CN116796481A - Social network key node identification method and system based on improved elastic model - Google Patents
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Abstract
The invention provides a social network key node identification method and a system based on an improved elastic model, wherein the method comprises the following steps: calculating the degree, ks value, network diameter and shortest path of node pairs of the social network node; based on a k-shell algorithm, introducing a position coefficient reflecting the influence offset effect among social network nodes according to the ks value difference among the social network nodes; based on the elastic model, obtaining the elastic force of the node pair reflecting the importance degree according to the degree of the node, the shortest path of the node pair, the network diameter and the position coefficient; and after summing the elasticity of the node pairs between the nodes and different nodes, sorting according to the importance degree, and obtaining the key nodes of the social network according to the sorting result. The influence of the location of the node in the social network on its importance is taken into account by the introduced location coefficients.
Description
Technical Field
The invention belongs to the technical field of network analysis, and particularly relates to a social network key node identification method and system based on an improved elastic model.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
Identifying key nodes in a complex network is an effective way to improve network robustness, while also facilitating more comprehensive and profound knowledge of different networks and their characteristics. The identification of key nodes in a complex network has become an academic hotspot problem, and the method for mining the key nodes in the complex network is increasingly diversified.
Social networks are a special complex network, closely related to people's life. The interaction of users in a social network can generate massive complex data information, so how to accurately and rapidly locate key users is quite realistic and challenging, and has commercial value as well. Information exchange and transfer between nodes in a network results in the propagation of information, which in the process depends on the original influence of the nodes and is independent of the positions of the nodes in a complex network.
There are many methods for solving the problem of complex network key node identification, and the most classical methods include k-shell algorithm, centrality (DC), mid-number centrality (BC) and the like. The k-shell algorithm is a classical and practical method in the complex network key node identification method. The method is characterized in that from the global information of the network, nodes in the network are sequentially deleted from the network according to the illuminance, and the continuous edges are deleted, just like onion peeling, until all nodes in the network are deleted or only isolated nodes exist, and the algorithm is ended. The K-shell decomposition method tends to assign a same ks value to multiple nodes, but the importance of the same shell node may not be the same, and nodes closer to the inner layer of the network cannot be distinguished. In addition, since the ks values do not provide enough node topology location information, the seed nodes evaluated by the K-shell method are typically located in the center of the graph, i.e., the identified key nodes are typically clustered in the same region.
Many students have proposed various physical models in an attempt to identify key nodes of a complex network among the proposed models, the attraction model and the spring model being most commonly used among the proposed physical models. Li et al put forward the gravitation model that can carry on the ordering of important node of the complex network by utilizing the sense that law of universal gravitation gives, then Li et al put forward the generalized gravity model on the basis of gravitation model in combination with the clustering coefficient, but calculate the clustering coefficient makes the algorithm complex, has greatly improved the time complexity. Then Meng et al put forward a key node identification algorithm (SM) based on Hooke's law in physics, but in the traditional elastic model, in identifying key nodes in a complex network, although node local information and global information are included in the calculation process, the importance degree of the node is greatly influenced by neglecting the position of the node in the network, and the method is not suitable for social networks.
Disclosure of Invention
In order to overcome the defects in the prior art, the invention provides a social network key node identification method and a social network key node identification system based on an improved elastic model, and the influence of the position of a node in a social network on the importance degree of the node is considered through an introduced position coefficient; and combining the position coefficient with the elasticity model, reflecting the influence degree of the nodes according to the elasticity between the nodes, further obtaining the importance degree of the nodes in the social network, and excavating out key nodes in the social network.
To achieve the above object, a first aspect of the present invention provides a social network key node identification method based on an improved elasticity model, including:
calculating the degree, ks value, network diameter and shortest path of node pairs of the social network node;
based on a k-shell algorithm, introducing a position coefficient reflecting the influence offset effect among social network nodes according to the ks value difference among the social network nodes;
based on the elastic model, obtaining the elastic force of the node pair reflecting the importance degree according to the degree of the node, the shortest path of the node pair, the network diameter and the position coefficient;
and after summing the elasticity of the node pairs between the nodes and different nodes, sorting according to the importance degree, and obtaining the key nodes of the social network according to the sorting result.
A second aspect of the present invention provides a social network key node identification system based on an improved elasticity model, comprising:
the computing module is used for computing the degree, the ks value, the network diameter and the shortest path of the node pair of the social network node;
the introducing module is used for introducing a position coefficient reflecting the influence offset effect among the social network nodes according to the ks value difference among the social network nodes based on the k-shell algorithm;
the elasticity calculation module: based on the elastic model, obtaining the elastic force of the node pair reflecting the importance degree according to the degree of the node, the shortest path of the node pair, the network diameter and the position coefficient;
and the identification module is used for sequencing the elastic forces of the node pairs between the nodes and different nodes according to the importance degree after summing the elastic forces, and obtaining key nodes of the social network according to the sequencing result.
A third aspect of the present invention provides a computer apparatus comprising: the system comprises a processor, a memory and a bus, wherein the memory stores machine-readable instructions executable by the processor, when the computer device runs, the processor and the memory are communicated through the bus, and the machine-readable instructions are executed by the processor to execute a social network key node identification method based on an improved elasticity model.
A fourth aspect of the present invention provides a computer readable storage medium having stored thereon a computer program which, when executed by a processor, performs a social network key node identification method based on an improved elasticity model.
The one or more of the above technical solutions have the following beneficial effects:
in the invention, based on the characteristic of a k-shell algorithm, a node ks value represents the position of a node in the whole complex network, a position coefficient reflecting the influence offset effect among social network nodes is introduced according to the ks value difference among the social network nodes, and the influence of the position of the node in the social network on the importance degree of the node is considered through the introduced position coefficient; and combining the position coefficient with the elasticity model, reflecting the influence degree of the nodes according to the elasticity between the nodes, further obtaining the importance degree of the nodes in the social network, and excavating out key nodes in the social network.
Additional aspects of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the invention.
FIG. 1 is a flowchart of identifying key nodes of a social network in accordance with an embodiment of the present invention;
fig. 2 (a) is a trend of change of F (t) with t when ρ=0.03 and σ=1.5 in the social network Email according to the first embodiment of the present invention;
fig. 2 (b) is a trend of change of F (t) with t when ρ=0.03, σ=1.5 in the social network Facebook according to the first embodiment of the present invention;
fig. 2 (c) is a trend of change of F (t) with t when ρ=0.03, σ=1.5 in the social network Hamster in the first embodiment of the present invention;
fig. 2 (d) is a trend of change of F (t) with t when ρ=0.03 and σ=1.5 in the social network Jazz according to the first embodiment of the present invention;
fig. 3 (a) shows a social network Email under different conditions F (t) when σ=1.5 ρ c ) Is a trend of change in (2);
fig. 3 (b) is a diagram showing a social network Facebook according to an embodiment of the present invention under different conditions F (t) when ρ is equal to σ=1.5 c ) Is a trend of change in (2);
fig. 3 (c) is a diagram showing the social network hemster F (t) under different ρ conditions when σ=1.5 in the first embodiment of the present invention c ) Is a trend of change in (2);
fig. 3 (d) is a diagram showing the social network Jazz under different conditions F (t) when σ=1.5 ρ in the first embodiment of the invention c ) Is a trend of change in (2);
FIG. 4 (a) is a social graph of an embodiment of the present inventionWhen ρ=0.03, F (t c ) Is a trend of change in (2);
fig. 4 (b) shows a social network Facebook according to the first embodiment of the present invention under different conditions F (t c ) Is a trend of change in (2);
fig. 4 (c) is a diagram showing a social network hemster F (t) under different σ conditions when ρ=0.03 in the first embodiment of the present invention c ) Is a trend of change in (2);
fig. 4 (d) shows the social network Jazz in the first embodiment of the invention under different conditions F (t) at ρ=0.03 c ) Is a trend of change in (2);
fig. 5 is a flowchart of the conversion of hooke's law into an elastic model in accordance with the first embodiment of the present invention.
Detailed Description
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the invention. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the present invention.
Embodiments of the invention and features of the embodiments may be combined with each other without conflict.
Term interpretation:
the degree is the most basic parameter for describing the statistical characteristics of the network, and the influence of the nodes in the network is described, wherein the value of the degree is equal to the number of the nodes connected to the node, namely the number of neighbors of the target node; degrees directly reflect whether a node has the ability to establish contact with surrounding nodes and are local variables.
The network diameter is the maximum value of the shortest path length between any two reachable nodes in the network.
Example 1
The embodiment discloses a social network key node identification method based on an improved elastic model, which comprises the following steps:
calculating the degree, ks value, network diameter and shortest path of node pairs of the social network node;
based on a k-shell algorithm, introducing a position coefficient reflecting the influence offset effect among social network nodes according to the ks value difference among the social network nodes;
based on the elastic model, obtaining the elastic force of the node pair reflecting the importance degree according to the degree of the node, the shortest path of the node pair, the network diameter and the position coefficient;
and after summing the elasticity of the node pairs between the nodes and different nodes, sorting according to the importance degree, and obtaining the key nodes of the social network according to the sorting result.
In the implementation, an improved elastic model based on a k-shell algorithm is provided based on a traditional elastic model, and aims to quickly and accurately identify key nodes in a complex network. The method utilizes the characteristic of a k-shell algorithm, and the position of a node in the whole complex network is represented by a node ks value. Meanwhile, a position coefficient is also provided, the value of the coefficient can enable the model to be converted in the improved elastic model and the traditional elastic model, and then the node influence is calculated by combining the position information with the elastic model. According to the method, on the basis of a traditional elastic model, the positions of nodes in a network and the influence counteracting effect among the nodes are calculated by utilizing the characteristics of a k-shell algorithm, so that the traditional elastic model is improved.
As shown in fig. 1, the key idea of the invention is to convert the elasticity of the nodes into the influence of the nodes, quickly and accurately find the seed node with the most influence in all the nodes, and maximize information propagation by using the interrelationship between the seed node and other nodes. Based on the traditional elasticity model, a k-shell algorithm is combined, and a position coefficient mu is introduced to describe the position information of the nodes and the influence offset effect between the node pairs. In addition, the improved model is applied to a real social network, so that the practical use value of the method is researched. The specific step flow is divided into five steps respectively: step 1: constructing a network; step 2: calculating node degree, network diameter and shortest path length of the node pair; step 3: calculating a ks value and a position coefficient mu of the node; step 4: calculating the elasticity of the node pairs; step 5: node importance is calculated.
Specifically, step 1: when the dynamic characteristics and the directionality of the network are not considered, the network is represented by G (V, E), V= { V1, V2, V3, …, vn } is a set of network nodes, and n is the number of nodes; e= { E1, E2, E3, …, em } is the set of edges, and m is the number of edges. A is that n×n ＝(a ij ) As a adjacency matrix of the network, when a ij When=1, it is explained that there is an edge between node i and node j, when a ij When=0, no edge is present between node i and node j.
Step 2: in the spring model, the stiffness coefficient in hooke's law is replaced by the product of the degrees of the node pairs, and the difference between the network diameter and the shortest path length of the node pairs is taken as a spring-type variable. The shortest path length between node i and node j is d ij 。
The degree calculation formula of the node is as follows:
wherein N is the total node number in the network, A n×n ＝(a ij ) A is an adjacency matrix of a network ij =1 or 0 indicates the presence or absence of a continuous edge between nodes i, j.
Step 3: calculating a ks value and a position coefficient mu of each node in the network:
the importance degree of each node in the complex network is judged, and the key nodes in the network are selected, so that the local information of the network is analyzed, and the global information of the network is paid attention to. In conventional complex networks, the more centrally located nodes, the higher their impact and importance. These nodes have a greater degree of radiation impact on other nodes in the network. Nodes in the central location of the network not only have a greater radiation effect on surrounding nodes, but also are more likely to be assisted by other surrounding nodes when nodes near the central location are affected by external influences, thereby counteracting some of the external influences. The method is better than the pudding of the large V deep rumors in the network, and other users related to the pudding can lift out the position to clarify the rumors, so that the adverse effect on the large V users is reduced.
In the embodiment, a position coefficient is introduced, the position of a node in a network is described by using the value of the node ks, the influence counteracting effect between the nodes is described by using the difference between the value of the node ks, and the position coefficient is calculated specifically as follows:
μ ij ＝e ks(i)-ks(j) (2)
where ks (i) is the ks value of node i and ks (j) is the ks value of node j.
As shown in fig. 5, in the conventional elastic model (SM), attractive force between nodes is abstracted into elasticity in the elastic model, and importance of the nodes in a complex network is described by the size of the elasticity of the nodes. Since the effect of the stiffness coefficient is to describe the amount of spring force that is generated when the spring is deformed, in SM the stiffness coefficient is converted to a product that characterizes the degree of node-to-importance, and the spring type deformation is converted to the difference between the network diameter and the node-to-distance. The spring force model conversion chart is shown in fig. 5. Elasticity S of node i i The expression is as follows:
S i ＝∑ j≠i (k i k j (d-d ij )) (3)
wherein k is i And k j Representing the degree of node i and node j, respectively, d being the network diameter, d ij Is the shortest path length from node i to node j. When only the elastic force case within the truncated radius is considered, equation (3) is converted into:
wherein r is s Representing the radius of the truncation. It can be seen that the larger the elastic force of the node pair with more neighbor nodes and shorter distance, namely the higher the importance degree.
The k-shell algorithm is proposed by Kitsak et al in 2010 at the earliest, and the core idea is that nodes in the network are sequentially deleted from the network according to the intensity of illumination from the global information of the network, and the continuous edges are deleted at the same time, just like onion peeling, until all nodes in the network are deleted or only isolated nodes exist, and the algorithm is ended. Namely: firstly, finding out a node with the degree of 1 in the network, wherein the ks value of the node is 1, and deleting the node and the connecting edge of the node; sequentially iterating to obtain the ks values of 2,3 and … …; the algorithm ends until all nodes in the network have been deleted or only orphaned nodes exist.
Step 4: calculating the elasticity between node pairs:
the importance of the nodes in the elastic model calculation network is judged from the following two aspects: (1) degree of node pairs: the larger the product of the degrees of the two nodes, the larger the elastic force between the node pairs, and the more important the nodes; (2) distance between node pairs: the distance between the nodes is inversely proportional to the magnitude of the elastic force between the nodes, and the closer the distance is, the greater the elastic force between the nodes, that is, the more important the nodes are.
The proposed improved spring force model considers that the final node spring force can be calculated only by calculating the spring force of a node pair in the network, and the force is marked as F ij . The elastic force calculation between the node pairs requires four elements, namely the degree of the node pairs, the shortest distance of the node pairs, the ks value of the nodes and the diameter of the network, F ij The specific calculation formula of (2) is as follows:
F ij ＝μ ij k i k j (d-d ij ) (5)
wherein mu ij For the position coefficient between node i and node j, k i And k j The degrees of nodes i and j, respectively, d is the network diameter, d ij Is the shortest path length from node i to node j.
As can be seen from equation (5), when node i and node j are at different levels in the network, i.e., both possess different ks values, F ij ≠F ji The method comprises the steps of carrying out a first treatment on the surface of the Whereas when node i and node j possess the same ks value, μ ij =0, that is to say the modified elastic model proposed in this embodiment is converted back to the conventional elastic model. When node i is more centrally located in the network relative to node j, i.e., when node i has a greater value of ks, its spring force against node j is greater than in the conventional spring force model. Conversely, when node i is at a more marginal location in the network than node j, it is to node jThe spring force is smaller than in the conventional spring force model.
Step 5: calculating the elasticity of the node:
the key node identification algorithm (KSSC) of the improved elastic model based on the k-shell, provided by the implementation, regards the sum of the node elastic forces as the importance degree of the node in the network, and the node importance degree ranking can be obtained by ranking according to the value of each node KSSC in the network.
And 4, obtaining the elasticity between a pair of nodes, solving the elasticity of the nodes to all other nodes in the network one by one, and summing the elasticity, namely the KSSC value, of the nodes. The specific calculation method of the KSSC value is as follows:
KSSC(i)＝∑ j≠i μ ij k i k j (d-d ij ) (6)
wherein KSSC (i) is the elastic force of node i.
If the KSSC value of the node within the range of the truncated radius r is calculated, the calculation formula is as follows:
KSSC r (i)＝∑ d≤r,j≠i μ ij k i k j (d-d ij ) (7)
wherein KSSC r (i) Is the elastic force of the node i in the range of the radius r.
From equation (6) and equation (7), both over the entire network and over the network cutoff radius: (1) When the degree of the node pair is larger and the distance of the node pair is closer, the elasticity of the node is larger, and the node is correspondingly known to be more important in the network; (2) When the ks value of the node is larger than that of the neighbor node, the node is positioned in the center of the relative network and has higher importance.
According to the social network key node identification method combining the elasticity model and the k-shell algorithm, the key nodes in the complex network are identified by calculating the elasticity of the nodes and converting the elasticity of the nodes into the influence of the nodes from the global and local information of the network and sequencing the KSSC values of the nodes. Compared with the traditional elastic model (SM), the innovation point of the method of the embodiment is thatThe method is characterized in that a position coefficient mu for expressing the position information of a node in a network is provided in combination with a k-shell algorithm ij . Compared with a common complex network, the method can be applied to a social network, has better performance and can prove that the method has practical value.
To verify the actual usability of the KSSC method, it is applied in social networks, and the traditional elasticity model (SM) of centrality (DC), medians centrality (BC), the k-shell algorithm (ks) and the k-shell index algorithm (Wks) based on potential edge weights are compared with the KSSC.
Because the information transmission process in the social network is very similar to the transmission process of infectious diseases, an SIR model is selected for experiments. And setting the key node selected by the above methods as a seed node to be an I-state node in an initial state of SIR model infection. To ensure that the experimental data were as accurate as possible, each set of experiments was run 100 times independently and averaged.
Data set: email, facebook, hamster and Jazz four social networks were used to test and compare the performance of the KSSC method with other methods. The specific topology characteristics of these networks are shown in table 1. Where |V| represents the number of network nodes, |E| represents the number of network edges, k max Representing the maximum degree of the network,<k>Representing the average degree of the network.
Table 1: specific topological characteristics of four social networks
| Network | |V| | |E| | k max | <k> |
| 1133 | 5451 | 71 | 9.62 | |
| 1574 | 17215 | 314 | 21.87 | |
| Hamster | 2426 | 16631 | 273 | 13.71 |
| Jazz | 198 | 2742 | 100 | 27.69 |
Comparative experiment 1: the first experiment was performed under conditions where the initial infection node ratio was 0.03 and the infection rate was 1.5. Fig. 2 (a) -2 (d) show the trend of the change F (t) accompanying the change of t. As can be seen from the data in the figure: the KSSC reaches the times of infection peak values of 240, 800, 400 and 400 on the four data sets respectively; the infection amounts of KSSC at the time of reaching the infection peak on the four data sets were 0.215, 0.135, 0.163, and 0.302, respectively. Under the condition that the initial infection node ratio and the infection rate are determined, the KSSC method proposed by the implementation at the same time t is the largest in infection mode, the fastest in infection peak value in all methods, and the largest in total infection amount when the infection process reaches a stable state. Infection with SM performed well in most cases in the other five methods, but the infection scale with ks was almost minimal. This illustrates that the KSSC proposed in this embodiment requires less time to reach the same infection scale. Put another way, more nodes can be infected with the same time KSSC, verifying the validity of the method.
Comparative experiment 2: the second experiment was performed at an infection rate of 1.5, and FIGS. 3 (a) -3 (d) show that there are different initial infection rates ρ, F (t) c ) Is a trend of change in (c). By comparing the infection status of the various methods on the four data sets, it can be seen that: the more nodes initially infected, the greater the amount of final infection when steady state is reached. When the initial infection proportion ρ is small, the final infection amounts of the various methods are not quite different; when the initial infection proportion ρ is large, the infection scale of KSSC is the largest in most data sets, especially when ρ=0.03, the final infection amounts in two large social networks of Facebook and Hamster can reach 0.111 and 0.162, respectively. Infection of KSSCs in Jazz networks, while not the best, performs relatively well. In the four social networks above, the KSSC has little advantage in terms of final infection when the initial infection proportion is small. The progressive predominance of the final infection scale of KSSC with increasing p compared to other methods, especially both SM and ks, suggests that the improvement of this implementation is very effective.
Comparative experiment 3: the third experiment was performed at an initial infection rate of 0.03, and FIGS. 4 (a) -4 (d) show that F (t) c ) Is a trend of change in (c). From the data in the figures, the following conclusions can be drawn: it is apparent that the final infection amount is proportional to the infection rate. When the infection rate is small, the methods under the four data sets perform poorly and are not widely separated. When the infection rate is high, the final infection amount of KSSC is always the largest. When σ=2, the final infection amounts of KSSC reached 0.175 and 0,679 in Facebook and Jazz networks, respectively, which were significantly higher than ks and SM. This well demonstrates that the advantages of the KSSC proposed by the present implementation are more pronounced.
From three sets of experimental data, it can be easily found that: the performance of KSSC is almost the best across different data sets, with better performance than ks and SM in most cases, a fast generalization process and a large infection scale. This demonstrates that the improved KSSC method is correct and efficient. The three groups of experimental data also illustrate that the KSSC improves the defect that the traditional elastic model is only used for the conventional complex network, and verifies that the ksS method has more practicability.
Example two
It is an object of the present embodiment to provide a social network key node identification system based on an improved elasticity model, comprising:
the computing module is used for computing the degree, the ks value, the network diameter and the shortest path of the node pair of the social network node;
the introducing module is used for introducing a position coefficient reflecting the influence offset effect among the social network nodes according to the ks value difference among the social network nodes based on the k-shell algorithm;
the elasticity calculation module: based on the elastic model, obtaining the elastic force of the node pair reflecting the importance degree according to the degree of the node, the shortest path of the node pair, the network diameter and the position coefficient;
and the identification module is used for sequencing the elastic forces of the node pairs between the nodes and different nodes according to the importance degree after summing the elastic forces, and obtaining key nodes of the social network according to the sequencing result.
Example III
It is an object of the present embodiment to provide a computing device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, which processor implements the steps of the method described above when executing the program.
Example IV
An object of the present embodiment is to provide a computer-readable storage medium.
A computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of the above method.
The steps involved in the devices of the second, third and fourth embodiments correspond to those of the first embodiment of the method, and the detailed description of the embodiments can be found in the related description section of the first embodiment. The term "computer-readable storage medium" should be taken to include a single medium or multiple media including one or more sets of instructions; it should also be understood to include any medium capable of storing, encoding or carrying a set of instructions for execution by a processor and that cause the processor to perform any one of the methods of the present invention.
It will be appreciated by those skilled in the art that the modules or steps of the invention described above may be implemented by general-purpose computer means, alternatively they may be implemented by program code executable by computing means, whereby they may be stored in storage means for execution by computing means, or they may be made into individual integrated circuit modules separately, or a plurality of modules or steps in them may be made into a single integrated circuit module. The present invention is not limited to any specific combination of hardware and software.
While the foregoing description of the embodiments of the present invention has been presented in conjunction with the drawings, it should be understood that it is not intended to limit the scope of the invention, but rather, it is intended to cover all modifications or variations within the scope of the invention as defined by the claims of the present invention.
Claims (10)
1. The social network key node identification method based on the improved elastic model is characterized by comprising the following steps of:
calculating the degree, ks value, network diameter and shortest path of node pairs of the social network node;
based on a k-shell algorithm, introducing a position coefficient reflecting the influence offset effect among social network nodes according to the ks value difference among the social network nodes;
based on the elastic model, obtaining the elastic force of the node pair reflecting the importance degree according to the degree of the node, the shortest path of the node pair, the network diameter and the position coefficient;
and after summing the elasticity of the node pairs between the nodes and different nodes, sorting according to the importance degree, and obtaining the key nodes of the social network according to the sorting result.
2. The method for identifying key nodes of social network based on improved elastic model as claimed in claim 1, wherein the product of two node degrees in the node pair is taken as stiffness coefficient in hooke's law in the elastic model, the difference between the network diameter and the shortest path of the node pair is taken as spring type variable in the elastic model, and the importance of the node in the social network is calculated based on the elastic model and is determined according to the degree of the node pair and the shortest path of the node pair.
3. The social network key node identification method based on the improved elasticity model as claimed in claim 2, wherein the elasticity of the node pair is specifically: the difference between the network diameter and the shortest path of the node pair, and the product of the degree of the two nodes in the node pair and the node position coefficient.
4. The method for identifying key nodes of a social network based on an improved elasticity model as claimed in claim 1, wherein the magnitude of the summed elasticity of the node pairs between the node and different nodes is used as the importance degree of the node in the social network, and the larger the value of the summed elasticity is, the more important the node in the social network is.
5. The improved elasticity model-based social network key node identification method of claim 1, wherein a difference in ks values of two nodes in a node pair is used as an index of e to determine a location coefficient reflecting an effect cancellation effect between social network nodes.
6. The method for identifying key nodes of a social network based on an improved elastic model as claimed in claim 1, wherein the degree of the node is determined according to whether the node has a continuous edge with other nodes in the social network.
7. The method for identifying key nodes of a social network based on an improved elasticity model as claimed in claim 1, wherein the k-shell algorithm is based on the fact that the nodes are sequentially deleted from the social network according to the node degree, and the ks value of the nodes in the social network is determined.
8. Social network key node identification system based on improved elasticity model, characterized by comprising:
the computing module is used for computing the degree, the ks value, the network diameter and the shortest path of the node pair of the social network node;
the introducing module is used for introducing a position coefficient reflecting the influence offset effect among the social network nodes according to the ks value difference among the social network nodes based on the k-shell algorithm;
the elasticity calculation module: based on the elastic model, obtaining the elastic force of the node pair reflecting the importance degree according to the degree of the node, the shortest path of the node pair, the network diameter and the position coefficient;
and the identification module is used for sequencing the elastic forces of the node pairs between the nodes and different nodes according to the importance degree after summing the elastic forces, and obtaining key nodes of the social network according to the sequencing result.
9. A computer device, comprising: a processor, a memory and a bus, the memory storing machine-readable instructions executable by the processor, the processor and the memory in communication via the bus when the computer device is running, the machine-readable instructions when executed by the processor performing the improved elasticity model-based social network key node identification method of any one of claims 1 to 7.
10. A computer readable storage medium, characterized in that it has stored thereon a computer program which, when executed by a processor, performs the social network key node identification method based on an improved elasticity model according to any one of claims 1 to 7.
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