CN106533778A - Method for identifying key node of command and control network based on hierarchical flow betweenness - Google Patents
Method for identifying key node of command and control network based on hierarchical flow betweenness Download PDFInfo
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Abstract
The invention provides a method for identifying a key node of a command and control network based on hierarchical flow betweenness. The method comprises the following specific steps: S1, establishing a command and control network model; S2, calculating the hierarchical flow betweenness of nodes in the command and control network; and S3, distinguishing the criticality of the nodes according to the hierarchical flow betweenness of the nodes. By adoption of the method, the algorithm complexity can be reduced, the precision of identifying the key node is improved, and the method is better applicable to the demand of identifying the key node of the command and control network.
Description
Technical field
The present invention relates to a kind of accuse network key node recognition methods, specifically a kind of finger based on level stream betweenness
Control network key node recognition methods.
Background technology
Charge network under Information Condition connects to form various non-linear logistic relations, network by physical communication network
Structure has the Complex Networks Features such as non-linear, level and adaptability.In the information age, command and control system is to form work
The core of war ability, it has also become the focus of war dominant right contention, and the primary goal attacked between ourselves and the enemy.Numerous studies table
Bright to accuse that network has the uncalibrated visual servo characteristic of complex network, when accusing that network is subject to calculated attack, network seems abnormal fragile,
After especially network key node is under attack, the paralysis of whole network is easily caused.Therefore, how to recognize key node and add
To protect so that accuse that network there is higher anti-attack ability to seem and is even more important.But the key node that has is calculated at present
Method has some limitations, it is impossible to meet the demand for accusing the identification of network key node.
Traditional key node recognizer mainly degree of having, centrad, characteristic vector, betweenness and approximately flows betweenness, but has
Its limitation, for example：The number on side is laid particular emphasis on based on the key node recognition methods of degree distribution, algorithm is simple, accuracy is poor；Base
In the key node recognition methods of the degree of approach, the degree of approach is measured according to position of the node in topological structure, to network topology
The dependence of structure is strong, it is difficult to adapt to accuse the demand of network topology dynamic change；Characteristic vector key node recognition methods meter
Calculation process needs the characteristic vector for solving adjacency matrix, and algorithm complex is larger, when network node is larger recognition speed compared with
Slowly；Based on the key node recognition methods of betweenness, accuracy of identification is high, but algorithm complex is high；Approximate stream betweenness is used as crucial section
Point knows method for distinguishing, and the complexity of algorithm is greatly reduced, and accuracy of identification is relatively accurate, but to accuse the applicability of network compared with
Difference, is not suitable for commanding controlling network.
In view of the problem of the complexity, precision and algorithm applicability of above-mentioned key node recognizer, existing key node
Recognition methods is not directly applicable charge network.Thus, it is necessary to propose a kind of new crucial section suitable for accusing network
Point recognition methods, to solve to accuse network key node identification problem.
The content of the invention
For the problems referred to above that prior art is present, the invention provides a kind of charge network based on level stream betweenness is closed
Key node recognition methods, the method can reduce algorithm complex, improve key node accuracy of identification, be more applicable for accusing net
The needs of network key node identification.
For achieving the above object, the technical scheme is that：A kind of charge network key section based on level stream betweenness
Point recognition methods, comprises the following steps that：
S1：Set up and accuse network model；
S2：Calculate the level stream betweenness for accusing each node in network；
S3：According to node level stream Jie's value distinguishing node criticality.
Further, set up and accuse network model specifically：Command entity is abstracted into into node, the relation between entity is taken out
As into side, setting up and accusing network model, network topological diagram is drawn, and have n node, m bar side, V=describing with G=(V, E)
{v1,v2,v3,…,vnRepresent node set, E={ e1,e2,e3,…,emRepresentative edge set.The adjacency matrix of G is A=
[aij], element a in AijIt is defined as：
Further, calculate the step of accusing the level stream betweenness of each node in network specifically：
a：Network model is accused in initialization, and in whole network, only individual node sends information every time, and other nodes are received
Information, initialized each nodal information amount are 1 (H of unit0(vi)=1),
H0=[1,1 ..., 1,1]N
Wherein N is node number；
b：In information walk process, if node vjDegree be kj, then with node vjAny one node v being joined directly togetheri
Receive information content and be 1/kj, while vjThe information content zero setting of node, after 1 iteration, node viThe information content for possessing
For：
Hn(vi)=Hn-1(vj)/kjN=0,1 ..., D
Wherein, i ≠ j in iterative process, Hn-1(vj) for node vjThe information content possessed after front an iteration, n change for n-th
Generation, and iterations is not more than network level D；
c：It is assumed that A is all and viThe node set that node is joined directly together, then, after n information flow, traversal set A is obtained
To node viPossess information content for Hn(vi)：
(n=1,2 ..., D) and vj∈A
d：Statistics accuses the informational capacity that network each node is collected after D Information Communication, that is, obtain each node
Level stream betweenness, can obtain：
HD=[HD(1),HD(2),…,HD(N)]。
Further, step d centering each element is normalized：
Node criticality matrix H can be obtained through normalizing the final informational capacity of each node,
Further, matrix H is the set of the criticality of all nodes, is sorted for matrix H according to numerical values recited, numerical value
More big then node is more crucial, the node in the node as matrix H of the most critical in charge network corresponding to maximum.
Further, said method also includes：S4：The step of determining accuracy of identification, specially：To accusing network model
Calculated attack is carried out, deleted one by one according to betweenness respectively, approximately flowed what betweenness, level stream betweenness and eigenvector algorithm were identified
Key node, and key node is deleted to accuse net to weigh using maximal connected subgraphs ratio and the two indexs of network efficiency
The impact of network, and then contrast the accuracy of identification of algorithms of different.
Further, after the key node that algorithms of different is identified is subject to calculated attack, the computing formula of network efficiency
It is as follows：
Wherein, dijThe shortest path length between node i and j is represented, N is total to accuse network node；The network effect
Rate reflection is to accuse that network suffers that the distance after calculated attack between arbitrary node becomes estranged degree, and network efficiency value is bigger, net
Network performance is better.
The present invention is due to using above technical method, obtaining following technique effect：The method can reduce algorithm
Complexity, improves key node accuracy of identification, is more applicable for accusing the needs of network key node identification.
Description of the drawings
For clearer explanation embodiments of the invention or the technical scheme of prior art, below will be to embodiment or existing
Accompanying drawing to be used needed for having technology description does one and simply introduces, it should be apparent that, drawings in the following description are only
Some embodiments of the present invention, for those of ordinary skill in the art, on the premise of not paying creative work, may be used also
To obtain other accompanying drawings according to these accompanying drawings.
Fig. 1 is to accuse network model, and command entity is abstracted into node, and the relation between entity is abstracted into side, and different
Side represent different contacts, including command relation and conspiracy relation.Wherein, command relation has the commander two that command step by step and bypass the immediate leadership
Kind, conspiracy relation has two kinds of internal coordination and outside collaboration.The charge network model number of nodes of structure be N=341, command layer
Secondary is 4.
Fig. 2 is information migration network topological diagram；
Fig. 3 is information migration situation and nodal information amount corresponding diagram；After figure interior joint v1 sends 1 information of unit, itself letter
The information content of unit 1 is divided in breath amount zero setting, node v1 neighbor node v2 and v3 equally,It is each node after migration once
Information content；Then node v2 and v3 send self information to neighbours again respectively as information source, and the rest may be inferred, through D=4 time
After information transmission, in final network, all informational capacities collected of each node areWhen v1 nodes
It is sent completely, then makes v2 successively, used as initial sending node, all nodes is collected in the final network of statistics for v3 ... ..., vN
Informational capacity；
Accompanying drawing 4 is level stream betweenness calculation flow chart.
Accompanying drawing 5 is the key node recognizer flow chart based on level stream betweenness.
Accompanying drawing 6 is the complexity comparison schematic diagram of algorithms of different.
Accompanying drawing 7 is the analogous diagram that maximal connected subgraphs ratio removes change with key node.
Accompanying drawing 8 is the analogous diagram that network efficiency removes change with key node.
Specific embodiment
To make purpose, technical scheme and the advantage of embodiments of the invention clearer, with reference to the embodiment of the present invention
In accompanying drawing, clearly complete description is carried out to the technical scheme in the embodiment of the present invention：
Accuse network in all kinds of nodes heterogeneity pass through node information content it is different, identification flow through what is contained much information
Node is simultaneously protected by, that is, protect key node, is to strengthen the important method for accusing network robustness.Key section based on betweenness
Point recognition methods weighs the key of the node as effective recognition methods by the ratio of calculate node shortest path
Degree.Approximate stream betweenness method makes moderate progress on algorithm complex, and main thought is that the information that any one node is produced is passed through
The secondary propagation of network diameter (dia) can cover whole network, account for the network information using the information content that node is eventually received total
The proportion of amount is weighing the significance level of node.And network is accused by strict level, the interconnection of each node inside command post
Intercommunication causes operational information share immediately inside same command post, i.e., after information reaches certain node in command post,
Other nodes inside command post can share to the information immediately.Meanwhile, by information migration can be seen that information content with
Machine migration number of times increases and declines to a great extent, and after migration to certain number of times, finally the information content of migration several times is several to final result
Without impact, so on the basis of the precision for ensureing algorithm, algorithm complex can be reduced by reducing migration number of times.Knot
The level for accusing network is closed, can cover entirely for several times by (D) secondary propagation through level for the information that any one node is produced
Network, all nodes in traverses network, each only one of which node send message, and other nodes receive message, node most terminating
The information content being subject to is exactly the level stream betweenness of node, and with the level stream betweenness of node weighing the significance level of node.It is first
Hypothesis below is given first with definition.
Assume 1：Assume a node vjAn information is produced, the information spreads all over whole charge network after D time is propagated,
Wherein D is the number of levels for accusing network.
Define 1：Number of levels, weaves into according to combat troop, and army (teacher) that active components are divided into, trip's (group), battalion, company etc. refer to
The quantity for waving level is referred to as number of levels.Usually 4 grades, also can be according to the quantity of needs adjustment commander's level of combat duty.
Embodiment 1
A kind of charge network key node recognition methods based on level stream betweenness, comprises the following steps that：
S1：Set up and accuse network model；Command entity is abstracted into into node, the relation between entity is abstracted into side, is set up
Network model is accused, network topological diagram is drawn, as shown in figure 1, and having n node, m bar side, V=describing with G=(V, E)
{v1,v2,v3,…,vnRepresent node set, E={ e1,e2,e3,…,emRepresentative edge set.The adjacency matrix of G is A=
[aij], element a in AijIt is defined as：
S2：The level stream betweenness for accusing each node in network is calculated, level stream betweenness is important as network node is weighed
The index of property；Comprise the concrete steps that：
a：Network model is accused in initialization, and in whole network, only individual node sends information every time, and other nodes are received
Information, initialized each nodal information amount are 1 (H of unit0(vi)=1), is shown below；
H0=[1,1 ..., 1,1]N
Wherein N is node number；
b：In information walk process, if node vjDegree be kj, then with node vjAny one node v being joined directly togetheri
Receive information content and be 1/kj, while vjThe information content zero setting of node, information migration schematic diagram as shown in Figure 2,3, through 1 time
After iteration, node viThe information content for possessing is：
Hn(vi)=Hn-1(vj)/kjN=0,1 ..., D
Wherein, i ≠ j in iterative process, Hn-1(vj) for node vjThe information content possessed after front an iteration, n change for n-th
Generation, and iterations is not more than network level D；
c：It is assumed that A is all and viThe node set that node is joined directly together, then, after n information flow, traversal set A is obtained
To node viPossess information content for Hn(vi)：
(n=1,2 ..., D) and vj∈A
d：Statistics accuses the informational capacity that network each node is collected after D Information Communication, that is, obtain each node
Level stream betweenness, can obtain：
HD=[HD(1),HD(2),…,HD(N)]。
As preferred, step d centering each element is normalized：
Node criticality matrix H can be obtained through normalizing the final informational capacity of each node,
S3：According to node level stream Jie's value distinguishing node criticality.I.e. matrix H is the criticality of all nodes
Set, sorts for matrix H according to numerical values recited, and the more big then node of numerical value is more crucial, and the node of the most critical in charge network is
Node corresponding to maximum in matrix H.
Embodiment 2
The present embodiment is used as the supplement to embodiment 1：
By the present invention propose key node recognition methods and existing characteristic vector, betweenness, the computation complexity of stream betweenness and
Accuracy of identification is contrasted.
1) algorithm complex contrast
It is the calculating of node level stream betweenness based on the core of the charge network key node recognition methods of level stream betweenness,
The time complexity of the key node recognizer is equivalent to the complexity of level stream betweenness algorithm.The following is based on betweenness, stream
The contrast of betweenness and level stream betweenness algorithm complex.
Accuse that network diameter (dia) is defined as the maximum of beeline between nodes：In network any one
There is a beeline between node pair, have maximum and minimum of a value in beeline, maximum is exactly network diameter.Meter
Calculate formula as follows：
Wherein, dijFor node viWith node vjBetween the minimum path of side number jumping figure, i, j ∈ [1, N] and i ≠ j.
The computation complexity of betweenness is o (N3), the algorithm complex of approximate stream betweenness is o (dia × N2).By algorithm flow
The complexity that level stream betweenness algorithm can be obtained is about o (D × N2), in eigenvector algorithm, the computation complexity of characteristic value has reached
O (N are arrived3), therefore the algorithm complex of characteristic vector is also bigger than the algorithm complex of betweenness, about o (N4).Four kinds of algorithms are multiple
Miscellaneous degree is to shown in such as table 1 and Fig. 6.
1 algorithm complex of table is contrasted
In table, to accuse network level, to accuse network diameter, N is to accuse the total nodes of network to dia to D.
From fig. 6 it can be seen that the algorithm complex of level stream betweenness and approximate stream betweenness is significantly lower than betweenness and feature
The algorithm complex of vector, and the algorithm complex of level stream betweenness also reduces half than approximate stream betweenness.That is, from
It can be seen that the advantage of level stream betweenness in the complexity of algorithm.
2) arithmetic accuracy contrast
For above-mentioned structure charge network model be respectively adopted characteristic vector, betweenness, approximate stream betweenness and set forth herein
Level stream betweenness identify key node, experimental result such as 2 (note of table：Only partial data).
Drawn by emulating date comprision：Approximate stream betweenness is essentially identical with this paper level stream betweenness recognition results,
That is the precision of two kinds of recognizers is more or less the same；And betweenness is slightly different with this paper recognition results.
2 four kinds of algorithm key node recognition results of table compare
Embodiment 3
For the accuracy of further analysis of key node recognition methods, this method also includes step S4：It is determined that identification essence
The step of spending, to accusing that network model carries out calculated attack, deletes respectively one by one according to betweenness (Betweenness), approximate stream
Betweenness (Approximation Flow Betweenness), level stream betweenness (Level Flow Betweenness) and feature
The key node that vectorial (Eigenvector) algorithm is identified, and utilize maximal connected subgraphs ratio and network efficiency the two
Index come weigh delete key node to accuse network impact, and then contrast algorithms of different accuracy of identification.
Fig. 7 is subject to after calculated attack for the key node that algorithms of different is identified, maximal connected subgraphs nodes in network
Account for the variation tendency of the ratio S for accusing the total node of network.Abscissa removes quantity for key node, and the longitudinal axis is largest connected son
Figure, in figure, curve represents network performance after the key node for removing algorithms of different identification.Network maximal connected subgraphs node ratio
Computing formula is as follows：
Wherein, NmFor the interstitial content of maximal connected subgraphs, N is to accuse network node sum.
The result obtained from Fig. 7 is can be seen that macroscopically, and the degree of accuracy of eigenvector algorithm is poor, other three kinds
The accuracy difference of algorithm is little；But for from microcosmic, the key node that this paper algorithms are identified when calculated attack is subjected to,
Accuse that the maximal connected subgraphs ratio of network is reduced to 10% at first.
Fig. 8 is subject to the network efficiency change that network is accused after calculated attack to show for the key node that algorithms of different is identified
It is intended to, the computing formula of network efficiency is as follows：
Wherein, dijThe shortest path length between node i and j is represented, N is total to accuse network node.Network efficiency is anti-
What is reflected is to accuse that network suffers that the distance after calculated attack between arbitrary node becomes estranged degree, and network efficiency value is bigger, internetworking
Can be better.
Fig. 7 and Fig. 8 illustrate the knowledge of level stream betweenness algorithm respectively in terms of the two from maximal connected subgraphs and network efficiency
Other precision is better than other several algorithms.The method is applied to charge network, can not only reduce algorithm complex, it is also possible to accurately
Identification key node.
Specifically, in this area, the researcher of network key node identification is accused in research, in stream
On the basis of betweenness, information migration number of times is simply reduced, or information migration number of times is set to into the half of network diameter and is changed
The stream betweenness method entered belongs to the calculating category of level stream betweenness, according to this come recognize accuse network key node method still
It is included in the scope advocated by scope of the present invention patent.
Claims (7)
1. the charge network key node recognition methods based on level stream betweenness, it is characterised in that comprise the following steps that：
S1：Set up and accuse network model；
S2：Calculate the level stream betweenness for accusing each node in network；
S3：According to node level stream Jie's value distinguishing node criticality.
2. the charge network key node recognition methods according to claim requires 1 based on level stream betweenness, its feature exist
In foundation accuses network model specifically：Command entity is abstracted into into node, the relation between entity is abstracted into side, foundation refers to
Control network model, and have n node, m bar side, V={ v describing with G=(V, E)1,v2,v3,…,vnNode set is represented,
E={ e1,e2,e3,…,emRepresentative edge set, the adjacency matrix of G is A=[aij], element a in AijIt is defined as：
3. the charge network key node recognition methods according to claim requires 1 based on level stream betweenness, its feature exist
In in calculating charge network the step of the level stream betweenness of each node specifically：
a：Network model is accused in initialization, each only individual node transmission information in whole network, other node receive informations,
Nodal information amount is
H0=[1,1 ..., 1,1]N
Wherein N is node number；
b：In information walk process, if node vjDegree be kj, then with node vjAny one node v being joined directly togetheriReceive
1/k is to information contentj, while vjThe information content zero setting of node, after 1 iteration, node viThe information content for possessing is：
Hn(vi)=Hn-1(vj)/kjN=0,1 ..., D
Wherein, i ≠ j in iterative process, Hn-1(vj) for node vjThe information content possessed after front an iteration, n are nth iteration,
And iterations is less than or equal to network level D；
c：It is assumed that A is all and viThe node set that node is joined directly together, then, after n information flow, traversal set A is saved
Point viPossess information content for Hn(vi)：
(n=1,2 ..., D) and vj∈A
d：Statistics accuses the informational capacity that network each node is collected after D Information Communication, that is, obtain the level of each node
Stream betweenness, can obtain：
HD=[HD(1),HD(2),…,HD(N)]。
4. the charge network key node recognition methods according to claim requires 3 based on level stream betweenness, its feature exist
In being normalized to step d centering each element：
Node criticality matrix H can be obtained through normalizing the final informational capacity of each node,
5. the charge network key node recognition methods according to claim requires 4 based on level stream betweenness, its feature exist
In matrix H is the set of the criticality of all nodes, is sorted for matrix H according to numerical values recited, and numerical value is more big, and node is more closed
Key, the node in the node as matrix H of the most critical in charge network corresponding to maximum.
6. the charge network key node recognition methods according to claim requires 1 based on level stream betweenness, its feature exist
In said method also includes：S4：The step of determining accuracy of identification, specially：To accusing that network model carries out calculated attack, point
Do not delete one by one according to betweenness, approximately flow betweenness, the key node that level stream betweenness and eigenvector algorithm are identified, and utilize
Maximal connected subgraphs ratio and the two indexs of network efficiency are weighing the impact for deleting key node to accusing network and then right
Than the accuracy of identification of algorithms of different.
7. the charge network key node recognition methods according to claim requires 6 based on level stream betweenness, its feature exist
In, after the key node that algorithms of different is identified is subject to calculated attack, the computing formula of network efficiency is as follows：
Wherein, dijThe shortest path length between node i and j is represented, N is total to accuse network node；The network efficiency is anti-
What is reflected is to accuse that network suffers that the distance after calculated attack between arbitrary node becomes estranged degree, and network efficiency value is bigger, internetworking
Can be better.
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| CN111371624A (en) * | 2020-03-17 | 2020-07-03 | 电子科技大学 | Tactical communication network key node identification method based on environment feedback |
| CN111371624B (en) * | 2020-03-17 | 2022-10-14 | 电子科技大学 | Tactical communication network key node identification method based on environment feedback |
| CN114567562A (en) * | 2022-03-01 | 2022-05-31 | 重庆邮电大学 | Method for identifying key nodes of coupling network of power grid and communication network |
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