When the p-th component of a certain label vector is 0 or 1, it means that the node corresponding to the label vector belongs to the p-th sub-network, where p is an integer from 1 to P. Further, there can be multiple components with a value of 1 in the label vector, indicating that the corresponding node belongs to multiple sub-networks at the same time.

[0100] Optionally, the segmentation process can use community detection algorithms such as Louvain algorithm and Girvan-Newman algorithm.

[0101] S332, based on the above standard network and N label vectors, constructing an inter-network adjacency matrix and P local feature matrices; the inter-network adjacency matrix is a P-order square matrix.

[0102] S333, performing eigenvalue decomposition on the above inter-network adjacency matrix and P local feature matrices respectively to obtain a first eigenvalue set and P second eigenvalue sets.

[0103] The first eigenvalue set includes P first eigenvalues; the second eigenvalue set includes a plurality of second eigenvalues.

[0104] It should be noted that after performing eigenvalue decomposition on the inter-network adjacency matrix, P first eigenvalues are obtained; after performing eigenvalue decomposition on each local feature matrix, a plurality of second eigenvalues are obtained, and the number of second eigenvalues is equal to the number of nodes in the corresponding sub-network.

[0105] S334, using the toughness evaluation model, processing the first feature value set and P second feature value sets to obtain the toughness value.

[0106] It can be seen that the standard network is segmented to obtain a plurality of sub-networks, and then the inter-network adjacency matrix and the local feature matrix corresponding to each sub-network are constructed, and further toughness evaluation is performed, so that the obtained toughness value not only focuses on the toughness change of the entire network, but also reflects the toughness change inside the sub-network and the connection toughness change between the sub-networks, which helps to observe the vulnerability of the network from a finer granularity.

[0107] In yet another optional embodiment, constructing the inter-network adjacency matrix and P local adjacency matrices based on the standard network and N label vectors includes:

[0108] S3321, based on N label vectors, performing pruning processing on the standard network to obtain P sub-networks.

[0109] The sub-network includes a plurality of nodes, a plurality of comprehensive attribute values, and a plurality of edges; the number of nodes of the sub-network is the same as the number of comprehensive attribute values.

[0110] It should be noted that the nodes in the sub-network correspond one-to-one to the comprehensive attribute values. Each comprehensive attribute value is used to describe the comprehensive communication capability of the corresponding node in the sub-network.

[0111] S3322, using the inter-network adjacency matrix construction model, processing N label vectors to obtain the inter-network adjacency matrix.

[0112] S3323, using the local feature matrix construction model, processing P sub-networks to obtain P local feature matrices.

[0113] In yet another optional embodiment, the expression of the inter-network adjacency matrix construction model is:

[0114] b i，j = δ(i, j) + [1 - δ(i, j)] x [1 - δ(<vec i , vec j >, 0)]

[0115] In the formula, b i,j is the element value of the i-th row and the j-th column of the inter-network adjacency matrix; vec i is a vector composed of all i-th elements of N label vectors; vec j is a vector composed of all j-th elements of N label vectors; i and j are integers from 1 to P; and <veci ,vec j > represents the inner product of vec i and vec j ; δ (·, ·) is the Kronecker function.

[0116] It can be seen that the element values of the inter-network adjacency matrix described above can only be 1 or 0, representing the existence or non-existence of a connection relationship between the sub-networks numbered as the corresponding row number and column number.

[0117] In yet another optional embodiment, the expression of the local feature matrix construction model described above is:

[0118]

[0119] In the formula, β ii(k),jj(k) is the element value of the ii(k)th row and jj(k)th column of the local feature matrix corresponding to the kth sub-network described above; and and are the ii(k)th and jj(k)th comprehensive attribute values of the kth sub-network described above, respectively; k is an integer from 1 to P; ii(k) and jj(k) are both integers from 1 to MM(k); and MM(k) is the number of nodes of the kth sub-network described above.

[0120] It should be noted that each element in the local feature matrix described above can reflect the association relationship of the comprehensive attribute values of the two nodes numbered as the corresponding row number and column number in the corresponding sub-network.

[0121] In yet another optional embodiment, the expression of the resilience evaluation model described above is:

[0122]

[0123] In the formula, V is the resilience value; p is an integer from 1 to P; N p is the number of second feature values of the pth second feature value set; γ p,q is the qth second feature value of the pth second feature value set; q is an integer from 1 to N p ; α is a preset proportion coefficient; λ t is the tth first feature value of the first feature value set; and t is an integer from 1 to N.

[0124] Optionally, the value range of α is [0, 1].

[0125] Preferably, the value of α is 1 / P.

[0126] In yet another optional embodiment, the trimming processing of the standard network based on the N label vectors described above to obtain P sub-networks comprises:

[0127] S33211, concatenate the N label vectors as column vectors to obtain a classification matrix.

[0128] S33212, split the classification matrix by rows to obtain P row vectors.

[0129] S33213, combine the serial numbers of the elements with a value of 1 in each row vector to obtain a serial number set corresponding to the row vector.

[0130] S33214, initialize the cycle number ix to 1.

[0131] S33215, delete the nodes with serial numbers belonging to the ixth serial number set and the corresponding comprehensive attribute values in the standard network to obtain the ixth initial subnetwork; delete the edges connected to nodes not belonging to the ixth initial subnetwork in the ixth initial subnetwork to obtain the ixth subnetwork.

[0132] S33216, increase the value of ix by 1.

[0133] S33217, repeat S33215-S33216 until ix is greater than P to obtain P subnetworks.

[0134] In another optional embodiment, the expression of the above comprehensive evaluation model is:

[0135]

[0136] In the formula, EV is the above comprehensive evaluation value; V kk+1 and V kk are the (kk+1)th and kkth above resilience values in the above resilience sequence respectively; kk is an integer from 1 to N.

[0137] It can be seen that the above comprehensive evaluation model performs a weighted average on the difference between adjacent resilience values, and the weight value linearly decreases with the increase of the serial number, so that the change of network resilience caused by the deletion of the node at the front is more influential to the comprehensive evaluation value, thereby making the node with a high criticality value more accurately recognized.

[0138] It can be seen that the network critical node recognition result evaluation method described in the embodiments of the present application can quantitatively evaluate the effect of critical node recognition from the perspective of network vulnerability, thereby providing a basis for the selection of critical node recognition methods.

[0139] Embodiment Two

[0140] Please refer to Figure 2 , Figure 2is a structural schematic diagram of a network key node identification result evaluation device disclosed by an embodiment of the present application. Wherein, Figure 2 The network key node identification result evaluation device described can be applied to network key node identification result evaluation, such as quantitative evaluation of key node identification results in a command network, a sensor network and a satellite communication network, and the embodiment of the present application is not limited. For example, Figure 2 As shown in the figure, the device can include a result obtaining module 201, a standardization module 202, a resilience evaluation module 203 and a comprehensive evaluation module 204.

[0141] The result obtaining module 201 described above is used to obtain a target network and an identification result set.

[0142] The standardization module 202 described above is used to perform standardization processing on the target network to obtain a standard network.

[0143] The resilience evaluation module 203 described above is used to perform processing on the standard network and the identification result set to obtain a resilience sequence.

[0144] The comprehensive evaluation module 204 described above is used to perform processing on the resilience sequence by using a comprehensive evaluation model to obtain a comprehensive evaluation value.

[0145] It can be seen that the network key node identification result evaluation device described in the embodiment of the present application can.

[0146] Embodiment three

[0147] Please refer to Figure 3 , Figure 3 is a structural schematic diagram of another network key node identification result evaluation device disclosed by an embodiment of the present application. Wherein, Figure 3 The network key node identification result evaluation device described can be applied to network key node identification result evaluation, such as quantitative evaluation of key node identification results in a command network, a sensor network and a satellite communication network, and the embodiment of the present application is not limited. For example, Figure 3 As shown in the figure, the network key node identification result evaluation device can include the following parts:

[0148] A memory 301 storing executable program codes;

[0149] A processor 302 coupled with the memory 301;

[0150] The processor 302 calls the executable program codes stored in the memory 301, which is used to execute the steps in the network key node identification result evaluation method described in embodiment one.

[0151] Embodiment four

[0152] The embodiment of the present application discloses a computer readable storage medium, which stores a computer program for electronic data exchange, wherein the computer program causes a computer to execute steps in the network key node identification result evaluation method described in the embodiment one.

[0153] The above described device embodiments are only illustrative, wherein the modules illustrated as separate components can or can not be physically separated, and the components illustrated as modules can or can not be physical modules, i.e., can be located in one place or distributed to multiple network modules. Part or all of the modules can be selected to achieve the purpose of the embodiment scheme according to actual needs. Those skilled in the art can understand and implement without creative labor.

[0154] Through the specific description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be realized by means of software and the necessary general hardware platform, and of course, can also be realized by hardware. Based on such understanding, the above technical solutions can be embodied in the form of software products, and the computer software product can be stored in a computer readable storage medium, including a read-only memory (ROM), a random access memory (RAM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), a one-time programmable read-only memory (OTPROM), an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disk storage, a magnetic disk storage, a magnetic tape storage, or any other computer readable medium that can be used to carry or store data.

[0155] It should be finally pointed out that: the network key node recognition result evaluation method and device disclosed by the embodiments of the present application are only the preferred embodiments of the present application, and are used to illustrate the technical solutions of the present application, but not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that; it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for part of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for evaluating a result of network key node identification, characterized by, The method comprises the following steps: S1, obtaining a target network and a set of identification results; The target network comprises N nodes, N attribute sequences and M edges; The attribute sequence comprises T attribute values; N, M and T are all integers greater than 1; The set of identification results comprises N key values corresponding to the nodes; S2, performing standardization processing on the target network to obtain a standard network; the standard network comprises N nodes, N comprehensive attribute values and M edges; S3, processing the standard network and the set of identification results to obtain a resilience sequence; the resilience sequence comprises N resilience values; S4, processing the resilience sequence by using a comprehensive evaluation model to obtain a comprehensive evaluation value; The expression of the comprehensive evaluation model is: In the formula, EV is the comprehensive evaluation value; V kk+1 and V kk are the (kk+1)th and the kkth toughness values in the toughness sequence, respectively; kk is an integer from 1 to N.

2. The network key node identification result evaluation method according to claim 1, characterized by, The processing of the standard network and the set of identification results to obtain a resilience sequence comprises the following steps: S31, performing descending order sorting processing on the set of identification results to obtain an identification result queue; S32, initializing an initial resilience sequence as an empty sequence; S33, performing resilience evaluation processing on the standard network to obtain the resilience value; and inserting the resilience value into the end of the initial resilience sequence; S34, performing de-queue updating on the identification result queue to obtain a current key value and the identification result queue after de-queue updating; S35, based on the current key value, performing deletion updating on the standard network to obtain the standard network after deletion updating; S36, repeating S33-S35 until the length of the identification result queue is 1; and determining that the resilience sequence is the initial resilience sequence.

3. The network key node identification result evaluation method according to claim 2, characterized by, The resilience evaluation processing on the standard network to obtain the resilience value comprises the following steps: S331, performing segmentation processing on the standard network to obtain N label vectors; The length of the label vector is P, and P is an integer greater than 1; the value of each component in the label vector is 0 or 1; S332, based on the standard network and the N label vectors, constructing an inter-network adjacency matrix and P local feature matrices; the inter-network adjacency matrix is a P-order square matrix; S333, performing eigenvalue decomposition processing on the inter-network adjacency matrix and the P local feature matrices respectively to obtain a first eigenvalue set and P second eigenvalue sets; The first eigenvalue set comprises P first eigenvalues; and each second eigenvalue set comprises several second eigenvalues; S334, processing the first eigenvalue set and the P second eigenvalue sets by using a resilience evaluation model to obtain the resilience value.

4. The network key node identification result evaluation method according to claim 3, characterized by, The construction of the inter-network adjacency matrix and the P local feature matrices based on the standard network and the N label vectors comprises the following steps: S3321, performing clipping processing on the standard network based on the N label vectors to obtain P sub-networks; The sub-network comprises several nodes, several comprehensive attribute values and several edges; the number of nodes of the sub-network is the same as the number of comprehensive attribute values; S3322, processing the N label vectors by using an inter-network adjacency matrix construction model to obtain an inter-network adjacency matrix; S3323, constructing a model by using the local feature matrix, processing the P sub-networks to obtain P local feature matrices.

5. The network key node identification result evaluation method according to claim 4, characterized by, An expression of the inter-network adjacency matrix construction model is: b i,j = δ(i, j) + [1 - δ(i, j)] x [1 - δ(<vec i , vec j , 0)] where b i,j is the element value of the ith row and jth column of the inter-network adjacency matrix; vec i is the vector of all ith elements of the N label vectors; vec j is the vector of all jth elements of the N label vectors; i and j are integers from 1 to P; <vec i ,vec j > denotes the inner product of vec i and vec j ; δ(*,*) is the Kronecker function.

6. The network key node identification result evaluation method according to claim 3, characterized by, An expression of the toughness evaluation model is: wherein V is the toughness value; p is an integer from 1 to P; N p is the number of the second characteristic values of the pth second set of characteristic values. gamma p,q is the qth second characteristic value of the pth second characteristic value set; q is 1 to N p is an integer; alpha is a preset proportional coefficient; lambda t is the tth first characteristic value of the first characteristic value set; t is an integer from 1 to N.

7. A network key node identification result evaluation apparatus characterized by comprising: a network key node identification result evaluation unit configured to evaluate a network key node identification result. The device comprises a result acquisition module, a standardization module, a toughness evaluation module and a comprehensive evaluation module. The result acquisition module is configured to acquire a target network and a set of identification results. The standardization module is configured to perform standardization processing on the target network to obtain a standard network. The toughness evaluation module is configured to process the standard network and the set of identification results to obtain a toughness sequence. The comprehensive evaluation module is configured to process the toughness sequence by using a comprehensive evaluation model to obtain a comprehensive evaluation value. An expression of the comprehensive evaluation model is: In the formula, EV is the comprehensive evaluation value; V kk+1 and V kk are the (kk+1)th and kkth toughness values in the toughness sequence, respectively; kk is an integer from 1 to N.

8. A network key node identification result evaluation apparatus characterized by comprising: The device comprises: a memory storing executable program codes; a processor coupled with the memory; The processor invokes the executable program codes stored in the memory to execute the network key node identification result evaluation method according to any one of claims 1-6.

9. A computer storable medium, characterized by The computer storage medium stores computer instructions, which are invoked to execute the network key node identification result evaluation method according to any one of claims 1-6.

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