CN106506192A - A kind of method and apparatus of identification network key node - Google Patents
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Abstract
The invention discloses a kind of method and apparatus of identification network key node, the method includes：Multiple measurement indexs are selected from degree index, subgraph index, betweenness index, characteristic vector index, approximate characteristic vector index, nearness index, knot removal loss index, stream betweenness index, approximate stream betweenness index, accumulative nomination index；According to the multiple measurement indexs for selecting, comprehensive evaluation value is calculated to each network node in all-network node；One threshold value is set, if the comprehensive evaluation value of some network node is more than the threshold value, using the network node as key node；Or the ratio that key node number accounts for network node sum is set, all-network node is sorted from big to small according to comprehensive evaluation value, network node within the ratio will be come as key node.This programme is chosen multiple measurement indexs and is fused into single comprehensive evaluation value, as the foundation for weighing network node importance, improves the accuracy of identification network key node.
Description
Technical Field
The invention relates to the technical field of communication, in particular to a method and a device for identifying network key nodes.
Background
From the network structure characteristic, most battlefield wireless communication networks belong to scale-free networks. The typical structural characteristics are as follows: most nodes in the network have only a few connections, and only a few nodes have a large number of connections with other nodes. Such networks are very resistant to random interference. But it may be overwhelmed in the face of deliberate interference directed at critical nodes.
The analysis of the network key nodes is the basis of the implementation of 'point-hitting wars' and is the premise of network selective interference. By means of the key nodes of the interference network, the interference effect can be doubled with half the effort, and the fighting efficiency of network countermeasure equipment of the same party can be fully exerted. Therefore, performing analysis of critical nodes on a battlefield wireless communication network becomes an important part of network analysis.
The network key node is a node playing an important role in network communication, and is characterized by the centrality of the network node in order to characterize the importance of the network node in the network, and the betweenness index is the most effective index for characterizing the centrality of the wireless communication network node in a battlefield in known measurement indexes. At present, a single measure index is usually used for identifying key nodes, but the importance of network nodes is influenced by a plurality of measure indexes at the same time, and the accuracy of identifying the key nodes by only using a certain single measure index is insufficient.
Disclosure of Invention
The invention provides a method and a device for identifying key nodes of a network, which aim to solve the problem of insufficient accuracy of identifying the key nodes by using a certain single measure index.
According to one aspect of the invention, the invention provides a method for identifying a network key node, comprising:
selecting a plurality of measure indexes from the degree index, the subgraph index, the betweenness index, the feature vector index, the approximate feature vector index, the proximity index, the node deletion loss index, the flow betweenness index, the approximate flow betweenness index and the accumulated nomination index;
calculating a comprehensive evaluation value for each network node in all the network nodes according to the selected multiple measure indexes;
setting a threshold, and if the comprehensive evaluation value of a certain network node is greater than the threshold, taking the network node as a key node; or setting the proportion of the number of the key nodes to the total number of the network nodes, sequencing all the network nodes from large to small according to the comprehensive evaluation value, and taking the network nodes arranged in the proportion as the key nodes.
According to another aspect of the present invention, the present invention provides an apparatus for identifying a key node in a network, comprising:
a measure index selection unit for receiving a plurality of measure indexes selected from a degree index, a sub-graph index, an betweenness index, a feature vector index, an approximate feature vector index, a proximity index, a node deletion loss index, a flow betweenness index, an approximate flow betweenness index, and a cumulative nomination index;
a comprehensive evaluation value calculation unit, configured to calculate a comprehensive evaluation value for each network node in all network nodes according to the multiple measure indexes received by the measure index selection unit;
the key node judging unit is used for setting a threshold value, and if the comprehensive evaluation value of a certain network node is greater than the threshold value, the network node is used as a key node; or the method is used for setting the proportion of the number of the key nodes in the total number of the network nodes, sequencing all the network nodes from large to small according to the comprehensive evaluation value, and taking the network nodes in the proportion as the key nodes.
The invention has the beneficial effects that: the embodiment of the invention selects a plurality of measurement indexes, and for each network node, the selected plurality of measurement indexes are fused into a single comprehensive evaluation value which is used as a basis for measuring the importance of the network node.
Drawings
FIG. 1 is a flow chart of a method of identifying network key nodes provided by one embodiment of the present invention;
FIG. 2 is a functional block diagram of an apparatus for identifying network key nodes according to an embodiment of the present invention;
fig. 3 is a schematic diagram of an OPNET scenario deployment scenario in an embodiment of the invention;
FIG. 4 is a schematic view of a parameter setting interface of an apparatus for identifying key nodes of a network in accordance with an embodiment of the present invention;
FIG. 5 is a diagram of the results of identifying key nodes of a network using a manual weighted synthesis method in accordance with one embodiment of the present invention;
FIG. 6 is a diagram illustrating the results of identifying key nodes in a network using entropy weighted gray correlation analysis in accordance with an embodiment of the present invention.
Detailed Description
The design concept of the invention is as follows: the importance of the network node is influenced by a plurality of measurement indexes at the same time, and the key node is difficult to accurately identify only by using a certain single measurement index. The invention adopts an analytic hierarchy process to select a plurality of measurement indexes to evaluate the importance of the network node, and various measurement indexes are not as important to the network node, so the weight of the evaluation index can be reasonably determined by combining a fuzzy comprehensive evaluation method, a single evaluation index value based on the synthesis of the plurality of measurement indexes is obtained after the plurality of measurement indexes are synthesized, the fuzziness of the evaluation result is quantified, the network key node is identified, and the identification accuracy can be improved.
Example one
Fig. 1 is a flowchart of a method for identifying a network key node according to an embodiment of the present invention, and as shown in fig. 1, the method for identifying a network key node according to the embodiment includes:
step S110: selecting a plurality of measure indexes from the degree index, the subgraph index, the betweenness index, the feature vector index, the approximate feature vector index, the proximity index, the node deletion loss index, the flow betweenness index, the approximate flow betweenness index and the accumulated nomination index.
Step S120: and calculating a comprehensive evaluation value for each network node in all the network nodes according to the selected multiple measure indexes.
Step S130: setting a threshold, and if the comprehensive evaluation value of a certain network node is greater than the threshold, taking the network node as a key node; or setting the proportion of the number of the key nodes to the total number of the network nodes, sequencing all the network nodes from large to small according to the comprehensive evaluation value, and taking the network nodes arranged in the proportion as the key nodes.
In this embodiment, a plurality of measurement indexes are selected, and for each network node, the selected plurality of measurement indexes are fused into a single comprehensive evaluation value, which is used as a basis for measuring the importance of the network node.
Example two
In the embodiment, a manual weighting synthesis method is adopted to calculate the comprehensive evaluation value of each network node, that is, each measure index is weighted according to a manually input weight parameter, and then the weighted sum of the measure indexes is calculated to be used as a final comprehensive evaluation value.
Step i): for the case of m measure indices with n network nodes, an index matrix is established:
wherein x isijIs the jth measure index of the ith network node.
Step ii): normalizing the matrix to obtain a normalized matrix:
wherein,xmax(j) and xmin(j) The optimal and worst values of the jth metric index of all network nodes are j ═ 1,2, …, m.
Step iii): setting weight values for m measurement indexes to obtain weight value vector W ═ (W)1,w2,...,wm) According to the formulaThe comprehensive evaluation value F of each network node can be calculatedi。
EXAMPLE III
Since the manual weighting synthesis method adopted in the second embodiment has a certain subjective randomness, and the distribution characteristics of each measure index cannot be accurately reflected in some cases, in order to automatically calculate the weight occupied by each index, the second embodiment calculates the comprehensive evaluation value of each network node by using the entropy weighting synthesis method.
Entropy is a quantity that represents the state of a matter system, indicating the extent to which that state may occur. The smaller the information entropy of a certain measure is, the larger the variation degree of the measure is, and the larger the amount of the provided information is, the larger the weight is; conversely, if the information entropy of a certain measure index is larger, the amount of information provided is smaller, and the weight is correspondingly smaller. Therefore, the variation degree of each measure index can be considered, the weight of each measure index is determined according to the entropy value obtained by calculation, then each measure index is weighted, and finally, a relatively comprehensive and objective evaluation result is obtained.
Step i): for a problem with n network nodes, m measure indices, an initial matrix is established:
wherein x isijIs the jth measure index of the ith network node.
And ii), carrying out normalization processing on the initial matrix to obtain a normalized matrix:
wherein,xmax(j) and xmin(j) The optimal and worst values of the jth measure index of all network nodes are j ═ 1,2, 3.
Step iii) according to the formula:
calculating the entropy S of the jth measure indexjWherein
if PijWhen 1, then PijlnPij0, which is contrary to the information disorder of entropy in information theory, so P is neededijThe definition of (1) is corrected as follows:
step iv) according to the formula:
the entropy weight w of the jth measure index can be determinedjSo as to further obtain the entropy vector W of m measurement indexes (W ═ W)1,w2,...,wm)。
Step v) according to the formula:
that is, the comprehensive evaluation value F of each network node can be calculatedi。
Example four
In the embodiment, a grey correlation analysis method is adopted to calculate the comprehensive evaluation value of each network node, and the grey correlation analysis method is expected to find the numerical relationship between the factors according to the difference or similarity of the development trends of the factors through a special method, so that the method is an effective method for measuring the correlation degree. The method quantifies the condition of system development change, is very suitable for dynamic process analysis, and accords with the actual change of the complex network moment.
Step i): for a problem with n network nodes, m metric indexes, the following is the criticality metric vector for the ith network node:
Xi＝(xi1,xi2,...,xim)
wherein x isijIs the jth measure index of the ith network node, and i is more than or equal to 1 and less than or equal to n.
Step ii): comprehensively comparing the measure indexes of all network nodes to obtain a reference vector of the measure indexes:
Yi＝(y1,y2,...,ym)
wherein, ykIs the optimal value of the k-th measure index for all network nodes.
Step iii): because the dimensions of each measurement index of the network nodes are not necessarily the same and some numerical values have very different magnitudes, they are subjected to non-dimensionalization processing, in this embodiment, an "averaging method" is adopted to perform a dimensionless processing on the critical measurement vector XiAnd a reference vector YiObtaining a dimensionless critical measure vector after dimensionless processing:
and a non-dimensionalized reference vector:
wherein,and k is more than or equal to 1 and less than or equal to m.
Step iv): establishing a difference matrix:
finding the maximum D in the matrixmaxAnd a minimum value Dmin。
Step v): according to the formula:
calculating the correlation coefficient r of each measure index of each network nodeikWherein r isikThe method is characterized in that rho is a resolution coefficient of a k-th measurement index of an ith network node, rho is more than 0 and less than 1, and preferably, rho takes a value of 0.5.
Step vi): according to the formula
Calculating the comprehensive evaluation value R of each network nodeiComparing the comprehensive evaluation value R of each network nodeiTo obtain the key sequence of the network nodes and the comprehensive evaluation value RiThe larger the node, the more important the node. A threshold value can be set, and the comprehensive evaluation value RiNetwork nodes above this threshold are critical nodes.
EXAMPLE five
In this embodiment, the comprehensive evaluation value of each network node is calculated by combining the entropy weight weighted comprehensive method and the gray correlation analysis method, so as to further improve the accuracy.
Step i): for a problem with n network nodes, m metric indexes, the following is the criticality metric vector for the ith network node:
Xi＝(xi1,xi2,...,xim)
wherein x isijIs the jth measure index of the ith network node, and i is more than or equal to 1 and less than or equal to n.
Step ii): comprehensively comparing the measure indexes of all network nodes to obtain a reference vector of the measure indexes:
Yi＝(y1,y2,...,ym)
wherein, ykIs the optimal value of the k-th measure index for all network nodes.
Step iii): vector X of measure of criticalityiAnd a reference vector YiObtaining a dimensionless critical measure vector after dimensionless processing:
and a non-dimensionalized reference vector:
wherein,and k is more than or equal to 1 and less than or equal to m.
Step iv): establishing a difference matrix:
finding the maximum D in the matrixmaxAnd a minimum value Dmin。
Step v): according to the formula:
calculating the correlation coefficient r of each measure index of each network nodeikWherein r isikFor the kth network nodeAnd rho is a resolution coefficient, rho is more than 0 and less than 1, and preferably, rho takes a value of 0.5.
Step vi): establishing an index matrix:and carrying out normalization processing to obtain a normalization matrix:wherein,xmax(j) and xmin(j) The optimal value and the worst value of the jth measure index of all the network nodes are respectively.
Step vii): according toDetermining the entropy S of the jth measure indexjWhereinand according toDetermining the entropy weight w of the jth measure indexjObtaining the entropy vector W of m measurement indexes as (W)1,w2,...,wm)。
Step viii): according to the formula:
the comprehensive evaluation value R of each network node can be calculatedi. It is also possible to compare the overall evaluation value R of each network nodeiThe key sequence of the network nodes is obtained, and the comprehensive evaluation value R is obtainediThe larger the node, the more important the node. Or alternatively a threshold value may be set as well,the comprehensive evaluation value RiNetwork nodes that are greater than the threshold value act as key nodes.
EXAMPLE six
Fig. 2 is a functional block diagram of an apparatus for identifying a network key node according to an embodiment of the present invention, and as shown in fig. 2, the apparatus for identifying a network key node according to the embodiment includes: a measure index selection unit 210, a comprehensive evaluation value calculation unit 220, and a key node judgment unit 230.
The measure index selection unit 210 receives a plurality of measure indexes selected from a degree index, a sub-graph index, an betweenness index, a feature vector index, an approximate feature vector index, a proximity index, a node deletion loss index, a flow betweenness index, an approximate flow betweenness index, and a cumulative nomination index.
The comprehensive evaluation value calculation unit 220 calculates a comprehensive evaluation value for each of all network nodes from the plurality of measure indexes received by the measure index selection unit 210.
The key node determination unit 230 sets a threshold, and if the comprehensive evaluation value of a certain network node is greater than the threshold, the network node is used as a key node; or the key node judging unit 230 sets the proportion of the number of key nodes to the total number of network nodes, sorts all the network nodes from large to small according to the comprehensive evaluation value, and takes the network nodes ranked within the proportion as the key nodes.
In a preferred embodiment, the comprehensive evaluation value calculation unit 220 is specifically configured to:
for the case of m measure indices with n network nodes, an index matrix is established:
wherein x isijIs the jth measure index of the ith network node.
Normalizing the matrix to obtain a normalized matrix:
wherein,xmax(j) and xmin(j) The optimal and worst values of the jth metric index of all network nodes are j ═ 1,2, …, m.
Setting weight values for m measurement indexes to obtain weight value vector W ═ (W)1,w2,...,wm) According to the formulaCalculating the comprehensive evaluation value F of each network nodei。
In another preferred embodiment, the comprehensive evaluation value calculation unit 220 is specifically configured to:
for a problem with n network nodes, m measure indices, an initial matrix is established:
wherein x isijIs the jth measure index of the ith network node.
Normalizing the initial matrix to obtain a normalized matrix:
wherein,xmax(j) and xmin(j) The optimal and worst values of the jth measure index of all network nodes are j ═ 1,2, 3.
According to the formula:
calculating the entropy S of the jth measure indexjWherein
according to the formula:
the entropy weight w of the jth measure index can be determinedjSo as to further obtain the entropy vector W of m measurement indexes (W ═ W)1,w2,...,wm)。
According to the formula:
calculating the comprehensive evaluation value F of each network nodei。
In yet another preferred embodiment, the comprehensive evaluation value calculation unit 220 is specifically configured to:
for a problem with n network nodes, m metric indexes, the following is the criticality metric vector for the ith network node:
Xi＝(xi1,xi2,...,xim)
wherein x isijIs the jth measure index of the ith network node, and i is more than or equal to 1 and less than or equal to n.
Comprehensively comparing the measure indexes of all network nodes to obtain a reference vector of the measure indexes:
Yi＝(y1,y2,...,ym)
wherein, ykIs the optimal value of the k-th measure index for all network nodes.
Vector X of measure of criticalityiAnd a reference vector YiObtaining a dimensionless critical measure vector after dimensionless processing:
and a non-dimensionalized reference vector:
wherein,and k is more than or equal to 1 and less than or equal to m.
Establishing a difference matrix:
finding the maximum D in the matrixmaxAnd a minimum value Dmin。
According to the formula:
calculating the correlation coefficient r of each measure index of each network nodeikWherein r isikThe method is characterized in that rho is a resolution coefficient of a k-th measurement index of an ith network node, rho is more than 0 and less than 1, and preferably, rho takes a value of 0.5.
According to the formula
Calculating the comprehensive evaluation value R of each network nodei。
In still another preferred implementation, the comprehensive evaluation value calculation unit 220 is specifically configured to:
for a problem with n network nodes, m metric indexes, the following is the criticality metric vector for the ith network node:
Xi＝(xi1,xi2,...,xim)
wherein x isijIs the jth measure index of the ith network node, and i is more than or equal to 1 and less than or equal to n.
Comprehensively comparing the measure indexes of all network nodes to obtain a reference vector of the measure indexes:
Yi＝(y1,y2,...,ym)
wherein, ykIs the optimal value of the k-th measure index for all network nodes.
Vector X of measure of criticalityiAnd a reference vector YiObtaining a dimensionless critical measure vector after dimensionless processing:
and a non-dimensionalized reference vector:
wherein,and k is more than or equal to 1 and less than or equal to m.
Establishing a difference matrix:
finding the maximum D in the matrixmaxAnd a minimum value Dmin。
According to the formula:
calculating the correlation coefficient r of each measure index of each network nodeikWherein r isikThe method is characterized in that rho is a resolution coefficient of a k-th measurement index of an ith network node, rho is more than 0 and less than 1, and preferably, rho takes a value of 0.5.
Establishing an index matrix:and carrying out normalization processing to obtain a normalization matrix:wherein,xmax(j) and xmin(j) The optimal value and the worst value of the jth measure index of all the network nodes are respectively.
According toDetermining the entropy S of the jth measure indexjWhereinand according toDetermining the entropy weight w of the jth measure indexjObtaining the entropy vector W of m measurement indexes as (W)1,w2,...,wm)。
According to the formula:
calculating the comprehensive evaluation value R of each network nodei。
Fig. 3 is a schematic diagram of an OPNET scenario deployment scenario in an embodiment of the present invention, and as shown in fig. 3, a wireless network communication scenario with 34 nodes is designed in this embodiment, and the scenario deployment is within a range of 200km × 100km, and includes three sentry stations plus one NCS node, four sensor nodes, twenty ordinary forwarding nodes, one command center node, and five attack platform nodes.
Before identifying the network key node, necessary settings need to be performed, such as selecting multiple required measurement indexes from ten given measurement indexes, selecting a method for calculating a comprehensive evaluation value, setting a threshold or a proportion for judging the key node, and further setting a weight of each measurement index if a manual weighted integration method is used, as shown in fig. 4. Fig. 5 is a result diagram of identifying a network key node by using a manual weighted synthesis method in an embodiment of the present invention, fig. 6 is a result diagram of identifying a network key node by using an entropy weighted gray correlation analysis method in an embodiment of the present invention, and compared with fig. 5 and fig. 6, the selected measure indexes are all 5: the degree index, the proximity index, the node deletion loss index, the betweenness index and the accumulated nomination, the proportion of the key nodes is set to be 10 percent, and the weight value set for each measure index is 0.2 when a manual weighting synthesis method is adopted. As shown in fig. 5, when the network key nodes are identified by using the manual weighted synthesis method, the identified network key nodes are 14, 10, 24 and 26; as shown in fig. 6, when the entropy weighted gray correlation analysis method is used to automatically calculate the weighting factors to identify the key points of the network, the identified key nodes of the network are 14, 10, 9, and 24. The entropy weight gray correlation analysis method is accurate.
While the foregoing is directed to embodiments of the present invention, other modifications and variations of the present invention may be devised by those skilled in the art in light of the above teachings. It should be understood by those skilled in the art that the foregoing detailed description is for the purpose of better explaining the present invention, and the scope of the present invention should be determined by the scope of the appended claims.
Claims (10)
1. A method of identifying a network critical node, comprising:
selecting a plurality of measure indexes from the degree index, the subgraph index, the betweenness index, the feature vector index, the approximate feature vector index, the proximity index, the node deletion loss index, the flow betweenness index, the approximate flow betweenness index and the accumulated nomination index;
calculating a comprehensive evaluation value for each network node in all the network nodes according to the selected multiple measure indexes;
setting a threshold, and if the comprehensive evaluation value of a certain network node is greater than the threshold, taking the network node as a key node; or setting the proportion of the number of the key nodes to the total number of the network nodes, sequencing all the network nodes from large to small according to the comprehensive evaluation value, and taking the network nodes arranged in the proportion as the key nodes.
2. The method according to claim 1, wherein the calculating a composite evaluation value for each of all network nodes according to the selected plurality of measure indexes comprises:
if the total number of all network nodes is n and the number of the selected measure indexes is m, establishing an index matrix according to the m measure indexes of the n network nodes
Carrying out normalization processing to obtain a normalization matrixWherein,xmax(j) and xmin(j) Respectively obtaining the optimal value and the worst value of the jth measure index of all the network nodes;
setting the weight vector W of m measurement indexes as (W)1,w2,...,wm)
According toCalculating the comprehensive evaluation value F of each network nodei。
3. The method according to claim 1, wherein the calculating a composite evaluation value for each of all network nodes according to the selected plurality of measure indexes comprises:
if the total number of all network nodes is n and the number of the selected measure indexes is m, establishing an index matrix according to the m measure indexes of the n network nodes:
carrying out normalization processing to obtain a normalization matrix:wherein,xmax(j) and xmin(j) Respectively obtaining the optimal value and the worst value of the jth measure index of all the network nodes;
according toDetermining the entropy S of the jth measure indexjWherein
according toDetermining the entropy weight w of the jth measure indexjObtaining the entropy vector W of m measurement indexes as (W)1,w2,...,wm)，
According toCalculating the comprehensive evaluation value F of each network nodei。
4. The method according to claim 1, wherein the calculating a composite evaluation value for each of all network nodes according to the selected plurality of measure indexes comprises:
if the total number of all network nodes is n and the number of the selected measurement indexes is m, determining the critical measurement vector X of each network node according to the m measurement indexes of the n network nodesi＝(xi1,xi2,...,xim)；
Comprehensively comparing the measure indexes of all network nodes to obtain a reference vector Y of the measure indexesi＝(y1,y2,...,ym) Wherein, ykIs the optimal value of the kth measure index of all network nodes;
for the criticality measure vector XiAnd the reference vector YiCarrying out dimensionless treatment to obtain dimensionless critical measure vectorAnd a non-dimensionalized reference vectorWherein,
establishing a matrix of differences
According toCalculating the correlation coefficient r of each measure index of each network nodeikWherein D ismaxAnd DminThe maximum value and the minimum value in the difference matrix delta are provided, rho is a resolution coefficient, and rho is more than 0 and less than 1;
according toCalculating the comprehensive evaluation value R of each network nodei。
5. The method according to claim 1, wherein the calculating a composite evaluation value for each of all network nodes according to the selected plurality of measure indexes comprises:
if the total number of all network nodes is n and the number of the selected measurement indexes is m, determining the critical measurement vector X of each network node according to the m measurement indexes of the n network nodesi＝(xi1,xi2,...,xim)；
Comprehensively comparing the measure indexes of all network nodes to obtain a reference vector Y of the measure indexesi＝(y1,y2,...,ym) Wherein, ykIs the optimal value of the kth measure index of all network nodes;
for the criticality measure vector XiAnd the reference vector YiCarrying out dimensionless treatment to obtain dimensionless critical measure vectorAnd a non-dimensionalized reference vectorWherein,
establishing a matrix of differences
According toCalculating the correlation coefficient r of each measure index of each network nodeikWherein D ismaxAnd DminThe maximum value and the minimum value in the difference matrix delta are provided, rho is a resolution coefficient, and rho is more than 0 and less than 1;
establishing an indexMatrix:and carrying out normalization processing to obtain a normalization matrix:wherein,xmax(j) and xmin(j) Respectively obtaining the optimal value and the worst value of the jth measure index of all the network nodes;
according toDetermining the entropy S of the jth measure indexjWhereinaccording toDetermining the entropy weight w of the jth measure indexjObtaining the entropy vector W of m measurement indexes as (W)1,w2,...,wm)；
According toCalculating the comprehensive evaluation value R of each network nodei。
6. An apparatus for identifying a key node in a network, comprising:
a measure index selection unit for receiving a plurality of measure indexes selected from a degree index, a sub-graph index, an betweenness index, a feature vector index, an approximate feature vector index, a proximity index, a node deletion loss index, a flow betweenness index, an approximate flow betweenness index, and a cumulative nomination index;
a comprehensive evaluation value calculation unit, configured to calculate a comprehensive evaluation value for each network node in all network nodes according to the multiple measure indexes received by the measure index selection unit;
the key node judging unit is used for setting a threshold value, and if the comprehensive evaluation value of a certain network node is greater than the threshold value, the network node is used as a key node; or the method is used for setting the proportion of the number of the key nodes in the total number of the network nodes, sequencing all the network nodes from large to small according to the comprehensive evaluation value, and taking the network nodes in the proportion as the key nodes.
7. The apparatus according to claim 6, wherein the comprehensive evaluation value calculation unit is specifically configured to:
establishing an index matrix according to m measurement indexes of n network nodes:and carrying out normalization processing to obtain a normalization matrixWherein,xmax(j) and xmin(j) Respectively obtaining the optimal value and the worst value of the jth measure index of all the network nodes;
setting the weight vector W of m measurement indexes as (W)1,w2,...,wm) According toCalculating the comprehensive evaluation value F of each network nodei。
8. The apparatus according to claim 6, wherein the comprehensive evaluation value calculation unit is specifically configured to:
according to m measurement indexes of n network nodesEstablishing an index matrixAnd carrying out normalization processing to obtain a normalization matrix:wherein,xmax(j) and xmin(j) Respectively obtaining the optimal value and the worst value of the jth measure index of all the network nodes;
according toDetermining the entropy S of the jth measure indexjWherein
according toDetermining the entropy weight w of the jth measure indexjObtaining the entropy vector W of m measurement indexes as (W)1,w2,...,wm) According toCalculating the comprehensive evaluation value F of each network nodei。
9. The apparatus according to claim 6, wherein the comprehensive evaluation value calculation unit is specifically configured to:
determining a criticality measure vector X of each network node according to m measure indexes of n network nodesi＝(xi1,xi2,...,xim)；
Comprehensively comparing measure indexes of all network nodes to obtain measure indexesReference vector Yi＝(y1,y2,...,ym) Wherein, ykIs the optimal value of the kth measure index of all network nodes;
for the criticality measure vector XiAnd the reference vector YiCarrying out non-quantitative tempering treatment to obtain a non-dimensionalized critical measurement vectorAnd a reference vector obtained after dimensionlessWherein,
establishing a matrix of differences
According toCalculating the correlation coefficient r of each measure index of each network nodeikWherein D ismaxAnd DminThe maximum value and the minimum value in the difference matrix delta are provided, rho is a resolution coefficient, and rho is more than 0 and less than 1;
according toCalculating the comprehensive evaluation value R of each network nodei。
10. The apparatus according to claim 6, wherein the comprehensive evaluation value calculation unit is specifically configured to:
determining a criticality measure vector X of each network node according to m measure indexes of n network nodesi＝(xi1,xi2,...,xim)；
Comprehensively comparing the measure indexes of all network nodes to obtain a reference vector Y of the measure indexesi＝(y1,y2,...,ym) Wherein, ykIs the optimal value of the kth measure index of all network nodes;
for the criticality measure vector XiAnd the reference vector YiCarrying out non-quantitative tempering treatment to obtain a non-dimensionalized critical measurement vectorAnd a reference vector obtained after dimensionlessWherein,
establishing a matrix of differences
According toCalculating the correlation coefficient r of each measure index of each network nodeikWherein D ismaxAnd DminThe maximum value and the minimum value in the difference matrix delta are provided, rho is a resolution coefficient, and rho is more than 0 and less than 1;
establishing an index matrix:and carrying out normalization processing to obtain a normalization matrix:wherein,xmax(j) and xmin(j) Respectively obtaining the optimal value and the worst value of the jth measure index of all the network nodes;
according toDetermining the entropy S of the jth measure indexjWhereinaccording toDetermining the entropy weight w of the jth measure indexjObtaining the entropy vector W of m measurement indexes as (W)1,w2,...,wm)；
According toCalculating the comprehensive evaluation value R of each network nodei。
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