CN114826936B - 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
- Publication number
- CN114826936B CN114826936B CN202210387176.1A CN202210387176A CN114826936B CN 114826936 B CN114826936 B CN 114826936B CN 202210387176 A CN202210387176 A CN 202210387176A CN 114826936 B CN114826936 B CN 114826936B
- Authority
- CN
- China
- Prior art keywords
- node
- nodes
- communication
- subset
- network
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
- H04L41/14—Network analysis or design
- H04L41/145—Network analysis or design involving simulating, designing, planning or modelling of a network
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
- H04L41/12—Discovery or management of network topologies
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D30/00—Reducing energy consumption in communication networks
- Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks
Landscapes
- Engineering & Computer Science (AREA)
- Computer Networks & Wireless Communication (AREA)
- Signal Processing (AREA)
- Data Exchanges In Wide-Area Networks (AREA)
Abstract
The invention provides a method and a system for determining a key node set of a 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 weighted network, and arranging the nodes in a descending order to obtain a node sequence; step S2, weight differences of adjacent nodes in the node sequence are calculated in sequence to obtain a distance sequence, a sudden drop point is determined from the distance sequence, and the node sequence is divided into node sets by the sudden drop point; and S3, calculating the communication efficiency degradation rate of the weighted network by utilizing each node subset in the node set, and selecting a plurality of key nodes from the nodes based on the communication efficiency degradation rate threshold 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 a weighted network communication efficiency.
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 various factors, some key node nodes in the candidate node system are often required to be selected 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 node importance, an algorithm for calculating the importance is provided, then the selection of each node is completed according to the importance, the research discusses the importance of a single node, and then the node selection is sequentially carried out; for the scene meeting the expected communication efficiency, the existing method can only solve the problem of how to quickly select the key node set; however, how to select the node set is not judged from the point of view of the node set, and the ordering of the nodes is finished based on the importance of the nodes, so that the problem of rapid optimization of the node set is solved.
Disclosure of Invention
In order to solve the problems of classification, ordering and preference of the key node sets of the weighted network communication efficiency, the invention provides a scheme for determining the key node sets of the weighted network communication efficiency.
The first aspect of the present invention discloses a method for determining a set of key nodes for weighting 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 weighted network, and ordering each node in a descending order according to the weight of each node, thereby obtaining a node sequence S= { T 1 ，…，T n -wherein the weighting network is derived from an information interaction network;
step S2, sequentially calculating the node sequence S= { T 1 ，...，T n Weight difference of adjacent nodes in the sequence to obtain a distance sequence D= { D 1 ，...，d i ,...，d n-1 }, where d i ＝T i -T i-1 And from the distance sequence d= { D 1 ，…，d i ,…，d n-1 Determining a dip point in the sequence of nodes s= { T using the dip point 1 ，…，T n Dividing into node sets l= { L 1 ,…,L m -wherein the number of dip points is m-1, L i Is a subset of the partitioned nodes; wherein:
the abrupt drop point represents that the weight difference of the adjacent nodes is larger than a threshold value;
step S3, utilizing node set L= { L 1 ,…,L m And each node subset in the weighted network is calculated, the communication efficiency degradation rate of the weighted network is calculated, and a plurality of key nodes are selected from the nodes based on the communication efficiency degradation rate threshold value 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, 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: information value, information processing speed, receiving processing time delay of each node, and one or more of bandwidth of a communication link section and transmission time delay of the link section between each node;
s1-2, determining the weight of each node in a quantization mode based on the node communication efficiency influence index; wherein:
determining the node communication efficacy impact index by analyzing communication links in the weighted network, including information value, node dissipation, node betweenness centrality, node communication capacity and node communication efficiency of each node;
and assigning index weights to the communication efficiency influence indexes of the nodes, wherein the node betweenness centrality has the maximum weight, and calculating the weight of each node in the quantization mode based on the index weights.
According to the method of the first aspect of the invention, in said step S2, a sequence d= { D is extracted from said distance sequence D = { 1 ，...，d i ,...，d n-1 The determining of the dip point specifically includes:
based on the distance sequence d= { D 1 ，...，d i ,...，d n-1 Mean μ and standard deviation σ of }, an abnormality threshold τ= (d) is set max -μ)/2σ，d max A maximum value element of the distance sequence;
for the distance sequence d= { D 1 ，...，d i ,...，d n-1 Each element in the list calculates an element outlier g i ＝(d i Mu)/sigma, when g i When ∈τ is not less than, d i Judging the point as a sudden drop point;
based on m-1 of the dip points, in the node sequence s= { T 1 ，…，T n Marking the position where the dip occurs, and according to the position, marking the node sequence s= { T 1 ，...，T n Dividing into node sets L＝{L 1 ,…,L m -wherein:
the nodes preceding the first dip position are partitioned into node subsets L 1 The method comprises the steps of carrying out a first treatment on the surface of the And is also provided with
Nodes following the last bump-down position are divided into node subsets L m The method comprises the steps of carrying out a first treatment on the surface of the And is also provided with
Nodes between adjacent dip locations are divided into node subsets 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, sequentially closing the node subsets L by taking the node subsets as granularity 1 ,...,L m Each time the node in the weighted network is closed, calculating the communication efficiency E of the weighted network 1～i (L 1～i 1.ltoreq.i.ltoreq.m, wherein E 1～i )L 1～i ) Representing the slave node subset L being closed 1 To node subset L i The communication efficiency of the weighted network is further calculated under the condition of all nodes in the current state
Until the communication performance degradation rate is not lower than the communication performance degradation rate threshold; wherein:
e represents the maximum communication efficiency of the weighting network when all nodes are fully loaded;
step S3-2, obtaining a set of closed node subsets, for the last node subset L of the set i Performing a binary search operation to determine the subset L of nodes i Such that:
at the shutdown node subset L 1 To node subset L i-1 All nodes in (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 also provided with
At the shutdown node subset L 1 To node subset L i-1 All nodes in (1), and the node subset L i The situation of the first n+1 nodes of (2)In case the communication efficiency degradation rate is not lower than the communication efficiency degradation rate threshold;
step S3-3, node subset L 1 To node subset L i-1 All nodes in (and the node subset L) i As the key nodes, to form the key node set.
In a second aspect, the invention discloses a system for determining a set of key nodes for weighting network communication efficiency. The system comprises:
a first processing unit configured to determine weights of each node in the weighted network based on communication efficiency of each node in the weighted network, and order each node in a descending order according to the weights of each node, thereby obtaining a node sequence s= { T 1 ，...，T n -wherein the weighting network is derived from an information interaction network;
a second processing unit configured to sequentially calculate the node sequence s= { T 1 ，...，T n Weight difference of adjacent nodes in the sequence to obtain a distance sequence D= { D 1 ，...，d i ,...，d n-1 }, where d i ＝T i -T i-1 And from the distance sequence d= { D 1 ，...，d i ,...，d n-1 Determining a dip point in the sequence of nodes s= { T using the dip point 1 ，...，T n Dividing into node sets l= { L 1 ,…,L m -wherein the number of dip points is m-1, L i Is a subset of the partitioned nodes; wherein:
the abrupt drop point represents that the weight difference of the adjacent nodes is larger than a threshold value;
a third processing unit configured to utilize the node set l= { L 1 ,…,L m And each node subset in the weighted network is calculated, the communication efficiency degradation rate of the weighted network is calculated, and a plurality of key nodes are selected from the nodes based on the communication efficiency degradation rate threshold value to form the key node set.
According to the system of the second aspect of the present 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: information value, information processing speed, receiving processing time delay of each node, and one or more of bandwidth of a communication link section and transmission time delay of the link section between each node;
determining the weight of each node in a quantization mode based on the node communication efficiency influence index; wherein:
determining the node communication efficacy impact index by analyzing communication links in the weighted network, including information value, node dissipation, node betweenness centrality, node communication capacity and node communication efficiency of each node;
and assigning index weights to the communication efficiency influence indexes of the nodes, wherein the node betweenness centrality has the maximum weight, and calculating the weight of each node in the quantization mode based on the index weights.
The system according to the second aspect of the present invention, the second processing unit is specifically configured to perform the following steps to obtain 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 μ and standard deviation σ of }, an abnormality threshold τ= (d) is set max -μ)/2σ，d max A maximum value element of the distance sequence;
for the distance sequence d= { D 1 ，…，d i ,...，d n-1 Each element in the list calculates an element outlier g i ＝(d i Mu)/sigma, when g i When ∈τ is not less than, d i Judging the point as a sudden drop point;
based on m-1 of the dip points, in the node sequence s= { T 1 ，…，T n Marking the position where the dip occurs, and according to the position, marking the node sequence s= { T 1 ，…，T n Dividing into node sets l= { L 1 ,…,L m -wherein:
the nodes preceding the first dip position are partitioned into node subsets L 1 The method comprises the steps of carrying out a first treatment on the surface of the And is also provided with
Nodes following the last bump-down position are divided into node subsets L m The method comprises the steps of carrying out a first treatment on the surface of the And is also provided with
Nodes between adjacent dip locations are divided into node subsets L 2 ,...,L m-1 。
According to the system of the second aspect of the present 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 Each time the node in the weighted network is closed, calculating the communication efficiency E of the weighted network 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 ) Representing the slave node subset L being closed 1 To node subset L i The communication efficiency of the weighted network is further calculated under the condition of all nodes in the current state
Until the communication performance degradation rate is not lower than the communication performance degradation rate threshold; wherein:
e represents the maximum communication efficiency of the weighting network when all nodes are fully loaded;
acquiring a set of closed node subsets, for the last node subset L of the set i Performing a binary search operation to determine the subset L of nodes i Such that:
at the shutdown node subset L 1 To node subset L i-1 All nodes in (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 also provided with
At the shutdown node subset L 1 To node subset L i-1 All nodes in (1), and the node subset L i In the case of the first n+1 nodes, the communication efficiency degradation rate is not lower than the communication efficiency degradation rate threshold;
step S3-3, node subset L 1 To node subset L i-1 All nodes in (and the node subset L) i As the key nodes, to form the key node set.
A third aspect of the invention discloses an electronic device. The electronic device comprises a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in a method for determining a key node set for weighting network communication efficiency according to the first aspect of the present invention.
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, implements the steps of a method for determining a set of key nodes for weighting network communication performance according to the first aspect of the present invention.
In summary, the technical scheme of the invention can effectively solve the problem of how to rapidly select the key node set on the premise of meeting the specific communication efficiency expectation, and further can provide guidance for the problems of robustness assessment, key protection node screening and the like of the network structure. The method is based on an existing network communication efficiency evaluation model, firstly, importance of each node in a network is converted into accurate quantification to weight, then clustering division is carried out according to weight distribution conditions, and finally, an optimal node set meeting the expectations is determined in a step-by-step screening mode.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings which are required in the description of the embodiments or the prior art will be briefly described, it being obvious that the drawings in the description below are some embodiments of the invention and that other drawings may be obtained from these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a method of determining a set of key nodes for weighting 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 weighting network communication performance 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
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The first aspect of the present invention discloses a method for determining a set of key nodes for weighting network communication efficiency. FIG. 1 is a flow chart of a method for evaluating communication performance of a weighting-oriented network according to an embodiment of the present invention; as shown in fig. 1, the method includes:
step S1, determining the weight of each node based on the communication efficiency of each node in the weighted network, and ordering each node in a descending order according to the weight of each node, thereby obtaining a node sequence S= { T 1 ，…，T n -wherein the weighting network is derived from an information interaction network;
step S2, sequentially calculating the node sequence S= { T 1 ，...，T n Weight difference of adjacent nodes in the sequence to obtain a distance sequence D= { D 1 ，...，d i ,...，d n-1 }, where d i ＝T i -T i-1 And from the distance sequence d= { D 1 ，…，d i ,…，d n-1 Determining a dip point in the sequence of nodes s= { T using the dip point 1 ，…，T n Dividing into sectionsPoint set L= { L 1 ,…,L m -wherein the number of dip points is m-1, L i Is a subset of the partitioned nodes; wherein:
the abrupt drop point represents that the weight difference of the adjacent nodes is larger than a threshold value;
step S3, utilizing node set L= { L 1 ,…,L m And each node subset in the weighted network is calculated, the communication efficiency degradation rate of the weighted network is calculated, and a plurality of key nodes are selected from the nodes based on the communication efficiency degradation rate threshold value to form the key node set.
In step S1, based on the communication efficiency of each node in the weighted network, determining the weight of each node, and ordering each node in descending order according to the weight of each node, thereby obtaining a node sequence s= { T 1 ，…，T n -wherein the weighting network is derived from an information interaction network.
In some embodiments, the step S1 specifically includes:
step S1-1, 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: information value, information processing speed, receiving processing time delay of each node, and one or more of bandwidth of a communication link section and transmission time delay of the link section between each node;
s1-2, determining the weight of each node in a quantization mode based on the node communication efficiency influence index; wherein:
determining the node communication efficacy impact index by analyzing communication links in the weighted network, including information value, node dissipation, node betweenness centrality, node communication capacity and node communication efficiency of each node;
and assigning index weights to the communication efficiency influence indexes of the nodes, wherein the node betweenness centrality has the maximum weight, and calculating the weight of each node in the quantization mode based on the index weights.
Specifically, the nodes are connected by edges, the edges representing communication link segments between the nodes, one communication link (from the first communication node to the tenth communication node) may route through other communication nodes (the fifth communication node, the eighth communication node) and multiple 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:
determining, for each communication node, a node weight value of the communication node of the information interaction network based on values of dimensions of information value of the communication node (information value of communication information generated by the communication node), information processing rate, and reception processing delay;
determining, for each communication link segment, a link segment weight value of the communication link segment based on a bandwidth of the communication link segment and a link segment transmission delay, where the link segment weight value is used as one link segment weight factor of the communication nodes at two ends of the communication link segment, and each communication node determines, after acquiring all link segment weight factors associated with the link segment weight factors, a node link weight value of the communication node (for example, 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 after acquiring link segment weight values of the communication link segments between the third communication node, the fifth communication node, and the twentieth communication node, respectively, the three link segment weight values are used as three link segment weight factors of the first communication node, and after summing, the node link segment weight values of the first communication node are obtained;
and carrying out summation processing on the node weight value and the node link section weight value in the information interaction network to obtain the weight of each communication node in the information interaction network, thereby realizing conversion of the information interaction network into the weighting network.
In step S1-2, the weighting network is analyzedDetermining the communication performance impact index of the node i, including the information value v of each node i Node maintenance cost c i Center of node betweenness b i Node communication capability p i Communication efficiency e with node i The method comprises the steps of carrying out a first treatment on the surface of the Specifically, there is a central server or data processing unit/module in the network to obtain and store the above-mentioned index attributes of each node.
In step S1-2, from the standpoint of influencing the communication performance, since the evaluation models and measurement units of the respective indexes are different, it is necessary to assign index weights and normalization processes to the respective node communication performance-influencing indexes, and referring to a typical network node importance evaluation method, in step S1-2, the node betweenness centrality is assigned with the maximum weight w 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 The method comprises the steps of carrying out a first treatment on the surface of the Calculating the weight w of the node i by the quantization mode based on the index weight 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 calculated in turn 1 ，...，T n Weight difference of adjacent nodes in the sequence to obtain a distance sequence D= { D 1 ，…，d i ,...，d n-1 }, where d i ＝T i -T i-1 And from the distance sequence d= { D 1 ，...，d i ,...，d n-1 Determining a dip point in the sequence of nodes s= { T using the dip point 1 ，...，T n Dividing into node sets l= { L 1 ,…,L m -wherein the number of dip points is m-1, L i Is a subset of the partitioned nodes; wherein: the dip point characterizes that the weight difference of the adjacent nodes is larger than a threshold value.
In some embodiments, in the step S2, from the distance sequence d= { D 1 ，...，d i ,...，d n-1 The determining of the dip point specifically includes:
based on the distance sequence d= { D 1 ，...，d i ,...，d n-1 Mean μ and standard deviation σ of }, an abnormality threshold τ= (d) is set max -μ)/2σ，d max A maximum value element of the distance sequence;
for the distance sequence d= { D 1 ，...，d i ,...，d n-1 Each element in the list calculates an element outlier g i ＝(d i Mu)/sigma, when g i When ∈τ is not less than, d i Judging the points as 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 1 ，...，T n Marking the position where the dip occurs, and according to the position, marking the node sequence s= { T 1 ，...，T n Dividing into node sets l= { L 1 ,…,L m -wherein:
the nodes preceding the first dip position are partitioned into node subsets L 1 The method comprises the steps of carrying out a first treatment on the surface of the And is also provided with
Nodes following the last bump-down position are divided into node subsets L m The method comprises the steps of carrying out a first treatment on the surface of the And is also provided with
Nodes between adjacent dip locations are divided into node subsets 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, the anomaly threshold value is τ= (D) max -μ)/2σ；
(3) Sequentially calculating the abnormal value g of each element in D i ＝(D i -μ)/σ；
(4) If g i Not less than τ, d i Marked as outliers, description T i And T i-1 There is a sudden drop in value between nodes, S= { T 1 ，...，T n Dividing into two sets { T } 1 ，…，T i Sum { 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 is utilized 1 ,…,L m And each node subset in the weighted network is calculated, the communication efficiency degradation rate of the weighted network is calculated, and a plurality of key nodes are selected from the nodes based on the communication efficiency degradation rate threshold value to form the key node set.
In some embodiments, the step S3 specifically includes:
step S3-1, sequentially closing the node subsets L by taking the node subsets as granularity 1 ,...,L m Each time the node in the weighted network is closed, calculating the communication efficiency E of the weighted network 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 ) Representing the slave node subset L being closed 1 To node subset L i The communication efficiency of the weighted network is further calculated under the condition of all nodes in the current state
Until the communication performance degradation rate is not lower than the communication performance degradation rate threshold; wherein:
e represents the maximum communication efficiency of the weighting network when all nodes are fully loaded;
step S3-2, obtaining a set of closed node subsets, for the last node subset L of the set i Performing a binary search operation to determine the subset L of nodes i Such that:
at the shutdown node subset L 1 To node subset L i-1 All nodes in (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 also provided with
At the shutdown node subset L 1 To node subset L i-1 All nodes in (1), and the node subset L i In the case of the first n+1 nodes, the communication efficiency degradation rate is not lower than the communication efficiency degradation rate threshold;
step S3-3, node subset L 1 To node subset L i-1 All nodes in (and the node subset L) i As the key nodes, to form the key node set.
Specifically, a key node optimization algorithm is provided, and a node set L= { L is selected 1 ,…,L m Node L 1 Substituting and calculating the communication efficiency degradation rate
Wherein r is i For node value, B i For network betweenness centrality, 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, which 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, k; n is n jk (i) The number of nodes i passing through the shortest path between nodes j and k is indicated. If eta < c, collecting the nodes L 2 Adding the node set in the closed state, repeating calculation until eta is more than or equal to c, and selecting the node set L 1～i Last L of (3) i And searching the node set with the communication efficiency reduction rate closest to the threshold value by using a binary search method or a sequential search method as a key node set.
In a second aspect, the invention discloses a system for determining a set of key nodes for weighting network communication efficiency. FIG. 2 is a block diagram of a system for determining a set of key nodes for weighting network communication performance 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 weights of the respective nodes based on communication performances of the respective nodes in the weighted network, and order the respective nodes in descending order according to the weights of the respective nodes, thereby obtaining a node sequence s= { T 1 ，...，T n -wherein saidThe weighting network is obtained according to the information interaction network;
a second processing unit 202 configured to sequentially calculate the node sequence s= { T 1 ，...，T n Weight difference of adjacent nodes in the sequence to obtain a distance sequence D= { D 1 ，...，d i ,...，d n-1 }, where d i ＝T i -T i-1 And from the distance sequence d= { D 1 ，...，d i ,...，d n-1 Determining a dip point in the sequence of nodes s= { T using the dip point 1 ，...，T n Dividing into node sets l= { L 1 ,…,L m -wherein the number of dip points is m-1, L i Is a subset of the partitioned nodes; wherein:
the abrupt drop point represents that the weight difference of the adjacent nodes is larger than a threshold value;
a third processing unit 203 configured to utilize a node set l= { L 1 ,…,L m And each node subset in the weighted network is calculated, the communication efficiency degradation rate of the weighted network is calculated, and a plurality of key nodes are selected from the nodes based on the communication efficiency degradation rate threshold value to form the key node set.
The system according to 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: information value, information processing speed, receiving processing time delay of each node, and one or more of bandwidth of a communication link section and transmission time delay of the link section between each node;
determining the weight of each node in a quantization mode based on the node communication efficiency influence index; wherein:
determining the node communication efficacy impact index by analyzing communication links in the weighted network, including information value, node dissipation, node betweenness centrality, node communication capacity and node communication efficiency of each node;
and assigning index weights to the communication efficiency influence indexes of the nodes, wherein the node betweenness centrality has the maximum weight, and calculating the weight of each node in the quantization mode based on the index weights.
The system according to the second aspect of the present invention, the second processing unit 202 is specifically configured to perform the following steps to obtain 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 μ and standard deviation σ of }, an abnormality threshold τ= (d) is set max -μ)/2σ，d max A maximum value element of the distance sequence;
for the distance sequence d= { D 1 ，...，d i ,...，d n-1 Each element in the list calculates an element outlier g i ＝(d i Mu)/sigma, when g i When ∈τ is not less than, d i Judging the point as a sudden drop point;
based on m-1 of the dip points, in the node sequence s= { T 1 ，…，T n Marking the position where the dip occurs, and according to the position, marking the node sequence s= { T 1 ，…，T n Dividing into node sets l= { L 1 ,…,L m -wherein:
the nodes preceding the first dip position are partitioned into node subsets L 1 The method comprises the steps of carrying out a first treatment on the surface of the And is also provided with
Nodes following the last bump-down position are divided into node subsets L m The method comprises the steps of carrying out a first treatment on the surface of the And is also provided with
Nodes between adjacent dip locations are divided into node subsets L 2 ,...,L m-1 。
The system according to the second aspect of the present invention, the third processing unit specific 203 is configured to perform:
sequentially closing the node subsets L by taking the node subsets as granularity 1 ,...,L m Each time the node in the weighted network is closed, calculating the communication efficiency E of the weighted network 1～i (L 1～i )，1≤i≤m wherein E is 1～i (L 1～i ) Representing the slave node subset L being closed 1 To node subset L i The communication efficiency of the weighted network is further calculated under the condition of all nodes in the current state
Until the communication performance degradation rate is not lower than the communication performance degradation rate threshold; wherein:
e represents the maximum communication efficiency of the weighting network when all nodes are fully loaded;
acquiring a set of closed node subsets, for the last node subset L of the set i Performing a binary search operation to determine the subset L of nodes i Such that:
at the shutdown node subset L 1 To node subset L i-1 All nodes in (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 also provided with
At the shutdown node subset L 1 To node subset L i-1 All nodes in (1), and the node subset L i In the case of the first n+1 nodes, the communication efficiency degradation rate is not lower than the communication efficiency degradation rate threshold;
step S3-3, node subset L 1 To node subset L i-1 All nodes in (and the node subset L) i As the key nodes, to form the key node set.
A third aspect of the invention discloses an electronic device. The electronic device comprises a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in a method for determining a key node set for weighting network communication efficiency according to the first aspect of the present invention.
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 by a system bus. Wherein the processor of the electronic device is configured to provide computing and control capabilities. The memory of the electronic device includes 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 the operating system and computer programs in the non-volatile storage media. The communication interface of the electronic device is used for conducting wired or wireless communication with an external terminal, and the wireless communication can be achieved 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, can also be keys, a track ball or a touch pad arranged on the shell of the electronic equipment, and can also be an external keyboard, a touch pad or a mouse and the like.
Those skilled in the art will appreciate that the structure shown in fig. 3 is merely a structural diagram of a portion related to the technical solution of the present disclosure, and does not constitute a limitation of the electronic device to which the present application is applied, and that a specific electronic device may include more or less components than those shown in the drawings, or may combine some components, or have different component arrangements.
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, implements the steps of a method for determining a set of key nodes for weighting network communication performance according to the first aspect of the present invention.
In summary, the technical scheme of the invention can effectively solve the problem of how to rapidly select the key node set on the premise of meeting the specific communication efficiency expectation, and further can provide guidance for the problems of robustness assessment, key protection node screening and the like of the network structure. The method is based on an existing network communication efficiency evaluation model, firstly, importance of each node in a network is converted into accurate quantification to weight, then clustering division is carried out according to weight distribution conditions, and finally, an optimal node set meeting the expectations is determined in a step-by-step screening mode.
Note that the technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be regarded as the scope of the description. The above examples merely represent a few embodiments of the present application, which are described in more detail and are not to be construed as limiting the scope of the invention. It should be noted that it would be apparent to those skilled in the art that various modifications and improvements could be made without departing from the spirit of the present application, which would be within the scope of the present application. Accordingly, the scope of protection of the present application is to be determined by the claims appended hereto.
Claims (10)
1. A method of determining a set of key nodes for a weighted network communication performance, the method comprising:
step S1, determining the weight of each node based on the communication efficiency of each node in the weighted network, and ordering each node in a descending order according to the weight of each node, thereby obtaining a node sequence S= { T 1 ，...，T n -wherein the weighting network is derived from an information interaction network;
step S2, sequentially calculating the node sequence S= { T 1 ，...，T n Weight difference of adjacent nodes in the sequence to obtain a distance sequence D= { D 1 ，...，d i ,...，d n-1 }, where d i ＝T i -T i-1 And from the distance sequence d= { D 1 ，...，d i ,...，d n-1 Determining a dip point in the sequence of nodes s= { T using the dip point 1 ，...，T n Dividing into node sets l= { L 1 ,…,L m -wherein the number of dip points is m-1, L i Is a subset of the partitioned nodes; wherein:
the abrupt drop point represents that the weight difference of the adjacent nodes is larger than a threshold value;
step S3, utilizing node set L= { L 1 ,...,L m And each node subset in the weighted network is calculated, the communication efficiency degradation rate of the weighted network is calculated, and a plurality of key nodes are selected from the nodes based on the communication efficiency degradation rate threshold value to form the key node set.
2. The method of claim 1, wherein the step S1 specifically includes:
step S1-1, 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: information value, information processing speed, receiving processing time delay of each node, and one or more of bandwidth of a communication link section and transmission time delay of the link section between each node;
s1-2, determining the weight of each node in a quantization mode based on the node communication efficiency influence index; wherein:
determining the node communication efficacy impact index by analyzing communication links in the weighted network, including information value, node dissipation, node betweenness centrality, node communication capacity and node communication efficiency of each node;
and assigning index weights to the communication efficiency influence indexes of the nodes, wherein the node betweenness centrality has the maximum weight, and calculating the weight of each node in the quantization mode based on the index weights.
3. The method according to claim 2, wherein in said step S2, a set of key nodes for weighted network communication performance is determined from said distance sequence d= { D 1 ，...，d i ,...，d n-1 The determining of the dip point specifically includes:
based on the distance sequence d= { D 1 ，...，d i ,...，d n-1 Mean μ and standard deviation σ of }, an abnormality threshold τ= (d) is set max -μ)/2σ，d max A maximum value element of the distance sequence;
for the distance sequence d= { D 1 ，...，d i ,...，d n-1 Each element in the list calculates an element outlier g i ＝(d i Mu)/sigma, when g i When ∈τ is not less than, d i Judging the point as a sudden drop point;
based on m-1 of the dip points, in the node sequence s= { T 1 ，...，T n Marking the position where the dip occurs, and according to the position, marking the node sequence s= { T 1 ，...，T n Dividing into node sets l= { L 1 ,…,L m -wherein:
the nodes preceding the first dip position are partitioned into node subsets L 1 The method comprises the steps of carrying out a first treatment on the surface of the And is also provided with
Nodes following the last bump-down position are divided into node subsets L m The method comprises the steps of carrying out a first treatment on the surface of the And is also provided with
Nodes between adjacent dip locations are divided into node subsets L 2 ,...,L m-1 。
4. A method for determining a set of key nodes for weighting network communication capabilities according to claim 3, wherein said step S3 specifically comprises:
step S3-1, sequentially closing the node subsets L by taking the node subsets as granularity 1 ,...,L m Each time the node in the weighted network is closed, calculating the communication efficiency E of the weighted network 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 degradation rate of the weighted network in the current state
Until the communication performance degradation rate is not lower than the communication performance degradation rate threshold; wherein:
E 1～i (L 1～i ) Representing the slave node subset L being closed 1 To node subset L i Is a member of the group (C)The communication efficiency of the weighting network is improved under the condition of nodes;
e represents the maximum communication efficiency of the weighting network when all nodes are fully loaded;
step S3-2, obtaining a set of closed node subsets, for the last node subset L of the set i Performing a binary search operation to determine the subset L of nodes i Such that:
at the shutdown node subset L 1 To node subset L i-1 All nodes in (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 also provided with
At the shutdown node subset L 1 To node subset L i-1 All nodes in (1), and the node subset L i In the case of the first n+1 nodes, the communication efficiency degradation rate is not lower than the communication efficiency degradation rate threshold;
step S3-3, node subset L 1 To node subset L i-1 All nodes in (and the node subset L) i As the key nodes, to form the key node set.
5. A system for determining a set of key nodes for a weighted network communication performance, the system comprising:
a first processing unit configured to determine weights of the respective nodes based on communication performances of the respective nodes in the weighted network, and order the respective nodes in descending order according to the weights of the respective nodes, thereby obtaining a node sequence s= { T 1 ，…，T n -wherein the weighting network is derived from an information interaction network;
a second processing unit configured to sequentially calculate the node sequence s= { T 1 ，...，T n Weight difference of adjacent nodes in the sequence to obtain a distance sequence D= { D 1 ，...，d i ,...，d n-1 }, where d i ＝T i -T i-1 And from the distance sequence d= { D 1 ，...，d i ,...，d n-1 Determining a dip point in the sequence of nodes s= { T using the dip point 1 ，...，T n Dividing into node sets l= { L 1 ,…,L m -wherein the number of dip points is m-1, L i Is a subset of the partitioned nodes; wherein:
the abrupt drop point represents that the weight difference of the adjacent nodes is larger than a threshold value;
a third processing unit configured to utilize the node set l= { L 1 ,…,L m And each node subset in the weighted network is calculated, the communication efficiency degradation rate of the weighted network is calculated, and a plurality of key nodes are selected from the nodes based on the communication efficiency degradation rate threshold value to form the key node set.
6. The system of claim 5, wherein 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: information value, information processing speed, receiving processing time delay of each node, and one or more of bandwidth of a communication link section and transmission time delay of the link section between each node;
determining the weight of each node in a quantization mode based on the node communication efficiency influence index; wherein:
determining the node communication efficacy impact index by analyzing communication links in the weighted network, including information value, node dissipation, node betweenness centrality, node communication capacity and node communication efficiency of each node;
and assigning index weights to the communication efficiency influence indexes of the nodes, wherein the node betweenness centrality has the maximum weight, and calculating the weight of each node in the quantization 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 for weighted network communication performance 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 μ and standard deviation σ of }, an abnormality threshold τ= (d) is set max -μ)/2σ，d max A maximum value element of the distance sequence;
for the distance sequence d= { D 1 ，...，d i ,...，d n-1 Each element in the list calculates an element outlier g i ＝(d i Mu)/sigma, when g i When ∈τ is not less than, d i Judging the point as a sudden drop point;
based on m-1 of the dip points, in the node sequence s= { T 1 ，…，T n Marking the position where the dip occurs, and according to the position, marking the node sequence s= { T 1 ，...，T n Dividing into node sets l= { L 1 ,…,L m -wherein:
the nodes preceding the first dip position are partitioned into node subsets L 1 The method comprises the steps of carrying out a first treatment on the surface of the And is also provided with
Nodes following the last bump-down position are divided into node subsets L m The method comprises the steps of carrying out a first treatment on the surface of the And is also provided with
Nodes between adjacent dip locations are divided into node subsets 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 Each time the node in the weighted network is closed, calculating the communication efficiency E of the weighted network 1～i (L 1～i ) I is more than or equal to 1 and less than or equal to m; advancing oneStep calculating the communication efficiency degradation rate of the weighted network in the current state
Until the communication performance degradation rate is not lower than the communication performance degradation rate threshold; wherein:
E 1～i (L 1～i ) Representing the slave node subset L being closed 1 To node subset L i In the case of all nodes of the weighting network;
e represents the maximum communication efficiency of the weighting network when all nodes are fully loaded;
acquiring a set of closed node subsets, for the last node subset L of the set i Performing a binary search operation to determine the subset L of nodes i Such that:
at the shutdown node subset L 1 To node subset L i-1 All nodes in (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 also provided with
At the shutdown node subset L 1 To node subset L i-1 All nodes in (1), and the node subset L i In the case of the first n+1 nodes, the communication efficiency degradation rate is not lower than the communication efficiency degradation rate threshold;
step S3-3, node subset L 1 To node subset L i-1 All nodes in (and the node subset L) i As the key nodes, to form the key node set.
9. An electronic device comprising a memory storing a computer program and a processor implementing the steps of the method of any one of claims 1 to 4 for determining a set of key nodes for a weighted network communication performance when the computer program is executed.
10. A computer readable storage medium, characterized in that the computer readable storage medium has stored thereon a computer program which, when executed by a processor, is adapted to determine the steps in a method of determining a set of key nodes of a weighted network communication performance according to any of claims 1 to 4.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210387176.1A CN114826936B (en) | 2022-04-14 | 2022-04-14 | Method and system for determining key node set of weighted network communication efficiency |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210387176.1A CN114826936B (en) | 2022-04-14 | 2022-04-14 | Method and system for determining key node set of weighted network communication efficiency |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114826936A CN114826936A (en) | 2022-07-29 |
| CN114826936B true CN114826936B (en) | 2023-06-09 |
Family
ID=82536888
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210387176.1A Active CN114826936B (en) | 2022-04-14 | 2022-04-14 | Method and system for determining key node set of weighted network communication efficiency |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114826936B (en) |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2019007985A1 (en) * | 2017-07-05 | 2019-01-10 | Technische Universität Darmstadt | Method and device for flexible ranking system for information in a multi-linked network |
| CN109379220A (en) * | 2018-10-10 | 2019-02-22 | 太原理工大学 | The method that complex network key node cluster based on Combinatorial Optimization excavates |
| CN109471994A (en) * | 2018-10-22 | 2019-03-15 | 西南石油大学 | Network key nodal test method and system |
| CN112116198A (en) * | 2020-08-04 | 2020-12-22 | 西安交通大学 | Data-driven process industrial state perception network key node screening method |
| CN114090860A (en) * | 2021-11-12 | 2022-02-25 | 云南大学 | Method and system for determining importance of weighted network node |
-
2022
- 2022-04-14 CN CN202210387176.1A patent/CN114826936B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2019007985A1 (en) * | 2017-07-05 | 2019-01-10 | Technische Universität Darmstadt | Method and device for flexible ranking system for information in a multi-linked network |
| CN109379220A (en) * | 2018-10-10 | 2019-02-22 | 太原理工大学 | The method that complex network key node cluster based on Combinatorial Optimization excavates |
| CN109471994A (en) * | 2018-10-22 | 2019-03-15 | 西南石油大学 | Network key nodal test method and system |
| CN112116198A (en) * | 2020-08-04 | 2020-12-22 | 西安交通大学 | Data-driven process industrial state perception network key node screening method |
| CN114090860A (en) * | 2021-11-12 | 2022-02-25 | 云南大学 | Method and system for determining importance of weighted network node |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114826936A (en) | 2022-07-29 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US10846052B2 (en) | Community discovery method, device, server and computer storage medium | |
| CN110929047B (en) | Knowledge graph reasoning method and device for focusing on neighbor entity | |
| Tran et al. | A multivariate fuzzy time series resource forecast model for clouds using LSTM and data correlation analysis | |
| US20150254554A1 (en) | Information processing device and learning method | |
| CN111867049B (en) | Positioning method, positioning device and storage medium | |
| Mitrovic et al. | Low-dimensional models for compressed sensing and prediction of large-scale traffic data | |
| CN110796399B (en) | Resource allocation method and device based on block chain | |
| CN113282409B (en) | Edge calculation task processing method and device and computer equipment | |
| CN111128307A (en) | Metabolic path prediction method and device, terminal device and readable storage medium | |
| Fan et al. | A multi-state reliability evaluation model for P2P networks | |
| WO2015124424A1 (en) | Method and system for link prediction in mobile computing | |
| CN114399321A (en) | Business system stability analysis method, device and equipment | |
| Bharti et al. | Clustering‐based resource discovery on Internet‐of‐Things | |
| CN111209930A (en) | Method and device for generating credit granting strategy and electronic equipment | |
| Ponmalar et al. | Machine Learning Based Network Traffic Predictive Analysis | |
| CN113158435B (en) | Complex system simulation running time prediction method and device based on ensemble learning | |
| Xin et al. | Vision paper: causal inference for interpretable and robust machine learning in mobility analysis | |
| CN114826936B (en) | Method and system for determining key node set of weighted network communication efficiency | |
| CN109343952B (en) | Bayesian network determination method, Bayesian network determination device, storage medium and electronic device | |
| CN115629883A (en) | Resource prediction method, resource prediction device, computer equipment and storage medium | |
| CN113282417B (en) | Task allocation method and device, computer equipment and storage medium | |
| CN111934938B (en) | Flow network key node identification method and device based on multi-attribute information fusion | |
| CN115860856A (en) | Data processing method and device, electronic equipment and storage medium | |
| CN115310355A (en) | Multi-energy coupling-considered multi-load prediction method and system for comprehensive energy system | |
| Abdullah et al. | Predicting qos for web service recommendations based on reputation and location clustering with collaborative filtering |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |