CN115622902A - Telecommunication network node importance calculation method based on network structure and node value - Google Patents
Telecommunication network node importance calculation method based on network structure and node value Download PDFInfo
- Publication number
- CN115622902A CN115622902A CN202211629129.XA CN202211629129A CN115622902A CN 115622902 A CN115622902 A CN 115622902A CN 202211629129 A CN202211629129 A CN 202211629129A CN 115622902 A CN115622902 A CN 115622902A
- Authority
- CN
- China
- Prior art keywords
- node
- nodes
- network
- importance
- index
- 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.)
- Granted
Links
- 238000004364 calculation method Methods 0.000 title description 9
- 238000000034 method Methods 0.000 claims abstract description 83
- 238000012163 sequencing technique Methods 0.000 claims abstract description 6
- 239000013598 vector Substances 0.000 claims description 35
- 239000011159 matrix material Substances 0.000 claims description 28
- 238000010606 normalization Methods 0.000 claims description 3
- 239000000203 mixture Substances 0.000 claims 2
- 230000004927 fusion Effects 0.000 abstract description 3
- 238000004422 calculation algorithm Methods 0.000 description 15
- 238000005065 mining Methods 0.000 description 8
- 230000005540 biological transmission Effects 0.000 description 7
- 238000011156 evaluation Methods 0.000 description 6
- 238000011160 research Methods 0.000 description 6
- 230000008859 change Effects 0.000 description 5
- 230000008602 contraction Effects 0.000 description 5
- 238000004891 communication Methods 0.000 description 4
- 238000013459 approach Methods 0.000 description 3
- 238000013461 design Methods 0.000 description 3
- 230000000694 effects Effects 0.000 description 3
- 230000008569 process Effects 0.000 description 3
- 238000004458 analytical method Methods 0.000 description 2
- 238000009833 condensation Methods 0.000 description 2
- 230000005494 condensation Effects 0.000 description 2
- 238000000354 decomposition reaction Methods 0.000 description 2
- 238000012217 deletion Methods 0.000 description 2
- 230000037430 deletion Effects 0.000 description 2
- 238000005295 random walk Methods 0.000 description 2
- 101000911753 Homo sapiens Protein FAM107B Proteins 0.000 description 1
- 206010033799 Paralysis Diseases 0.000 description 1
- 102100026983 Protein FAM107B Human genes 0.000 description 1
- 238000013528 artificial neural network Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000015556 catabolic process Effects 0.000 description 1
- 238000007621 cluster analysis Methods 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000002708 enhancing effect Effects 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 238000000556 factor analysis Methods 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 238000003012 network analysis Methods 0.000 description 1
- 230000002093 peripheral effect Effects 0.000 description 1
- 238000012847 principal component analysis method Methods 0.000 description 1
- 238000007619 statistical method Methods 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
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
- H04L41/142—Network analysis or design using statistical or mathematical methods
-
- 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)
- Physics & Mathematics (AREA)
- Algebra (AREA)
- General Physics & Mathematics (AREA)
- Mathematical Analysis (AREA)
- Mathematical Optimization (AREA)
- Mathematical Physics (AREA)
- Probability & Statistics with Applications (AREA)
- Pure & Applied Mathematics (AREA)
- Data Exchanges In Wide-Area Networks (AREA)
Abstract
The invention discloses a method for calculating the importance of nodes of a telecommunication network based on a network structure and node values, which relates to the technical field of important nodes of the network and comprises the following steps of 1) establishing a network topology model of the telecommunication network; 2) Calculating local information indexes of the nodes according to the topological structure of the telecommunication network; 3) Calculating the global information index of the node according to the topological structure of the telecommunication network; 4) Calculating the flow scale index of the node according to the service condition of the telecommunication network; 5) Calculating a node bearing key service information index according to the service condition of the telecommunication network; 6) Calculating the node importance according to the local information index, the global information index, the flow scale index and the bearing key service information index; 7) Sorting the importance of the nodes; 8: and outputting the sequencing result of the node importance. The invention effectively fuses the local attribute, the global attribute and the value of the network node in a targeted manner, and determines the actual importance of the node according to the closeness obtained by fusion.
Description
Technical Field
The invention relates to the technical field of network important node mining, in particular to the technical field of telecommunication network node importance calculation methods based on network structures and node values.
Background
The complex network theory can represent various complex systems in shape and color as the structure of the network, and the attributes and functions of nodes in the network have great influence on the growth and evolution of the network. Important node mining is one of the most core problems in the fields of network attack, network flow propagation and control and the like, and the control force and the influence force of the few nodes on the network are beyond imagination. We have understood that the root cause of the role differences of network nodes is heterogeneity of the network structure, but it is difficult to find out which nodes are the most important ones, and researchers have proposed a very large number of important node mining methods which stand from respective standpoints and provide alternative solutions for us to explore the importance of nodes of a telecommunication network in different contexts from different perspectives. Looking at the various approaches, it can be seen that they essentially follow the following ideas.
1) The method is the simplest and most intuitive method and essentially considers the importance of the nodes from the local environment of the nodes. The local environment of the node comprises direct neighbors, indirect neighbors and edges, the clustering coefficient of the node and the like. The sequencing method based on the node neighbors mainly comprises a degree centrality method, a semi-local centrality method, a k-shell decomposition method and the like. The direct neighbor number of the node is considered by the medium centrality, and the information of the four-layer neighbor of the node is considered by the semi-local centrality. k-shell decomposition can be seen as an extension of centrality, which defines the importance of nodes according to their location in the network, considering that nodes that are more important at the core are more important. The k-shell algorithm assigns a value to each node starting from the edge node of the network to the center of the network, with the closer to the center the value of the k-shell is greater. At the same time, the influence and the transmissibility of the nodes will also increase with this value. In addition, while the number of neighbors is considered, some mining methods are explored from the perspective of mutual enhancement of the importance of neighbor nodes, which mainly refer to a series of methods based on feature vectors. In addition, the proximity centrality, the Katz centrality and the information index evaluate the importance of the nodes in terms of the strength of the connection between the nodes and all the nodes in the global scope.
The node neighbor-based sequencing method mainly considers the node information and the neighbor information, and the indexes are simple in calculation and low in time complexity and can be used for a large-scale network.
2) Based on the path sorting method, the method considers the importance of the node from the position of the node in the path. The method mainly comprises betweenness centrality, communication betweenness centrality, centrifugal centrality, approximate centrality, katz centrality, graph centrality and other path-based mining methods. The betweenness centrality represents the importance of the nodes by using the number of the shortest paths passing through the nodes, can be used in a directed network, is particularly suitable for a network sensitive to traffic transmission, and can be used for optimizing deployment, bottleneck detection and the like of network transmission. The decentralization is to measure the importance degree of a node by using the maximum value of the shortest path between the node and other nodes, and can only be used in a connected network. The approach centrality is the average value of the shortest paths of one node and other nodes to measure the importance degree of the node, and the network flow has the best observation view. Katz centrality considers the number of paths among all node pairs and different enhancing effects of the length of each path on the centrality of one node, a matrix inversion method can simplify operation, time complexity is high, and the method is suitable for networks with few loops. The graph centrality considers the contribution of nodes on the global closed loop to the importance, the shorter the loop is, the larger the contribution is, the degeneracy of the integrity centrality can be reduced, and the graph centrality is suitable for the condition that the number of loops in the network is large and the time complexity is high.
The path-based sequencing method mainly considers network global information, and the indexes are generally high in accuracy, but high in time complexity and not suitable for a large network.
3) Based on a node removal and contraction sorting method, the method considers the importance of nodes from the influence of the nodes on the network function, mainly considers the change of the network structure and the function after the nodes are removed, and mainly comprises the following steps: a node contraction method, a residual near-centrality, a spanning tree method for node deletion, and the like. The node contraction method focuses on the change of the network condensation degree after the nodes are deleted, the network condensation degree is calculated once every node is contracted, and the time complexity is high; the shortest distance method of node deletion and the residual approach centrality mainly concern the change of the average shortest distance in the network, and the time complexity is high; the spanning tree method for deleting the nodes focuses on the change of the network spanning tree after the nodes are deleted, is suitable for a network insensitive to transmission delay, can only be used in a network which is still connected after the nodes are deleted, and has high time complexity.
It can be seen that the most significant characteristic of the sorting method based on node removal and contraction is that the structure of the network is in dynamic change during the sorting process of the important nodes, and the importance of the nodes is often reflected in the destructiveness of the network after the nodes are removed. From the viewpoint of measuring the robustness of the network, once some nodes fail or are removed, the network may be paralyzed or differentiated into several disconnected subnets. In actual life, many infrastructure networks have the risk of 'failure at a bit and breakdown of the whole network'. In order to prevent risks, researchers have proposed many methods to study the changes in the structure and function of the network after the nodes are shrunk or removed, thereby providing a basis for the design and construction of new systems. The method of system science provides new visual angle for us, but the method is limited to small-scale network experiment due to higher computational complexity.
Besides the three types of evaluation algorithms mentioned above, there are a node ranking method based on feature vectors and a node importance ranking method based on random walks. The node sorting method based on the feature vector not only considers the quantity of node neighbors, but also considers the influence of the quality of the node neighbors on the importance of the node, and comprises a feature vector center method and an accumulative nomination method, wherein the two methods are generally used in a undirected network, and the latter convergence is faster; the node importance ranking method based on random walk is mainly a webpage ranking technology based on link relations among webpages, and the link relations among the webpages can be interpreted as mutual correlation and mutual support among the webpages so as to judge the importance degree of the webpages, and typical methods of the method include PageRank, leaderRank, HITS algorithm and the like.
In summary, the existing method for identifying important nodes is mainly based on a network physical topology structure, and various centrality indexes can quantify the importance of the physical topology of the nodes from different angles. The sorting method based on the node neighbors is based on the indexes of network local attributes; while the path-based ranking method and the node removal and contraction-based ranking method are based on the network global property metrics. Nodes have multiple attributes, so that a plurality of node importance identification algorithms exist. When the structure information of the network is related to each node importance degree recognition algorithm, the importance of the node is measured from a certain specific angle to the structure characteristic of a certain aspect of the network, if the structure of the target network is obvious in the aspect, a good effect can be obtained, and the method has certain applicability and certain defects. In order to improve the accuracy of the identification result of the important node, the most ideal method is to fuse all the attributes of the node, but the method is not feasible. On the one hand, the nodes have more unknown attributes to be explored, and on the other hand, the time cost required for fusing too many node attributes is huge. Besides the above-mentioned evaluation algorithms, the comprehensive evaluation method of the mixed index can often achieve better effect in practical application. The method of the mixed index mainly includes a comprehensive weighting method, a fuzzy comprehensive evaluation method, a grey system evaluation method, an analytic hierarchy process, a multivariate statistical analysis method (a principal component analysis method, a factor analysis method, a cluster analysis method, a discriminant analysis method and the like), a TOPSIS method, a neural network method and the like.
Under a complex network environment, on one hand, the theory and the method of the complex network provide a completely new perspective for the problem of identifying important nodes originally belonging to the field of information mining, and on the other hand, the mining of the important nodes has important significance and value for the theoretical research and the application expansion of network science. The existing complex network node importance recognition algorithm research has many places which need further research and improvement. The method mainly comprises the following steps that firstly, theoretical research cannot keep up with application requirements, secondly, structural importance and functional importance are derailed, thirdly, theoretical and experimental derailment is carried out, and requirements for node importance degree identification in different fields are different. Therefore, in the complex network analysis, it is a great challenge to design an effective method for evaluating the importance of the entire network node.
Disclosure of Invention
The invention aims to: in order to solve the technical problems, the invention provides a method for calculating the importance of nodes of a telecommunication network based on a network structure and node values.
The invention specifically adopts the following technical scheme for realizing the purpose:
a telecommunication network node importance calculation method based on network structure and node value comprises the following steps: step 1: establishing a network topology model of a telecommunications network and constructing an adjacent matrix A of an unlicensed network corresponding to the model N*N And weighted network adjacency matrix W N*N N represents the total number of nodes in the entire network; step 2: calculating nodes v according to the topology of the telecommunication network i The local information index L (i); and step 3: computing nodes v according to a telecommunications network topology i The global information index G (i); and 4, step 4: calculating nodes v according to telecommunication network service conditions i Flow size index TS i (ii) a And 5: calculating nodes v according to telecommunication network service conditions i Loaded key service information index BI i (ii) a And 6: according to the local information index L (i), the global information index G (i), the traffic scale index TSi and the loaded key service information index BI i Calculating the importance of the nodes; and 7: sorting the importance of the nodes; and 8: and outputting the sequencing result of the node importance.
Preferably, in step 2, the nodes v are computed according to the topology of the telecommunication network i The local information index L (i) further comprises a adjacency matrix A according to the unweighted network N*N Computing strong connectivity between nodesThe method specifically comprises the following steps:
node v in a computing network i Degree of (d):
wherein j represents a division node v i All other node sequence numbers in the network except for N represents the total number of nodes in the whole network, a ij Representing a node v i And node v j If two nodes are connected, a ij Is 1, otherwise is 0.
Computing connection strength CS between nodes ij The following were used:
in the formula, N (i) and N (j) each represent a node v i And node v j Set of neighbor points of, D i Representing a node v i Degree of (D) j Representing a node v j Degree of (d), current node v i And node v j The neighbors are identical, their connection strength CS ij =1, CS if they do not have a common neighbor ij =0。
Preferably, in step 2, the calculation node v is calculated i The specific method of the local information index L (i) is as follows:
wherein,
in the formula,
，CS ij representing a node v i And node v j The connection strength between the two is j ≠ i, j =1,. N; c (i) of all nodes form a vector C, D (i) is a node v in the network i The degrees of all nodes form a vectorD；K c Vectors, max (m), calculated for all nodes by equation (4) aboveC) Representing slave vectorsCTaking the maximum value of C (i) of all nodes contained in the list, max: (D) Representing slave vectorsDThe degree D (i) of all the nodes contained is the maximum value.
Preferably, in step 3, the nodes v are computed according to the topology of the telecommunication network i Comprises a computing node v i Mesomeric centrality of (c):
in the formula, b jk (i) Representing a node v j And node v k Between via a node v i The number of shortest paths of (c); b jk Is a slave node v j To node v k The total number of all shortest paths between them,Vrepresenting a set of nodes.
Preferably, the network global information index is calculated by the following formulaG(i) It is used to quantify the importance of node locations while distinguishing the tip nodes from the boundary nodes:
in the formula:BC(i) Representing a node v i The mesomeric center of (A) is,BCis an betweenness centrality vector consisting of betweenness centralities BC (i) of each node, max: (BC) Representing slave vectorsBCAnd taking the maximum betweenness centrality of the nodes.
Preferably, in step 4, the node v is calculated according to the traffic situation of the telecommunication network i Flow scale index ofTS i Comprises the following steps:
wherein N is i Representing a node v i Neighbor set of (a), w ij Representing a node v i And node v j The value of the flow in between,Wfor network adjacency matrix W N*N The element is w ij And max (W) denotes a slave network adjacency matrix W N*N The maximum flow value is taken.
Preferably, in step 5, the node v is calculated according to the traffic situation of the telecommunication network i Loaded key service information index BI i The following were used:
wherein,
in the formula, BI i Representing the node v after normalization i The service importance of the bearer; b (i) represents node v i The importance of the traffic actually carried is,brepresents a vector consisting of the traffic importance b (i) of all nodes,max(b) Representing slave vectorsbTaking a maximum vector value;Krepresenting the total number of service types; q (s, d, k) denotes the node v s And node v d In a direction ofkThe number of class services;Idenotes a traffic importance vector, I (k) denoteskThe importance of the class service.
Preferably, in step 6, the local information index L (i), the global information index G (i), and the flow scale index TS are used as the basis i And the key service information index BI of the load i Compute node v i The specific method of importance of (b) is as follows.
1) ComputingInformation entropy of jth importance indicatore j ：
Whereint j Represents the sum of the attribute values under the jth importance index:
2) Calculating the information entropy weight of the jth importance indexew j ：
N represents the number of nodes in the network, the network has m node importance indexes, wherein the ith node v i Is denoted as t ij ，m=4，t i1 ，t i2 ，t i3 ，t i4 Respectively represent local information index L (i), global information index G (i) and flow scale index TS of nodes i And node v i Service importance of bearer BI i To form a node importance index matrixT，The weight of the jth importance index value is:
。
preferably, in step 7, the importance of the nodes is ranked, and the specific steps are as follows:
1) Constructing a matrix of normalized index values
2) Determining a weighting matrix
3) To obtain a positive ideal solution F + Negative ideal solution F - Namely:
4) Calculating the distance D from each node to the positive and negative ideal solutions i + And D i - Namely:
5) Calculate the ith node v i Closeness of (C) i I.e. is node v i The node importance value obtained by fusing the plurality of node importance indexes is as follows:
the invention has the following beneficial effects:
1. the invention analyzes the position of the node in the telecommunication network from the local and global information, the flow scale and the loaded service importance of the node, and quantifies the node importance from four indexes. The invention provides a node importance identification method based on node value and a network topological structure, which can be used for effectively fusing network local attribute, global attribute and network node value in a targeted manner and determining the actual importance of a node according to the closeness obtained by fusion.
2. It can be seen from the research results related to the importance of the traditional network nodes that the existing method for identifying important nodes is mainly based on the network physical topological structure, the importance of the physical topology of the nodes can be quantified from different angles by various centrality indexes, and if only a single centrality index is used for depicting the importance of the nodes, the nodes cannot be comprehensively analyzed. The invention researches and designs a telecommunication network node importance calculation method facing node value and network topological structure by adopting a comprehensive evaluation method of mixed indexes mainly according to advantages and disadvantages of calculation algorithms of various network node importance and combining with the network structure characteristics and service characteristics of a telecommunication network.
Drawings
FIG. 1 is a flow chart of the method 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 some, but not all, embodiments of the present invention.
Example 1
As shown in fig. 1, the present embodiment provides a method for calculating importance of nodes in a telecommunications network based on network structure and node value, including the following steps:
step 1: establishing a network topology model of a telecommunications network and constructing an unauthorized network adjacency matrix corresponding to the model
And weighted network adjacency matrix
And N denotes the total number of nodes in the entire network.
Step 2: and calculating the local information index of the node according to the telecommunication network topological structure.
And calculating the local information index of the telecommunication network by adopting a centrality algorithm.
1) Degree centrality, which means that the degree centrality of a node refers to the node in the networkThe total number of neighbors and the degree centrality characterize the direct influence of the nodes, and the larger the degree of one node is, the more neighbors can be directly influenced, namely the more important the ith node v in the network i The degree of (c) can be defined as:
wherein j represents the i-th node divided by v i The serial numbers of all other nodes in the network except the network, N represents the total number of nodes in the whole network, a ij I.e. the non-weighted network adjacency matrix
Row i and column j of the middle, representing node v i And node v j The connection relation between the two nodes is 1 if the two nodes are connected; otherwise, i is more than or equal to 0,0 and less than or equal to N.
2) The connection strength between nodes, and the node v by analyzing the relation between adjacent nodes i And node v j The strength of the connection between the two nodes is determined by the number of the mutual neighbors of the two nodes, the strength between the two nodes is increased when the two nodes have more mutual neighbors, if the strength of the connection between the nodes is weaker, the node can be a bridge node or a unique path node, and therefore the bridge node or the unique path node in the network can be represented by the strength of the connection between the nodes.
Defining connection strength CS between nodes ij The following were used:
in the formula, N (i) and N (j) each represent a node v i And node v j Set of neighbor points of (D) i Representing a node v i Degree of (D) j Representing a node v j Degree of (d) when node v i And node v j Are identical, their connection strengths
If they do not have a common neighbor, then
。
3) When the local information index of the telecommunication network is calculated by adopting a degree centrality algorithm, the characteristics of the telecommunication network are considered, the scale of neighbor nodes and the connection strength between nodes are reflected by the node degree, the importance of the neighbor nodes is reflected, and a node v is introduced i Local information L (i):
wherein,
in the formula,
，
representing a node v i And node v j The connection strength between the nodes, C (i) of all the nodes form a vectorC(ii) a D (i) is a node v in the network i The degrees of all nodes form a vectorD；K c Vectors calculated for all nodes by the above formula,
representing slave vectorsCAll nodes involved
Taking the maximum value out of the data,
represents from direction toMeasurement ofDThe number of degrees of all nodes involved is taken as the maximum.
And step 3: and calculating the global information index of the node according to the telecommunication network topological structure.
And calculating the global information index of the telecommunication network by adopting an betweenness centrality algorithm.
1) The betweenness centrality is expressed by the following expression:
in the formula, b jk (i) Representing a node v j And node v k Inter-passing node v i The number of shortest paths of (c); b jk To the slave node v j To node v k The total number of all shortest paths in between,Vrepresenting a set of nodes; the betweenness centrality definition considers that if one node is a necessary path for communication between other node pairs in the network, the node has an important position in the network; obviously, the higher the value of the centrality of a node, the greater the influence of the node, and accordingly the more important it is.
2) And when the global information index of the telecommunication network is calculated by adopting an betweenness centrality algorithm, the characteristics of the telecommunication network are considered. The physical topology of the telecommunication network has more boundary nodes and end nodes, the difference of the importance degrees of the two types of nodes cannot be identified only from local information indexes, and actually, the importance degrees of the two types of nodes have larger difference; the boundary nodes are connected with each block in the network, and the removal of the boundary nodes can cause the disconnection of the communication among the blocks and break the network into a plurality of subnets; while the tip node has a small number of connections in the network, removing the tip node has little effect on network connectivity.
To this end, global information G (i) is introduced to quantify the importance of node positions, while distinguishing the tip nodes from the boundary nodes:
in the formula: BC (i) represents node v i The mesomeric center of (A) is,BCis an betweenness centrality vector composed of betweenness centralities of all nodes,max(BC)representing slave vectorsBCThe highest mesomeric centrality is taken. The magnitude of the betweenness centrality depends on the number of shortest paths passing through the node in the whole network, the shortest paths of node pairs between two different blocks need to pass through the boundary node, so that the betweenness centrality of the boundary node is larger, and the peripheral nodes are opposite, so that the trunk node and the boundary node can be distinguished by using the betweenness centrality as a global information index.
And 4, step 4: and calculating the flow scale index of the node according to the service condition of the telecommunication network.
In a telecommunication network, the connection compactness of network nodes can be described by utilizing flow values among the network nodes, the timeliness of the importance mining of the telecommunication network nodes is considered, and the weighted centrality algorithm is adopted to represent the strength of the network flow according to the characteristics of the network topology node importance algorithm. Defining a node v i The flow scale indexes of (1) are as follows:
wherein,
weighted network adjacency matrix for network
,
Representing a node v i Neighbor set of (a), w ij Representing a node v i And node v j Flow value between, no connecting edge between two nodes
，max(W)Representing adjacency matrices from weighted networks
Take the maximum flow value w ij 。
And 5: and calculating the key service information index carried by the node according to the service condition of the telecommunication network.
The telecommunication services transmitted in the telecommunication network have obvious importance difference, whether part of the telecommunication services with higher importance are normally transmitted determines whether the telecommunication network can safely and stably operate, but the flow required by the transmission of the part of the services is usually smaller, so the importance of the node cannot be simply judged according to the flow of the node, and in the actual telecommunication network, the telecommunication services with lower importance, such as video monitoring and the like, do not directly participate in telecommunication control, but the flow required by the transmission is larger; whereas the traffic associated with the telecommunications control has a higher traffic importance but the traffic carrying the traffic is not significant.
The service importance is usually calculated by an analytic hierarchy process, the requirements of different telecommunication services on the same transmission performance index (such as time delay and bit error rate) have differences, and more important services require a communication system to provide higher priority to meet the service transmission requirement, so that the telecommunication services can be ensured to be operated safely and stably.
Defining a node v i The service importance of the bearer is as follows:
wherein,
in the formula, BI i Representing the node v after normalization i The service importance of the bearer; b (i) represents node v i Practical bearingThe importance of the service being carried is determined,brepresents a vector consisting of traffic importance b (i) of all nodes, max (b) represents a slave vectorbTaking a maximum vector value; k represents the total number of service types; q (s, d, k) denotes the node v s And node v d The number of kth class of traffic present in between;Irepresents the importance vector of the service formed by I (k), and I (k) represents the importance degree of the kth class of service, usually judged by experts or obtained according to historical data, so that when the node v i When the sending, receiving and forwarding traffic is large or the importance of the traffic is higher, the corresponding BI i The larger the value.
Step 6: according to the local information index L (i), the global information index G (i) and the flow scale index TS i And bearing key service information index
Compute node v i The importance of (c).
If N nodes are shared in the telecommunication network, m node importance indexes are required to be fused to obtain the actual importance of each node, wherein the jth importance index value of the ith node is recorded as t ij Respectively calculating a plurality of importance index values corresponding to each node through the node importance indexes to obtain a node importance index matrixT. Let the weight of the jth index be
And m represents the number of indexes. The invention has 4 importance indexes, including: the node v calculated in the step 2 i The local information L (i), the node v calculated in step 3 i The network global information index G (i), the node v calculated in step 4 i Flow rate scale index TS i And the node v obtained by calculation in step 5 i Importance of a service to a bearer
So that the value of m is 4, and therefore the j-th importance index value t of the i-th node ij J has a value of 1,2,3,4, which represents the above 4And (4) an importance index.
The entropy weighting method is a traditional index weight calculation method, the amount of information provided by the indexes is measured by using the data difference in the indexes, and the larger the data difference is, the more the indexes can clearly quantify the node importance.
The information entropy weight of each index is calculated by an entropy weight method, and the method specifically comprises the following steps:
1) Calculating the information entropy e of the jth index j ：
Wherein t is j Represents the sum of the attribute values under the j-th index:
2) Calculating information entropy weight ew of j index j ：
When the entropy value of the node index is smaller, the larger the information quantity provided by the index is, the larger the weight is; similarly, when the entropy of the node index is larger, the amount of information provided by the index is smaller, and the weight is smaller.
And 7: using TOPSIS method to sort the node importance; the method comprises the following specific steps:
1) Constructing a normalized index value matrix
2) Determining a weighting matrix
3) Obtain a positive ideal solution
Sum and minus ideal solution
Namely:
4) Calculating the distance from each node to the positive and negative ideal solutions
And
namely:
5) Calculating the closeness C of the ith node i I.e. is node v i The node importance value obtained by fusing a plurality of indexes through the algorithm of the invention is as follows:
and step 8: and outputting the result of calculating the importance of the telecommunication network nodes based on the network structure and the node value.
In order to fully characterize the importance of nodes in a telecommunication network, the importance of the nodes should be comprehensively quantified and evaluated based on a plurality of indexes such as network local attributes, global attributes, traffic scale and bearer service information. The invention provides a telecommunication network node importance calculating method based on a network structure and node value, which comprehensively considers the information quantity provided by each index such as network local attribute, global attribute, flow scale, bearing service information and the like to obtain objective index weight. And finally, weighting the index matrix by using the weight of each index, and finishing the fusion of a plurality of indexes by combining TOPSIS (technique for order preference by similarity to ideal solution) to obtain the closeness of the nodes to the ideal solution so as to obtain the actual importance of each node.
The topology of the telecommunications network in this embodiment is represented as
Wherein
Is a node set, and N is the number of nodes in the network;
is the set of edges, and M is the number of edges in the network.
A graph's unweighted adjacency matrix is marked as
In a undirected network
If and only if node v i And v j There is a connecting edge between them, otherwise
(ii) a In a directed network
If and only if there is one slave node v i Point direction v j Directed edge of otherwise
. It is agreed that all information traveling in the network is collectively referred to as a network flow.
A path in a network is an alternating sequence of a set of nodes and edges like this: v. of 1 ，e 1 ，v 2 ，e 2 ， ……，e N-1 ,v N Wherein v is i ，v i+1 Is e i Two end points of (a). A network is said to be connected if there is a path between any pair of nodes connecting them. The telecommunications network service matrix B being a matrix
And K represents the total number of traffic types in the network.
The specific topological structure of the telecommunication network is different from other types of scale-free networks, and if only the local topology of the nodes is considered, the key positions of the boundary nodes in the network topology can be ignored. Meanwhile, the importance degree of the service possibly carried by the important node obtained only from the analysis of the network topology is lower, and the influence of the failure of the key node in the topology on the telecommunication network is smaller. Therefore, the invention comprehensively considers the position of the node in the telecommunication network from four node importance indexes, such as local information L, global information G, flow scale TS, node bearing key service information BI and the like.
Claims (9)
1. The method for calculating the importance of the nodes of the telecommunication network based on the network structure and the node value is characterized by comprising the following steps:
step 1: establishing a network topology model of a telecommunications network and constructing an adjacent matrix A of an unlicensed network corresponding to the model N*N And weighted network adjacency matrix W N*N N represents the total number of nodes in the entire network;
step 2: computing nodes v according to a telecommunications network topology i The local information index L (i);
and step 3: computing nodes v according to a telecommunications network topology i The global information index G (i);
and 4, step 4: calculating nodes v according to telecommunication network service conditions i Flow size index TS i ；
And 5: calculating nodes v according to telecommunication network service conditions i Loaded key service information index BI i ；
Step 6: according to local partInformation index L (i), global information index G (i), flow size index TS i And the key service information index BI of the load i Calculating the importance of the nodes;
and 7: sorting the node importance degrees;
and 8: and outputting the sequencing result of the node importance.
2. The method of claim 1, wherein in step 2, the nodes v are calculated according to the topology of the telecommunication network i Further comprises a adjacency matrix A according to the unweighted network N*N Calculating the connection strength between nodes, specifically comprising:
node v in a computing network i Degree of (d):
wherein j represents a dividing node v i All other node numbers in the network except for N representing the total number of nodes in the whole network, a ij Representing a node v i And node v j If two nodes are connected, a ij Is 1, otherwise is 0;
computing connection strength CS between nodes ij The following were used:
in the formula, N (i) and N (j) each represent a node v i And node v j Set of neighbor points of, D i Representing a node v i Degree of (D) j Representing a node v j Degree of (d) when node v i And node v j Are identical, their connection strength CS ij =1, CS if they do not have a common neighbor ij =0。
3. A method of calculating the importance of a node in a telecommunications network based on network structure and node value as claimed in claim 2 wherein in step 2, the node v is calculated i The specific method of the local information index L (i) is as follows:
wherein,
in the formula,
，CS ij representing a node v i And node v j The connection strength between the two, j ≠ i, j = 1. C (i) of all nodes constitutes a vectorCD (i) is a node v in the network i The degrees of all nodes form a vectorD；K c Vectors, max (m), calculated for all nodes by equation (4) aboveC) Representing slave vectorsCTaking the maximum value of C (i) of all the nodes contained in C (i), max: (D) Representing slave vectorsDThe degree D (i) of all the nodes contained is the maximum value.
4. A method according to claim 3, wherein in step 3, the nodes v are calculated according to the topology of the telecommunication network i Global information index ofG(i) Comprising a computing node v i Mesomeric centrality of (c):
in the formula, b jk (i) Watch (A)Show node v j And node v k Between via a node v i The number of shortest paths of (1); b is a mixture of jk To the slave node v j To node v k The total number of all shortest paths between them,Vrepresenting a collection of nodes.
5. The method of claim 4, wherein the network global information indicator is calculated by the following formulaG(i) It is used to quantify the importance of node locations while distinguishing the tip nodes from the boundary nodes:
in the formula:BC(i) Representing a node v i The mesomeric center of (A) is,BCis the betweenness centrality of each nodeBC(i) An intermediate central vector of composition, max: (BC) Representing slave vectorsBCAnd taking the maximum betweenness centrality of the nodes.
6. The method of claim 5 in which in step 4, the node v is calculated according to the traffic conditions of the telecommunications network i Flow scale index ofTS i Comprises the following steps:
wherein N is i Representing a node v i Neighbor set of (a), w ij Representing a node v i And node v j The value of the flow in between,Wfor network adjacency matrix W N*N The element is w ij And max (W) denotes a slave network adjacency matrix W N*N The maximum flow value is taken.
7. The method of claim 6A method for calculating the importance of nodes in telecommunication network based on network structure and node value is characterized in that in step 5, the node v is calculated according to the service condition of telecommunication network i Loaded key service information index BI i The following:
wherein,
in the formula, BI i Representing the node v after normalization i The service importance of the bearer; b (i) represents node v i The importance of the traffic actually carried is,brepresents a vector consisting of the traffic importance b (i) of all nodes,max(b) Representing slave vectorsbTaking a maximum vector value;Krepresenting the total number of service types; q (s, d, k) denotes the node v s And node v d In a direction ofkThe number of class services;Idenotes a traffic importance vector, I (k) denoteskThe importance of the class service.
8. The method according to claim 7, wherein in step 6, the method is based on local information index L (i), global information index G (i), and traffic scale index TS i And the key service information index BI of the load i Compute node v i The specific method of importance of (c) is as follows:
1) Calculating information entropy of jth importance indexe j ：
Whereint j Represents the sum of the attribute values under the jth importance index:
2) Calculating the information entropy weight of the jth importance indexew j ：
N represents the number of nodes in the network, the network has m node importance indexes, wherein the ith node v i The j-th importance index value of (2) is denoted as t ij ，m=4，t i1 ，t i2 ，t i3 ，t i4 Respectively represent nodes v i Local information index L (i), global information index G (i), and traffic scale index TS i And node v i Loaded key service information index BI i To form a node importance index matrixT，The weight of the jth importance index value is:
。
9. the method for calculating the importance of nodes in a telecommunication network based on the network structure and the node value as claimed in claim 8, wherein in the step 7, the importance of the nodes is ranked, and the specific steps are as follows:
1) Constructing a matrix of normalized index values
2) Determining a weighting matrix
3) Find out the positive ideal solution F + Sum negative ideal solution F - Namely:
4) Calculating the distance D from each node to the positive and negative ideal solutions i + And D i - Namely:
5) Calculate the ith node v i Closeness of (C) i I.e. is node v i The node importance value obtained by fusing the plurality of node importance indexes is as follows:
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211629129.XA CN115622902B (en) | 2022-12-19 | 2022-12-19 | Telecommunication network node importance calculation method based on network structure and node value |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211629129.XA CN115622902B (en) | 2022-12-19 | 2022-12-19 | Telecommunication network node importance calculation method based on network structure and node value |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN115622902A true CN115622902A (en) | 2023-01-17 |
| CN115622902B CN115622902B (en) | 2023-04-07 |
Family
ID=84880601
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211629129.XA Active CN115622902B (en) | 2022-12-19 | 2022-12-19 | Telecommunication network node importance calculation method based on network structure and node value |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115622902B (en) |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115865740A (en) * | 2023-03-02 | 2023-03-28 | 中国人民解放军国防科技大学 | Network structure-based key link identification method and device |
| CN116032828A (en) * | 2023-02-27 | 2023-04-28 | 中国人民解放军国防科技大学 | Medium number centrality approximate calculation method and device |
| CN116827807A (en) * | 2023-05-30 | 2023-09-29 | 国网吉林省电力有限公司 | Power communication network node importance evaluation method based on multi-factor evaluation index |
| CN118035374A (en) * | 2024-04-11 | 2024-05-14 | 山东省国土测绘院 | Basic geographic information linkage updating method and system based on cloud platform |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7765319B1 (en) * | 2003-07-30 | 2010-07-27 | Gorman Sean P | System and method for analyzing the structure of logical networks |
| CN106130007A (en) * | 2016-06-27 | 2016-11-16 | 华北电力大学 | A kind of active distribution network energy storage planing method theoretical based on vulnerability |
| CN108879708A (en) * | 2018-08-28 | 2018-11-23 | 东北大学 | A kind of the reactive voltage partition method and system of active distribution network |
| CN109005055A (en) * | 2018-07-16 | 2018-12-14 | 西安交通大学 | Complex network information node different degree evaluation method based on multiple dimensioned manifold |
| CN109245948A (en) * | 2018-07-27 | 2019-01-18 | 北京邮电大学 | The mapping method of virtual network and its device perceived safely |
| CN110474806A (en) * | 2019-08-19 | 2019-11-19 | 国网甘肃省电力公司经济技术研究院 | A kind of power telecom network key node recognition methods |
| CN113285828A (en) * | 2021-05-19 | 2021-08-20 | 湖南经研电力设计有限公司 | Complex network key node identification method and power grid key node identification method |
| CN114567562A (en) * | 2022-03-01 | 2022-05-31 | 重庆邮电大学 | Method for identifying key nodes of coupling network of power grid and communication network |
| CN115168654A (en) * | 2022-05-24 | 2022-10-11 | 青海师范大学 | Multi-attribute decision-based super-network important node identification method |
-
2022
- 2022-12-19 CN CN202211629129.XA patent/CN115622902B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7765319B1 (en) * | 2003-07-30 | 2010-07-27 | Gorman Sean P | System and method for analyzing the structure of logical networks |
| CN106130007A (en) * | 2016-06-27 | 2016-11-16 | 华北电力大学 | A kind of active distribution network energy storage planing method theoretical based on vulnerability |
| CN109005055A (en) * | 2018-07-16 | 2018-12-14 | 西安交通大学 | Complex network information node different degree evaluation method based on multiple dimensioned manifold |
| CN109245948A (en) * | 2018-07-27 | 2019-01-18 | 北京邮电大学 | The mapping method of virtual network and its device perceived safely |
| CN108879708A (en) * | 2018-08-28 | 2018-11-23 | 东北大学 | A kind of the reactive voltage partition method and system of active distribution network |
| CN110474806A (en) * | 2019-08-19 | 2019-11-19 | 国网甘肃省电力公司经济技术研究院 | A kind of power telecom network key node recognition methods |
| CN113285828A (en) * | 2021-05-19 | 2021-08-20 | 湖南经研电力设计有限公司 | Complex network key node identification method and power grid key node identification method |
| CN114567562A (en) * | 2022-03-01 | 2022-05-31 | 重庆邮电大学 | Method for identifying key nodes of coupling network of power grid and communication network |
| CN115168654A (en) * | 2022-05-24 | 2022-10-11 | 青海师范大学 | Multi-attribute decision-based super-network important node identification method |
Non-Patent Citations (1)
| Title |
|---|
| FANG YANG: "Identification of Key Nodes Based on Integrating of Global and Local Information" * |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116032828A (en) * | 2023-02-27 | 2023-04-28 | 中国人民解放军国防科技大学 | Medium number centrality approximate calculation method and device |
| CN116032828B (en) * | 2023-02-27 | 2023-06-13 | 中国人民解放军国防科技大学 | Medium number centrality approximate calculation method and device |
| CN115865740A (en) * | 2023-03-02 | 2023-03-28 | 中国人民解放军国防科技大学 | Network structure-based key link identification method and device |
| CN116827807A (en) * | 2023-05-30 | 2023-09-29 | 国网吉林省电力有限公司 | Power communication network node importance evaluation method based on multi-factor evaluation index |
| CN116827807B (en) * | 2023-05-30 | 2024-05-10 | 国网吉林省电力有限公司 | Power communication network node importance evaluation method based on multi-factor evaluation index |
| CN118035374A (en) * | 2024-04-11 | 2024-05-14 | 山东省国土测绘院 | Basic geographic information linkage updating method and system based on cloud platform |
| CN118035374B (en) * | 2024-04-11 | 2024-06-14 | 山东省国土测绘院 | Basic geographic information linkage updating method and system based on cloud platform |
Also Published As
| Publication number | Publication date |
|---|---|
| CN115622902B (en) | 2023-04-07 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN115622902B (en) | Telecommunication network node importance calculation method based on network structure and node value | |
| CN107482626B (en) | Method for identifying key nodes of regional power grid | |
| CN115622903B (en) | Telecommunication network node importance calculation method based on network structure | |
| CN108009710A (en) | Node test importance appraisal procedure based on similarity and TrustRank algorithms | |
| CN110135092A (en) | Complicated weighting network of communication lines key node recognition methods based on half local center | |
| CN110837602A (en) | User recommendation method based on representation learning and multi-mode convolutional neural network | |
| CN112036445B (en) | User identity recognition method across social networks based on neural tensor network | |
| CN106533759B (en) | A kind of link prediction method based on path entropy in multitiered network | |
| CN112311608B (en) | Multilayer heterogeneous network space node characterization method | |
| CN111428323A (en) | Method for identifying group of key nodes by using generalized discount degree and k-shell in complex network | |
| CN114268547B (en) | Multi-attribute decision-making method for identifying key nodes of air emergency communication network | |
| CN108734223A (en) | The social networks friend recommendation method divided based on community | |
| CN110533253A (en) | A kind of scientific research cooperative Relationship Prediction method based on Heterogeneous Information network | |
| CN113422695A (en) | Optimization method for improving robustness of topological structure of Internet of things | |
| CN112487658A (en) | Method, device and system for identifying key nodes of power grid | |
| CN107295537A (en) | A kind of method and system for wireless sensor network reliability of testing and assessing | |
| CN112580902A (en) | Object data processing method and device, computer equipment and storage medium | |
| CN107885971A (en) | Using the method for improving flower pollination algorithm identification key protein matter | |
| CN114492749A (en) | Time-limited red-blue countermeasure problem-oriented game decision method with action space decoupling function | |
| Li et al. | Network topology optimization via deep reinforcement learning | |
| CN115114484A (en) | Abnormal event detection method and device, computer equipment and storage medium | |
| CN106912040A (en) | A kind of AdHoc network key node recognition methods for merging elimination method | |
| CN109636338A (en) | A kind of broad band photoelectrical operation management platform system | |
| CN116132167B (en) | Multi-protocol botnet detection method oriented to Internet of things | |
| CN111405605A (en) | Wireless network interruption detection method based on self-organizing mapping |
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 |