CN114826936A - Method and system for determining key node set of weighted network communication efficiency - Google Patents
Method and system for determining key node set of weighted network communication efficiency Download PDFInfo
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- H—ELECTRICITY
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Abstract
The invention provides a method and a system for determining a key node set of weighted network communication efficiency. The method comprises the following steps: step S1, determining the weight of each node based on the communication efficiency of each node in the weighting network, and obtaining a node sequence by descending order; step S2, calculating the weight difference of adjacent nodes in the node sequence in sequence to obtain a distance sequence, determining a dip point from the distance sequence, and dividing the node sequence into node sets by using the dip point; step S3, calculating a communication performance degradation rate of the weighting network by using each node subset in the node set, and selecting a plurality of key nodes from each node based on a threshold value of the communication performance degradation rate to form the key node set.
Description
Technical Field
The invention belongs to the technical field of communication networks, and particularly relates to a method and a system for determining a key node set of communication efficiency of a weighting network.
Background
The key node selection of the weighted network communication is not a single and isolated node but a node system, and due to the constraint of multiple factors, some key nodal nodes are often required to be selected from candidate node systems to ensure the expected communication efficiency. Therefore, the key node selection is focused on how to find these key nodes. In the current research, most achievements discuss the importance of nodes, provide an algorithm for calculating the importance, complete the selection of each node according to the importance, discuss the importance of a single node, and then sequentially select the nodes; for a scene meeting expected communication efficiency, the existing method can only solve the problem of how to quickly select a key node set; but how to select is not judged from the perspective of the node set, and the ordering of each node is completed based on the importance of the node, so that the problem of quick optimization of the node set is solved.
Disclosure of Invention
In order to solve the problems of classification, sequencing and optimization of the key node set of the weighted network communication efficiency, the invention provides a scheme for determining the key node set of the weighted network communication efficiency.
The invention discloses a method for determining a key node set of weighted network communication efficiency in a first aspect. The method comprises the following steps:
step S1, determining a weight of each node based on the communication performance of each node in the weighting network, and sorting the nodes in a descending order according to the weights of the nodes, thereby obtaining a node sequence S ═ { T ═ T { 1 ，...，T n Obtaining the weighting network according to the information interaction network;
step S2, sequentially calculating the node sequence S ═ { T ═ T 1 ，...，T n The weight difference of adjacent nodes in the sequence is obtained, and the distance sequence D is obtained as D 1 ，...，d i ,...，d n-1 In which d is i ＝T i -T i-1 And from said distance sequence D ═ D 1 ，...，d i ,...，d n-1 Determining an abrupt drop point, and using the abrupt drop point to set the node sequence S-T 1 ，...，T n Dividing into node sets L ═ L 1 ,…,L m Wherein the number of the abrupt drop nodes is m-1, L i The divided node subsets are obtained; wherein:
the weight difference of the adjacent nodes represented by the sudden drop points is greater than a threshold value;
step S3, using node set L ═ L { (L) 1 ,…,L m Calculating the communication efficiency reduction rate of the weighting network for each node subset in the network, and selecting a plurality of key nodes from each node based on the threshold value of the communication efficiency reduction rate to form the key node set.
According to the method of the first aspect of the present invention, the step S1 specifically includes:
step S1-1, based on the attribute of each node in the information interaction network, converting the information interaction network into the weighting network, wherein the attribute of each node comprises: the information value, the information processing rate and the receiving processing time delay of each node, and one or more of the bandwidth of a communication link section and the transmission time delay of the link section among the nodes;
step S1-2, determining the weight of each node in a quantification mode based on the node communication efficiency influence indexes; wherein:
determining the node communication efficiency influence indexes including information value, node dispersity, node betweenness centrality, node communication capacity and node communication efficiency of each node by analyzing communication links in the weighting network;
and allocating index weights to the node communication performance influence indexes, wherein the node betweenness centrality has the maximum weight, and the weight of each node is calculated in the quantification mode based on the index weights.
According to the method of the first aspect of the invention,in the step S2, D is determined from the distance sequence D ═ D 1 ，...，d i ,...，d n-1 Determining the dip point specifically comprises:
based on the distance sequence D ═ D 1 ，...，d i ,...，d n-1 Mean value μ and standard deviation σ of }, and anomaly threshold τ ═ d is set max -μ)/2σ，d max Is the maximum value element of the distance sequence;
for the distance sequence D ═ D 1 ，…，d i ,…，d n-1 Every element in the calculation of the element outlier g i ＝(D i - μ)/σ, when g i When t is greater than or equal to t, d is i Judging as a sudden drop point;
based on m-1 of the dip points, in the node sequence S ═ { T ═ T 1 ，…，T n Marking the position where the sudden drop occurs in the node sequence, and according to the position, setting the node sequence S as { T } 1 ，...，T n Dividing into node sets L ═ L 1 ,…,L m }, wherein:
the nodes before the first dip position are divided into a node subset L 1 (ii) a And is
The nodes after the last dip position are divided into a subset of nodes L m (ii) a And is
The nodes between adjacent dip locations are divided into a subset of nodes L 2 ,...,L m-1 。
According to the method of the first aspect of the present invention, the step S3 specifically includes:
step S3-1, taking the node subset as granularity, closing the node subset L in sequence 1 ,...,L m The node in (1) calculates the communication efficiency E of the weighting network every time the shutdown is executed 1～i (L 1～i ) I is not less than 1 and not more than m, wherein E 1～i (L 1～i ) Indicating that the slave node subset L is being switched off 1 To node subset L i The communication performance of the weighting network, and further calculating the communication performance degradation rate of the weighting network in the current state
Until the communication performance degradation rate is not less than the communication performance degradation rate threshold; wherein:
e represents the maximum value of the communication efficiency of the weighting network when all nodes work fully;
step S3-2, obtaining the closed node subset collection, for the last node subset L of the collection i Performing a binary search operation to determine the subset of nodes L i Such that:
at the closing node subset L 1 To node subset L i-1 And the node subset L i In the case of the first n nodes, the communication performance degradation rate is lower than the communication performance degradation rate threshold; and is provided with
At the closing node subset L 1 To node subset L i-1 And the node subset L i In the case of the first n +1 nodes, the communication performance degradation rate is not lower than the communication performance degradation rate threshold;
step S3-3, node subset L 1 To node subset L i-1 All nodes in (b) and the node subset L i The first n +1 nodes as the key nodes to form the set of key nodes.
The invention discloses a system for determining a key node set for weighting network communication efficiency in a second aspect. The system comprises:
a first processing unit, configured to, in step S1, determine a weight of each node based on communication performance of each node in the weighting network, and sort the nodes in a descending order according to the weight of each node, so as to obtain a node sequence S ═ { T ═ T { (T {) 1 ，...，T n Obtaining the weighting network according to the information interaction network;
a second processing unit configured to sequentially calculate the node sequence S ═ { T ═ T 1 ，...，T n Obtaining the distance sequence D ═ D by the weight difference of adjacent nodes in the sequence 1 ，...，d i ,...，d n-1 In which d is i ＝T i -T i-1 And from said distance sequence D ═ D 1 ，...，d i ,...，d n-1 Determining an abrupt drop point, and using the abrupt drop point to set the node sequence S-T 1 ，...，T n Dividing into node sets L ═ L 1 ,…,L m Wherein the number of the abrupt drop nodes is m-1, L i The divided node subsets are obtained; wherein:
the weight difference of the characteristic adjacent nodes of the sudden drop points is greater than a threshold value;
a third processing unit configured to utilize a set of nodes L ═ { L ═ L 1 ,…,L m Calculating the communication efficiency reduction rate of the weighting network for each node subset in the network, and selecting a plurality of key nodes from each node based on the threshold value of the communication efficiency reduction rate to form the key node set.
According to the system of the second aspect of the invention, the first processing unit is specifically configured to perform:
converting the information interaction network into the weighting network based on the attribute of each node in the information interaction network, wherein the attribute of each node comprises: the information value, the information processing rate and the receiving processing time delay of each node, and one or more of the bandwidth of a communication link section and the transmission time delay of the link section among the nodes;
determining the weight of each node in a quantitative mode based on the node communication efficiency influence indexes; wherein:
determining the node communication efficiency influence indexes including the information value, the node dispersity, the node betweenness centrality, the node communication capacity and the node communication efficiency of each node by analyzing the communication links in the weighting network;
and allocating index weights to the node communication performance influence indexes, wherein the node betweenness centrality has the maximum weight, and the weight of each node is calculated in the quantification mode based on the index weights.
According to a second aspect of the inventionThe second processing unit is specifically configured to perform the following steps to derive the distance sequence D ═ { D ═ from the distance sequence 1 ，...，d i ,...，d n-1 Determining the dip point:
based on the distance sequence D ═ D 1 ，...，d i ,...，d n-1 Mean value μ and standard deviation σ of }, and anomaly threshold τ ═ d is set max -μ)/2σ，d max Is the maximum value element of the distance sequence;
for the distance sequence D ═ D 1 ，...，d i ,...，d n-1 Every element in the calculation of the element outlier g i ＝(D i - μ)/σ, when g i When t is greater than or equal to t, d is i Judging as a sudden drop point;
based on m-1 of the dip points, in the node sequence S ═ { T ═ T 1 ，...，T n Marking the position where the sudden drop occurs in the node sequence, and according to the position, setting the node sequence S as { T } 1 ，...，T n Dividing into node sets L ═ L 1 ,…,L m }, wherein:
the nodes before the first dip position are divided into a node subset L 1 (ii) a And is
The nodes after the last dip position are divided into a subset of nodes L m (ii) a And is
The nodes between adjacent dip locations are divided into a subset of nodes L 2 ,...,L m-1 。
According to the system of the second aspect of the invention, the third processing unit is specifically configured to perform:
sequentially closing the node subsets L by taking the node subsets as granularity 1 ,...,L m The node in (1) calculates the communication efficiency E of the weighting network every time the shutdown is executed 1～i (L 1～i ) I is more than or equal to 1 and less than or equal to m, wherein E 1～i (L 1～i ) Indicating that the slave node subset L is being switched off 1 To node subset L i The communication performance of the weighting network, and further calculating the communication performance degradation rate of the weighting network in the current state
Until the communication performance degradation rate is not less than the communication performance degradation rate threshold; wherein:
e represents the maximum value of the communication efficiency of the weighting network when all nodes work fully;
obtaining a set of subsets of nodes that are closed, for the last subset of nodes L of the set i Performing a binary search operation to determine the subset of nodes L i Such that:
at the closing node subset L 1 To node subset L i-1 And the node subset L i In the case of the first n nodes, the communication performance degradation rate is lower than the communication performance degradation rate threshold; and is
At the closing node subset L 1 To node subset L i-1 And the node subset L i In the case of the first n +1 nodes, the communication performance degradation rate is not lower than the communication performance degradation rate threshold;
step S3-3, node subset L 1 To node subset L i-1 All nodes in (b) and the node subset L i The first n +1 nodes as the key nodes to form the set of key nodes.
A third aspect of the invention discloses an electronic device. The electronic device comprises a memory storing a computer program and a processor implementing the steps of a method for determining a set of key nodes for weighted network communication efficiency according to the first aspect of the present invention when the computer program is executed by the processor.
A fourth aspect of the invention discloses a computer-readable storage medium. The computer readable storage medium has stored thereon a computer program which, when executed by a processor, performs the steps of a method of determining a set of key nodes for weighted network communication efficiency according to the first aspect of the invention.
In conclusion, the technical scheme of the invention can effectively solve the problem of how to quickly select the key node set on the premise that the weighted network meets the specific communication efficiency expectation, and further can provide guidance for the problems of robustness evaluation, key protection node screening and the like of a network structure. The method is based on the existing network communication efficiency evaluation model, firstly, the importance of each node in the network is converted into accurate quantification to weight, then clustering division is carried out according to the weight distribution condition, and finally, the optimal node set meeting the expectation is determined in a step-by-step screening mode.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the embodiments or the description in the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
FIG. 1 is a flow diagram of a method of determining a set of key nodes that weight network communication performance in accordance with an embodiment of the present invention;
FIG. 2 is a block diagram of a system for determining a set of key nodes for weighted network communication efficiency in accordance with an embodiment of the present invention;
fig. 3 is a block diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The invention discloses a method for determining a key node set of weighted network communication efficiency in a first aspect. Fig. 1 is a flowchart of a method for evaluating communication performance of a weighting network according to an embodiment of the present invention; as shown in fig. 1, the method includes:
step S1, determining a weight of each node based on the communication performance of each node in the weighting network, and sorting the nodes in a descending order according to the weights of the nodes, so as to obtain a node sequence S ═ T 1 ，...，T n Obtaining the weighting network according to the information interaction network;
step S2, sequentially calculating the node sequence S ═ { T ═ T 1 ，...，T n Obtaining the distance sequence D ═ D by the weight difference of adjacent nodes in the sequence 1 ，...，d i ,...，d n-1 In which d is i ＝T i -T i-1 And from said distance sequence D ═ D 1 ，...，d i ,...，d n-1 Determine a dip point with which to place the node sequence S ═ T 1 ，...，T n Dividing into node sets L ═ L 1 ,…,L m Wherein the number of the abrupt drop nodes is m-1, L i The divided node subsets are obtained; wherein:
the weight difference of the adjacent nodes represented by the sudden drop points is greater than a threshold value;
step S3, using node set L ═ L { (L) 1 ,…,L m Calculating the communication efficiency reduction rate of the weighting network for each node subset in the network, and selecting a plurality of key nodes from each node based on the threshold value of the communication efficiency reduction rate to form the key node set.
In step S1, based on the communication performance of each node in the weighting network, determining the weight of each node, and sorting the nodes in a descending order according to the weight of each node, so as to obtain a node sequence S ═ { T ═ T { (T) } 1 ，...，T n And obtaining the weighting network according to the information interaction network.
In some embodiments, the step S1 specifically includes:
step S1-1, based on the attribute of each node in the information interaction network, converting the information interaction network into the weighting network, wherein the attribute of each node comprises: the information value, the information processing rate and the receiving processing time delay of each node, and one or more of the bandwidth of a communication link section and the transmission time delay of the link section among the nodes;
step S1-2, determining the weight of each node in a quantification mode based on the node communication efficiency influence indexes; wherein:
determining the node communication efficiency influence indexes including the information value, the node dispersity, the node betweenness centrality, the node communication capacity and the node communication efficiency of each node by analyzing the communication links in the weighting network;
and allocating index weights to the node communication performance influence indexes, wherein the node betweenness centrality has the maximum weight, and the weight of each node is calculated in the quantification mode based on the index weights.
Specifically, the nodes are connected by edges, the edges represent communication link segments between the nodes, one communication link (from the first communication node to the tenth communication node) can be routed to other communication nodes (the fifth communication node, the eighth communication node) and a plurality of communication link segments (including the communication link segments of the first communication node to the fifth communication node, the communication link segments of the fifth communication node to the eighth communication node, and the communication link segments of the eighth communication node to the tenth communication node).
In step S1-1, converting the information interaction network into the weighting network specifically includes:
for each communication node, determining a node weight value of the communication node of the information interaction network based on the values of the dimensions of the information value of the communication node (the information value of the communication information generated by the communication node), the information processing rate and the receiving and processing delay;
for each communication link segment, determining a link segment weight value for the communication link segment based on a bandwidth of the communication link segment and a link segment transmission delay, the link weight value being a link weight factor for communication nodes at both ends of the communication link, after obtaining all link segment weight factors associated with each communication node, determining a node link segment weight value of the communication node through summation by each communication node (for example, after a first communication node is directly connected with a third communication node, a fifth communication node and a twentieth communication node through a single communication link segment, and link segment weight values of communication link segments among the third communication node, the fifth communication node and the twentieth communication node are respectively obtained, the three link segment weight values are used as three link segment weight factors of the first communication node, and the node link segment weight value of the first communication node is obtained after summation);
and summing the node weight values and the node link section weight values in the information interaction network to obtain the weight of each communication node in the information interaction network, so that the information interaction network is converted into the weighting network.
In step S1-2, determining the node i communication performance influence index including the information value v of each node by analyzing the communication links in the weighting network i Node maintenance cost c i Node betweenness centrality b i Node communication capability p i Efficiency of communication with node e i (ii) a Specifically, a central server or data processing unit/module exists in the network to obtain and store the above index attributes of each node.
In step S1-2, since the evaluation model and the measurement unit of each index are different from each other in terms of influence on communication performance, index weight and normalization processing need to be assigned to each node communication performance influence index, referring to a typical network node importance evaluation method, in step S1-2, a maximum weight w is assigned to the node betweenness centrality b Similarly, the weights of the other indexes are obtained by adopting an analytic hierarchy process, and are respectively w v ,w c ,w p ,w e (ii) a Calculating the weight w of the node i based on the index weight by the quantization mode i ＝w b b i +w v v i +w c c i +w p p i +w e e i
In step S2, the node sequence S ═ { T is sequentially calculated 1 ，...，T n Obtaining the distance sequence D ═ D by the weight difference of adjacent nodes in the sequence 1 ，...，d i ,...，d n-1 In which d is i ＝T i -T i-1 And from said distance sequence D ═ D 1 ，...，d i ,...，d n-1 Determining an abrupt drop point, and using the abrupt drop point to set the node sequence S-T 1 ，...，T n Dividing into node sets L ═ L 1 ,…,L m Wherein the number of the abrupt drop nodes is m-1, L i Is divided node subset; wherein: the weight difference of the adjacent nodes represented by the sudden drop points is larger than a threshold value.
In some embodiments, in said step S2, D ═ D from said sequence of distances 1 ，...，d i ,...，d n-1 Determining the dip point specifically comprises:
based on the distance sequence D ═ D 1 ，...，d i ,...，d n-1 Mean value μ and standard deviation σ of }, and anomaly threshold τ ═ d is set max -μ)/2σ，d max Is the maximum value element of the distance sequence;
for the distance sequence D ═ D 1 ，...，d i ,...，d n-1 Each element in the } calculates an element outlier g i ＝(D i - μ)/σ, when g i When t is greater than or equal to t, d is i Judging the sudden drop points, and sequentially finding out all the sudden drop points;
based on m-1 of the dip points, in the node sequence S ═ { T ═ T 1 ，...，T n Marking the position where the sudden drop occurs in the node sequence, and according to the position, setting the node sequence S as { T } 1 ，...，T n Dividing into node sets L ═ L 1 ,…,L m }, wherein:
the nodes before the first dip position are divided into a node subset L 1 (ii) a And is
The nodes after the last dip position are divided into a subset of nodes L m (ii) a And is
The nodes between adjacent dip locations are divided into a subset of nodes L 2 ,...,L m-1 。
The specific algorithm is as follows:
(1) respectively calculating the average value mu and the standard deviation sigma of D;
(2) assuming that the maximum value of each element in D is Dmax, let τ be (D) max -μ)/2σ；
(3) Sequentially calculating abnormal values g of all elements in D i ＝(D i -μ)/σ；
(4) If g is i If t is greater than or equal to τ, d will be i Marked as abnormal points, illustrate T i And T i-1 There is a sudden drop in value between nodes, with S ═ T 1 ，...，T n Divide into two sets T 1 ，...，T i And { T } i+1 ，...，T n }；
(5) After all the abnormal points are determined, the node set S is divided into a plurality of node subsets: l ═ L 1 ,…,L m }。
In step S3, the node set L ═ { L ═ L is used 1 ,…,L m Calculating the communication efficiency reduction rate of the weighting network for each node subset in the network, and selecting a plurality of key nodes from each node based on the threshold value of the communication efficiency reduction rate to form the key node set.
In some embodiments, the step S3 specifically includes:
step S3-1, taking the node subset as granularity, closing the node subset L in sequence 1 ,...,L m The node in (1) calculates the communication efficiency E of the weighting network every time the shutdown is executed 1～i (L 1～i ) I is not less than 1 and not more than m, wherein E 1～i (L 1～i ) Indicating that the slave node subset L is being switched off 1 To node subset L i The communication performance of the weighting network, and further calculating the communication performance degradation rate of the weighting network in the current state
Until the communication performance is reducedA rate is not lower than the communication performance degradation rate threshold; wherein:
e represents the maximum value of the communication efficiency of the weighting network when all nodes work fully;
step S3-2, obtaining the closed node subset collection, for the last node subset L of the collection i Performing a binary search operation to determine the subset of nodes L i Such that:
at the closing node subset L 1 To node subset L i-1 And the node subset L i In the case of the first n nodes, the communication performance degradation rate is lower than the communication performance degradation rate threshold; and is
At the closing node subset L 1 To node subset L i-1 And the node subset L i In the case of the first n +1 nodes, the communication performance degradation rate is not lower than the communication performance degradation rate threshold;
step S3-3, node subset L 1 To node subset L i-1 All nodes in (b) and the node subset L i The first n +1 nodes as the key nodes to form the set of key nodes.
Specifically, a key node optimization algorithm is provided, and a node set L ═ L is selected 1 ,…,L m Will node L 1 Substituting, calculating the communication efficiency degradation rate
Wherein r is i As a node value, B i For the centrality of network betweenness, it can be defined as the ratio of the number of paths passing through a certain node in all shortest paths in the network to the total number of shortest paths, and it reflects the role and influence of the node in the whole network, and is an important global geometry.
n jk Representing the number of shortest paths between nodes j and k; n is jk (i) Representing the shortest path between nodes j, kThe number of nodes i passed through. If eta is less than c, collecting the nodes L 2 Adding the node set L into the node set in the closed state, repeating the calculation until eta is more than or equal to c, and collecting the selected node set L 1～i Last one L of i And searching a node set with the communication efficiency degradation rate closest to a threshold value by using a binary search method or a sequential search method as a key node set.
The invention discloses a system for determining a key node set for weighting network communication efficiency in a second aspect. FIG. 2 is a block diagram of a system for determining a set of key nodes for weighted network communication efficiency in accordance with an embodiment of the present invention; as shown in fig. 2, the system 200 includes:
a first processing unit 201 configured to determine a weight of each node based on communication performance of each node in the weighting network, and sort the nodes in a descending order according to the weight of each node, so as to obtain a node sequence S ═ { T ═ T 1 ，...，T n Obtaining the weighting network according to the information interaction network;
a second processing unit 202 configured to sequentially calculate the node sequence S ═ { T ═ T 1 ，...，T n Obtaining the distance sequence D ═ D by the weight difference of adjacent nodes in the sequence 1 ，...，d i ,...，d n-1 In which d is i ＝T i -T i-1 And from said distance sequence D ═ D 1 ，...，d i ,...，d n-1 Determining an abrupt drop point, and using the abrupt drop point to set the node sequence S-T 1 ，...，T n Dividing into node sets L ═ L 1 ,…,L m Wherein the number of the abrupt drop nodes is m-1, L i The divided node subsets are obtained; wherein:
the weight difference of the adjacent nodes represented by the sudden drop points is greater than a threshold value;
a third processing unit 203 configured to utilize a node set L ═ { L ═ L 1 ,…,L m Calculating the communication performance reduction rate of the weighting network for each node subset in the network, and selecting a plurality of nodes from the nodes based on the threshold value of the communication performance reduction rateKey nodes to form the set of key nodes.
According to the system of the second aspect of the present invention, the first processing unit 201 is specifically configured to perform:
converting the information interaction network into the weighting network based on the attribute of each node in the information interaction network, wherein the attribute of each node comprises: the information value, the information processing rate and the receiving processing time delay of each node, and one or more of the bandwidth of a communication link section and the transmission time delay of the link section among the nodes;
determining the weight of each node in a quantitative mode based on the node communication efficiency influence indexes; wherein:
determining the node communication efficiency influence indexes including information value, node dispersity, node betweenness centrality, node communication capacity and node communication efficiency of each node by analyzing communication links in the weighting network;
and allocating index weights to the node communication performance influence indexes, wherein the node betweenness centrality has the maximum weight, and the weight of each node is calculated in the quantification mode based on the index weights.
According to the system of the second aspect of the present invention, said second processing unit 202 is specifically configured to perform the following steps to derive said distance sequence D ═ { D ═ from said distance sequence 1 ，...，d i ,...，d n-1 Determining the dip point:
based on the distance sequence D ═ D 1 ，...，d i ,...，d n-1 Mean value μ and standard deviation σ of }, and anomaly threshold τ ═ d is set max -μ)/2σ，d max Is the maximum value element of the distance sequence;
for the distance sequence D ═ D 1 ，...，d i ,…，d n-1 Every element in the calculation of the element outlier g i ＝(D i - μ)/σ, when g i When t is greater than or equal to t, d is i Judging as a sudden drop point;
based on m-1 of the sudden drop points, at the sectionPoint sequence S ═ T 1 ，...，T n Marking the position where the sudden drop occurs in the node sequence, and according to the position, setting the node sequence S as { T } 1 ，...，T n Dividing into node sets L ═ L 1 ,…,L m }, in which:
the nodes before the first dip position are divided into a node subset L 1 (ii) a And is
The nodes after the last dip position are divided into a subset of nodes L m (ii) a And is
The nodes between adjacent dip locations are divided into a subset of nodes L 2 ,...,L m-1 。
According to the system of the second aspect of the present invention, the third processing unit 203 is configured to perform:
sequentially closing the node subsets L by taking the node subsets as granularity 1 ,...,L m The node in (1) calculates the communication efficiency E of the weighting network every time the shutdown is executed 1～i (L 1～i ) I is not less than 1 and not more than m, wherein E 1～i (L 1～i ) Indicating that the slave node subset L is being switched off 1 To node subset L i The communication performance of the weighting network in the case of all the nodes in the network, and further calculating the communication performance degradation rate of the weighting network in the current state
Until the communication performance degradation rate is not less than the communication performance degradation rate threshold; wherein:
e represents the maximum value of the communication efficiency of the weighting network when all nodes work fully;
obtaining a set of subsets of nodes that are closed, for the last subset of nodes L of the set i Performing a binary search operation to determine the subset of nodes L i Such that:
at the closing node subset L 1 To node subset L i-1 And the node subset L i In the case of the first n nodes, the communication performance degradation rate is lower than the communication performance degradation rate threshold(ii) a And is
At the closing node subset L 1 To node subset L i-1 And the node subset L i In the case of the first n +1 nodes, the communication performance degradation rate is not lower than the communication performance degradation rate threshold;
step S3-3, node subset L 1 To node subset L i-1 All nodes in and the subset of nodes L i The first n +1 nodes as the key nodes to form the set of key nodes.
A third aspect of the invention discloses an electronic device. The electronic device comprises a memory storing a computer program and a processor implementing the steps of a method for determining a set of key nodes for weighted network communication efficiency according to the first aspect of the present invention when the computer program is executed by the processor.
FIG. 3 is a block diagram of an electronic device according to an embodiment of the invention; as shown in fig. 3, the electronic device includes a processor, a memory, a communication interface, a display screen, and an input device connected through a system bus. Wherein the processor of the electronic device is configured to provide computing and control capabilities. The memory of the electronic equipment comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The communication interface of the electronic device is used for carrying out wired or wireless communication with an external terminal, and the wireless communication can be realized through WIFI, an operator network, Near Field Communication (NFC) or other technologies. The display screen of the electronic equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the electronic equipment can be a touch layer covered on the display screen, a key, a track ball or a touch pad arranged on the shell of the electronic equipment, an external keyboard, a touch pad or a mouse and the like.
It will be understood by those skilled in the art that the structure shown in fig. 3 is only a partial block diagram related to the technical solution of the present disclosure, and does not constitute a limitation of the electronic device to which the solution of the present application is applied, and a specific electronic device may include more or less components than those shown in the drawings, or combine some components, or have a different arrangement of components.
A fourth aspect of the invention discloses a computer-readable storage medium. The computer readable storage medium has stored thereon a computer program which, when executed by a processor, performs the steps of a method of determining a set of key nodes for weighted network communication efficiency according to the first aspect of the invention.
In conclusion, the technical scheme of the invention can effectively solve the problem of how to quickly select the key node set on the premise that the weighted network meets the specific communication efficiency expectation, and further can provide guidance for the problems of robustness evaluation, key protection node screening and the like of a network structure. The method is based on the existing network communication efficiency evaluation model, firstly, the importance of each node in the network is converted into accurate quantification to weight, then clustering division is carried out according to the weight distribution condition, and finally, the optimal node set meeting the expectation is determined in a step-by-step screening mode.
Note that, the technical features of the above embodiments may be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description in the present specification. The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (10)
1. A method for determining a set of key nodes for weighted network communication efficiency, the method comprising:
step (ii) ofS1, determining the weight of each node based on the communication efficiency of each node in the weighting network, and sequencing each node in a descending order according to the weight of each node, thereby obtaining a node sequence S ═ { T ═ T 1 ，...，T n Obtaining the weighting network according to the information interaction network;
step S2, sequentially calculating the node sequence S ═ { T ═ T 1 ，...，T n Obtaining the distance sequence D ═ D by the weight difference of adjacent nodes in the sequence 1 ，...，d i ,...，d n-1 In which d is i ＝T i -T i-1 And from said distance sequence D ═ D 1 ，...，d i ,...，d n-1 Determining an abrupt drop point, and using the abrupt drop point to set the node sequence S-T 1 ，...，T n Dividing into node sets L ═ L 1 ,…,L m Wherein the number of the abrupt drop nodes is m-1, L i The divided node subsets are obtained; wherein:
the weight difference of the adjacent nodes represented by the sudden drop points is greater than a threshold value;
step S3, using node set L ═ L { (L) 1 ,…,L m Calculating the communication efficiency reduction rate of the weighting network for each node subset in the network, and selecting a plurality of key nodes from each node based on the threshold value of the communication efficiency reduction rate to form the key node set.
2. The method as claimed in claim 1, wherein the step S1 specifically includes:
step S1-1, based on the attribute of each node in the information interaction network, converting the information interaction network into the weighting network, wherein the attribute of each node comprises: the information value, the information processing rate and the receiving processing time delay of each node, and one or more of the bandwidth of a communication link section and the transmission time delay of the link section among the nodes;
step S1-2, determining the weight of each node in a quantification mode based on the node communication efficiency influence indexes; wherein:
determining the node communication efficiency influence indexes including information value, node dispersity, node betweenness centrality, node communication capacity and node communication efficiency of each node by analyzing communication links in the weighting network;
and allocating index weights to the node communication performance influence indexes, wherein the node betweenness centrality has the maximum weight, and the weight of each node is calculated in the quantification mode based on the index weights.
3. The method of claim 2, wherein in step S2, the distance sequence D ═ D is determined from the distance sequence 1 ，...，d i ,...，d n-1 Determining the dip point specifically comprises:
based on the distance sequence D ═ D 1 ，...，d i ,...，d n-1 Mean value μ and standard deviation σ of }, and anomaly threshold τ ═ d is set max -μ)/2σ，d max Is the maximum value element of the distance sequence;
for the distance sequence D ═ D 1 ，...，d i ,...，d n-1 Every element in the calculation of the element outlier g i ＝(D i - μ)/σ, when g i When t is greater than or equal to t, d is i Judging as a sudden drop point;
based on m-1 of the dip points, in the node sequence S ═ { T ═ T 1 ，...，T n Marking the position where the sudden drop occurs in the node sequence, and according to the position, setting the node sequence S as { T } 1 ，...，T n Dividing into node sets L ═ L 1 ,…,L m }, wherein:
the nodes before the first dip position are divided into a node subset L 1 (ii) a And is
The nodes after the last dip position are divided into a subset of nodes L m (ii) a And is
The nodes between adjacent dip locations are divided into nodesSubset L 2 ,...,L m-1 。
4. The method as claimed in claim 3, wherein the step S3 specifically includes:
step S3-1, taking the node subset as granularity, closing the node subset L in sequence 1 ,...,L m The node in (1) calculates the communication efficiency E of the weighting network every time the shutdown is executed 1～i (L 1～i ) I is more than or equal to 1 and less than or equal to m; further calculating the communication efficiency reduction rate of the weighting network in the current state
Until the communication performance degradation rate is not less than the communication performance degradation rate threshold; wherein:
E 1～i (L 1～i ) Indicating that the slave node subset L is being switched off 1 To node subset L i The communication performance of the weighting network in case of all nodes in the network;
e represents the maximum value of the communication efficiency of the weighting network when all nodes work fully;
step S3-2, obtaining the closed node subset collection, for the last node subset L of the collection i Performing a binary search operation to determine the subset of nodes L i Such that:
at the closing node subset L 1 To node subset L i-1 And the node subset L i In the case of the first n nodes, the communication performance degradation rate is lower than the communication performance degradation rate threshold; and is
At the closing node subset L 1 To node subset L i-1 And the node subset L i In the case of the first n +1 nodes, the communication performance degradation rate is not lower than the communication performance degradation rate threshold;
step S3-3, node subset L 1 To node subset L i-1 All nodes inAnd the node subset L i The first n +1 nodes as the key nodes to form the set of key nodes.
5. A system for determining a set of key nodes for weighted network communication efficiency, the system comprising:
a first processing unit configured to determine a weight of each node based on a communication performance of each node in the weighting network, and sort the nodes in a descending order according to the weight of each node, thereby obtaining a node sequence S ═ { T ═ T 1 ，...，T n Obtaining the weighting network according to the information interaction network;
a second processing unit configured to sequentially calculate the node sequence S ═ { T ═ T 1 ，...，T n Obtaining the distance sequence D ═ D by the weight difference of adjacent nodes in the sequence 1 ，...，d i ,...，d n-1 In which d is i ＝T i -T i-1 And from said distance sequence D ═ D 1 ，...，d i ,...，d n-1 Determining an abrupt drop point, and using the abrupt drop point to set the node sequence S-T 1 ，...，T n Dividing into node sets L ═ L 1 ,…,L m Wherein the number of the abrupt drop nodes is m-1, L i The divided node subsets are obtained; wherein:
the weight difference of the adjacent nodes represented by the sudden drop points is greater than a threshold value;
a third processing unit configured to utilize a set of nodes L ═ { L ═ L 1 ,…,L m Calculating the communication efficiency reduction rate of the weighting network for each node subset in the network, and selecting a plurality of key nodes from each node based on the threshold value of the communication efficiency reduction rate to form the key node set.
6. The system for determining a set of key nodes that weigh network communication efficiency according to claim 5, wherein said first processing unit is specifically configured to perform:
converting the information interaction network into the weighting network based on the attribute of each node in the information interaction network, wherein the attribute of each node comprises: the information value, the information processing rate and the receiving processing time delay of each node, and one or more of the bandwidth of a communication link section and the transmission time delay of the link section among the nodes;
determining the weight of each node in a quantitative mode based on the node communication efficiency influence indexes; wherein:
determining the node communication efficiency influence indexes including information value, node dispersity, node betweenness centrality, node communication capacity and node communication efficiency of each node by analyzing communication links in the weighting network;
and allocating index weights to the node communication performance influence indexes, wherein the node betweenness centrality has the maximum weight, and the weight of each node is calculated in the quantification mode based on the index weights.
7. The system of claim 6, wherein the second processing unit is specifically configured to perform the following steps to determine the set of key nodes that will enable weighted network communication from the distance sequence D ═ D 1 ，...，d i ,...，d n-1 Determining the dip point:
based on the distance sequence D ═ D 1 ，...，d i ,...，d n-1 Mean value μ and standard deviation σ of }, and anomaly threshold τ ═ d is set max -μ)/2σ，d max Is the maximum value element of the distance sequence;
for the distance sequence D ═ D 1 ，...，d i ,...，d n-1 Every element in the calculation of the element outlier g i ＝(D i - μ)/σ, when g i When t is greater than or equal to t, d is i Judging as a sudden drop point;
based on m-1 of the dip points, in the node sequence S ═ { T ═ T 1 ，...，T n The position of sudden drop of the mark is marked according toThe position is to set the node sequence S ═ { T ═ T 1 ，...，T n Dividing into node sets L ═ L 1 ,…,L m }, wherein:
the nodes before the first dip position are divided into a node subset L 1 (ii) a And is
The nodes after the last dip position are divided into a subset of nodes L m (ii) a And is
The nodes between adjacent dip locations are divided into a subset of nodes L 2 ,...,L m-1 。
8. The system of claim 7, wherein the third processing unit is specifically configured to perform:
sequentially closing the node subsets L by taking the node subsets as granularity 1 ,...,L m The node in (1) calculates the communication efficiency E of the weighting network every time the shutdown is executed 1～i (L 1～i ) I is more than or equal to 1 and less than or equal to m; further calculating the communication efficiency reduction rate of the weighting network in the current state
Until the communication performance degradation rate is not less than the communication performance degradation rate threshold; wherein:
E 1～i (L 1～i ) Indicating that the slave node subset L is being switched off 1 To node subset L i The communication performance of the weighting network in case of all nodes in the network;
e represents the maximum value of the communication efficiency of the weighting network when all nodes work fully;
obtaining a set of subsets of nodes that are closed, for the last subset of nodes L of the set i Performing a binary search operation to determine the subset of nodes L i Such that:
at the closing node subset L 1 To node subset L i-1 And the node subset L i In the case of the first n nodes, thenThe rate of degradation of communication performance is below the rate of degradation threshold; and is
At the closing node subset L 1 To node subset L i-1 And the node subset L i In the case of the first n +1 nodes, the communication performance degradation rate is not lower than the communication performance degradation rate threshold;
step S3-3, node subset L 1 To node subset L i-1 All nodes in and the subset of nodes L i The first n +1 nodes as the key nodes to form the set of key nodes.
9. An electronic device, characterized in that the electronic device comprises a memory storing a computer program and a processor implementing the steps of a method of determining a set of key nodes that weigh network communication efficiency according to any of claims 1 to 4 when the computer program is executed.
10. A computer-readable storage medium, having stored thereon a computer program which, when executed by a processor, performs the steps of a method of determining a set of key nodes for weighted network communication efficiency according to any one of claims 1 to 4.
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