CN109034562A - A kind of social networks node importance appraisal procedure and system - Google Patents
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Abstract
The present invention relates to a kind of social networks node importance appraisal procedure and systems, belong to social network analysis technical field, solves the problems, such as that the node importance appraisal procedure in the prior art based on H index or class H index is not high to node importance discrimination, effectively can not carry out importance ranking to the node with identical H value.The following steps are included: seeking the K index value of given social networks interior joint；According to the sum of the K index value of all neighbor nodes of node to be assessed, the different degree of node to be assessed is determined；Different degree based on node to be assessed assesses the importance of the node to be assessed.The present invention takes full advantage of the influence power of neighbor node, can carry out effectively distinguishing sequence to the node importance of identical H index value, can quickly and accurately assess the node importance in social networks, and assessment result discrimination is high；Meanwhile extensive social networks can be analyzed, it is adaptable convenient for quickly discovery importance node.
Description
Technical Field
The invention relates to the technical field of social network analysis, in particular to a social network node importance evaluation method and system.
Background
In recent years, social network research development is greatly promoted by representing the prosperity of social software by WeChat, microblog, FaceBook and Linkedin, wherein the importance evaluation of the social network nodes is an important direction of the social network research, and the importance evaluation of the network nodes is quickly and effectively significant for further identifying key nodes and analyzing network structures.
The existing method for measuring the importance of the nodes in the social network can be divided into methods based on network local attributes, network global attributes, random walks and community structures according to implementation methods. The representative method based on the local attribute information is a degree centrality method, the value of a node represents the number of nodes connected with the node, the local importance of the node can be intuitively reflected, and the condition of the node in the whole network cannot be well reflected; the measurement based on the global attribute comprises betweenness centrality, compactness centrality, feature vector centrality and the like, and the time complexity of the method is relatively high and is not suitable for a large network.
The H index is initially used for evaluating the personal achievement influence of researchers, corresponds to the nodes in the social network, and is H if the degree of at least H neighbor nodes of one node is H. However, the importance evaluation of the social network nodes directly applying the H index has the same defects as the k-shell decomposition algorithm, and the nodes with the same H value cannot be distinguished. The root cause of this deficiency is that the importance of a node in a social network depends not only on its own metric value, but also on the influence of its neighbor nodes on the node, or the degree of dependency of the neighbor nodes on the node. To solve this problem, H-like indices such as g-index, K-index, w-index, etc. are presented. The K-index is further subdivided by the sum of the total degrees of the neighbor nodes, but still cannot effectively distinguish partial nodes.
Disclosure of Invention
In view of the foregoing analysis, the present invention aims to provide a method and a system for evaluating importance of nodes in a social network, so as to solve the problem that the existing node importance evaluation method based on an H-index or an H-like index is not highly classified in the node importance area and cannot effectively rank the importance of nodes having the same H value.
The purpose of the invention is mainly realized by the following technical scheme:
in one aspect, a social network node importance evaluation method is provided, which includes the following steps:
solving a K index value of a node in a given social network;
determining the importance of the node to be evaluated according to the sum of the K index values of all the neighbor nodes of the node to be evaluated;
and evaluating the importance of the node to be evaluated based on the importance of the node to be evaluated.
The invention has the following beneficial effects: the method and the device make full use of the connection information among the social network nodes, comprehensively consider the influence of the neighbor nodes, not only make full use of the influence of the nodes, but also make full use of the influence of the neighbor nodes, can effectively distinguish the importance of the nodes with the same H index value, can quickly and accurately evaluate the importance of the nodes in the social network, simultaneously has high distinguishing degree of evaluation results, can analyze the large-scale social network, is convenient to quickly find the importance nodes, and has strong adaptability.
On the basis of the scheme, the invention is further improved as follows:
further, the step of obtaining the K index value of the node in the social network includes the following steps:
solving an H index value of a node in a given social network;
selecting neighbor nodes with the values not less than the H index value of the node from the neighbor node set of the node;
and determining the K index value of the node according to the sum of the values of the selected neighbor nodes.
Further, the K index value of the node is determined according to the sum of the values of the selected neighbor nodes, and the formula is as follows:
in the formula, KiK index value, h, representing node i in social networkiRepresents the H index value of the node i,representing the sum of the values of the selected neighbor nodes.
Further, determining the importance of the node to be evaluated according to the sum of the K index values of all the neighbor nodes of the node to be evaluated, wherein the formula is as follows:
in the formula, gamma(i)A set of neighbor nodes, K, of a node i to be evaluatedjIs the K index value, LK, of the neighbor node j(i)Is the importance of the node i to be evaluated.
Further, the obtaining the H index value of the node in the given social network includes the following steps:
solving the values of all nodes in the social network;
and calculating the H index value of the node in the social network by adopting a binary search method.
In another aspect, a social network node importance evaluation system is further provided, including:
the node K index value obtaining module is used for obtaining a K index value of a node in a given social network;
the node importance determining module is used for determining the importance of the node to be evaluated according to the sum of the K index values of all the neighbor nodes of the node to be evaluated and outputting the importance to the node importance evaluating module;
and the node importance evaluation module evaluates the importance of the node to be evaluated according to the received importance of the node.
The invention has the following beneficial effects: the method and the device make full use of the connection information among the social network nodes, comprehensively consider the influence of the neighbor nodes, not only make full use of the influence of the nodes, but also make full use of the influence of the neighbor nodes, can effectively distinguish the importance of the nodes with the same H index value, can quickly and accurately evaluate the importance of the nodes in the social network, simultaneously has high distinguishing degree of evaluation results, can analyze the large-scale social network, is convenient to quickly find the importance nodes, and has strong adaptability.
On the basis of the scheme, the invention is further improved as follows:
further, the node K index value obtaining module comprises an H index value obtaining unit, a neighbor node selecting unit and a K index value determining unit;
the H index value obtaining unit is used for obtaining the H index value of the node in the given social network and outputting the H index value to the neighbor node selecting unit;
the neighbor node selection unit selects neighbor nodes with the values not less than the H index value of the node from the neighbor node set of the node;
and the K index value determining unit determines the K index value of the node according to the sum of the values of the neighbor nodes selected by the neighbor node selecting unit.
Further, according to the sum of the values of the selected neighbor nodes, determining the K index value of the node, wherein the formula is as follows:
in the formula, KiK index value, h, representing node i in social networkiRepresents the H index value of the node i,representing the sum of the values of the selected neighbor nodes.
Further, determining the importance of the node to be evaluated according to the sum of the K index values of all the neighbor nodes of the node to be evaluated, wherein the formula is as follows:
in the formula, gamma(i)Set of neighbor nodes, K, for node i to be evaluatedjIs the K index value, LK, of the neighbor node j(i)Is the importance of the node i to be evaluated.
Further, the node H index value solving unit includes a value solving subunit and an H index value solving subunit:
the value solving subunit is used for solving the values of all nodes in the social network and outputting the values to the H index value solving subunit;
and the H index value obtaining subunit is used for calculating the H index value of the node in the social network by adopting a binary search method.
In the invention, the technical schemes can be combined with each other to realize more preferable combination schemes. Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
Drawings
The drawings are only for purposes of illustrating particular embodiments and are not to be construed as limiting the invention, wherein like reference numerals are used to designate like parts throughout.
Fig. 1 is a flowchart of a social network node importance evaluation method according to an embodiment of the present invention.
Fig. 2 is a schematic structural diagram of a social network node importance evaluation system according to a second embodiment of the present invention.
Fig. 3 is a schematic diagram of the topology of a simple exemplary network according to the third and fourth embodiments of the present invention.
Fig. 4 is a diagram of the complementary cumulative distribution function CCDF of the network shown in fig. 3.
Fig. 5 is a schematic diagram of a complementary cumulative distribution function CCDF of the Karate club network according to the fourth embodiment of the present invention.
Fig. 6 is a schematic diagram of a complementary cumulative distribution function CCDF of the Dolphin network according to a fourth embodiment of the present invention.
Fig. 7 is a schematic diagram of a complementary cumulative distribution function CCDF of the Celegan network according to the fourth embodiment of the present invention.
Fig. 8 is a schematic diagram of a change of a degree of distinction index of a method for evaluating importance of different nodes when an n parameter of the LFR network generator changes according to the fourth embodiment of the present invention.
Fig. 9 is a schematic diagram of a change of a degree of distinction index of a method for evaluating importance of different nodes when a μ parameter of the LFR network generator changes according to a fourth embodiment of the present invention.
Fig. 10 is a schematic diagram of a change of a degree of distinction index of a method for evaluating importance of different nodes when a k parameter of an LFR network generator changes according to a fourth embodiment of the present invention.
Fig. 11 is a schematic diagram of a change of a degree of distinction index of a method for evaluating importance of different nodes when a λ parameter of the LFR network generator changes according to a fourth embodiment of the present invention.
Detailed Description
The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate preferred embodiments of the invention and together with the description, serve to explain the principles of the invention and not to limit the scope of the invention.
Example one
A social network node importance assessment method is disclosed. As shown in fig. 1, the method comprises the following steps:
step S1, obtaining a K index value of a node in a given social network;
step S2, determining the importance of the node to be evaluated according to the sum of the K index values of all the neighbor nodes of the node to be evaluated;
and step S3, evaluating the importance of the node to be evaluated based on the importance of the node to be evaluated.
Compared with the prior art, the social network node importance evaluation method provided by the embodiment makes full use of the connection information between the social network nodes, comprehensively considers the influence of the neighbor nodes, not only makes full use of the influence of the nodes, but also makes full use of the influence of the neighbor nodes of the nodes, can effectively distinguish the importance of the nodes with the same H index value, can quickly and accurately evaluate the importance of the nodes in the social network, has high distinguishing degree of evaluation results, can analyze a large-scale social network, is convenient to quickly find the importance nodes, and has strong adaptability.
Specifically, in step S1, the step of obtaining the K index value of the node in the social network includes the following steps:
step S101, solving an H index value of a node in a given social network;
firstly, solving the values of all nodes in a given social network; and then according to the definition of the H index, calculating the H index value H of the node in the social network according to the solved value (the H index value H of the node indicates that the degrees of at least H neighbor nodes of the node are all larger than H). Preferably, the H index value H is calculated by a quadratic search method.
S102, selecting neighbor nodes with the values not smaller than the H index value of the node from a neighbor node set of the node;
and step S103, determining the K index value of the node according to the sum of the values of the neighbor nodes selected in the step S102.
The K index value calculation formula of the node is as follows:
in the formula, KiK index value, h, representing node i in social networkiRepresents the H index value of the node i,representing the sum of the values of the selected neighbor nodes.
In step S2, determining the importance of the node to be evaluated according to the sum of the K index values of all neighboring nodes of the node to be evaluated, where the formula is:
in the formula, gamma(i)A set of neighbor nodes, K, of a node i to be evaluatedjIs the K index value, LK, of the neighbor node j(i)Is the importance of the node i to be evaluated.
In step S3, the importance of the node is evaluated based on the determined importance of the node. After the importance degrees of all the nodes in the social network are obtained, all the nodes are ranked according to the importance degrees, and then the importance of the nodes is evaluated (the larger the importance degree value is, the higher the importance is).
Example two
A social network node importance assessment system is disclosed. As shown in fig. 2, includes: the node K index value obtaining module, the node importance determining module and the node importance evaluating module are used for obtaining the node K index value; wherein,
the node K index value obtaining module is used for obtaining a K index value of a node in a given social network;
the node importance determining module is used for determining the importance of the node to be evaluated according to the sum of the K index values of all the neighbor nodes of the node to be evaluated and outputting the importance to the node importance evaluating module;
and the node importance evaluation module evaluates the importance of the node to be evaluated according to the received importance of the node.
Compared with the prior art, the social network node importance evaluation system provided by the embodiment makes full use of the connection information between the social network nodes, comprehensively considers the influence of the neighbor nodes, not only utilizes the influence of the nodes, but also makes full use of the influence of the neighbor nodes, can effectively distinguish the importance of the nodes with the same H index value, can quickly and accurately evaluate the importance of the nodes in the social network, has high distinguishing degree of evaluation results, can analyze a large-scale social network, is convenient to quickly find the importance nodes, and has strong adaptability.
Specifically, the node K index value obtaining module comprises an H index value obtaining unit, a neighbor node selecting unit and a K index value determining unit; wherein,
the H index value obtaining unit is used for obtaining an H index value of a node in a given social network and outputting the H index value to the neighbor node selecting unit;
the H index value obtaining unit includes a value obtaining subunit and an H index value obtaining subunit: the value solving subunit is used for solving the values of all the nodes in the social network and outputting the values to the H index value solving subunit; the H index value obtaining subunit is used for calculating the H index value of the node in the social network by adopting a binary search method.
A neighbor node selecting unit for selecting neighbor nodes with values not less than the H index value of the node from the neighbor node set of the node;
and the K index value determining unit determines the K index value of the node according to the sum of the values of the neighbor nodes selected by the neighbor node selecting unit.
It is emphasized that, in the K index value determination unit, the K index value calculation formula of the node is:
in the formula, KiK index value, h, representing node i in social networkiRepresents the H index value of the node i,representing the sum of the values of the selected neighbor nodes.
The node importance determining module is used for determining the importance of the node to be evaluated and outputting the importance to the node importance evaluating module; specifically, the importance of the node to be evaluated is determined according to the sum of the K index values of all the neighbor nodes of the node to be evaluated, and the formula is as follows:
in the formula, gamma(i)A set of neighbor nodes, K, of a node i to be evaluatedjIs the K index value, LK, of the neighbor node j(i)Is the importance of the node i to be evaluated.
And the node importance evaluation module is used for acquiring the importance of the node importance determination module and evaluating the importance of the node. After the importance degrees of all the nodes in the social network are obtained, all the nodes are ranked according to the importance degrees, and then the importance degrees of the nodes are evaluated (the larger the importance degree value is, the higher the importance degree value is).
EXAMPLE III
The social network node importance evaluation method in the first embodiment is used for evaluating the importance of the network node, taking a simple example network as an example. The topology of an exemplary network is shown in fig. 3, comprising 17 nodes and 21 edges. The method specifically comprises the following steps:
1) for a given example network, the H-index value of a node in the social network is calculated using a binary search method according to the definition of the H-index, as shown in table 1.
Table 1: h index value of example network node
| Node numbering | Value of H index | Node numbering | Value of H index |
| 1 | 1 | 10 | 3 |
| 2 | 1 | 11 | 3 |
| 3 | 1 | 12 | 3 |
| 4 | 1 | 13 | 3 |
| 5 | 1 | 14 | 1 |
| 6 | 2 | 15 | 2 |
| 7 | 3 | 16 | 1 |
| 8 | 2 | 17 | 1 |
| 9 | 1 |
2) Selecting neighbor nodes with the values not smaller than the H index value of the node from the neighbor node set of the node; and calculating the sum of the values of the selected neighbor nodes to obtain the K index value of the node. The K index values for example network nodes are shown in table 2.
Table 2: k index value for an example network node
| Node numbering | Value of K index | Node numbering | Value of K index |
| 1 | 1.75 | 10 | 3.47 |
| 2 | 1.67 | 11 | 3.36 |
| 3 | 1.67 | 12 | 3.44 |
| 4 | 1.83 | 13 | 3.44 |
| 5 | 1.75 | 14 | 1.83 |
| 6 | 2.33 | 15 | 2.60 |
| 7 | 3.31 | 16 | 1.75 |
| 8 | 2.56 | 17 | 1.50 |
| 9 | 1.67 |
3) And calculating the sum of the K index values of all the neighbor nodes of the node as the importance of the node. According to the node importance calculation formula in the first embodiment of the present invention, the calculated importance of the exemplary network node is shown in table 3.
Table 3: importance of example network nodes
| Node numbering | Degree of importance | Node numbering | Degree of importance |
| 1 | 2.33 | 10 | 17.93 |
| 2 | 1.83 | 11 | 10.35 |
| 3 | 1.83 | 12 | 12.87 |
| 4 | 5.67 | 13 | 12.87 |
| 5 | 2.33 | 14 | 3.47 |
| 6 | 8.64 | 15 | 8.63 |
| 7 | 8.36 | 16 | 4.10 |
| 8 | 8.44 | 17 | 1.75 |
| 9 | 2.56 |
As can be seen from table 3, almost all example network nodes are given different degrees of importance. The importance of example network nodes is well separated, thereby making the importance assessment result separation higher.
Example four
In this embodiment, a real network and a manual network are taken as examples, and the social network node importance assessment method in the first embodiment is used for node importance assessment of the network, and compared with other existing node importance assessment methods. Typical methods of selection include: h index (H for short), K index (K for short), PageRank algorithm (PR for short), classical K kernel decomposition algorithm (KS for short), KS-IF algorithm (KSIF for short), MDD algorithm (MDD for short) and the method (LK for short). In order to better evaluate the performance of various importance evaluation methods, a discrimination index M is introduced here. The discrimination index is defined as follows:
wherein R is a rank vector of network node importance, n is the total rank number of the vector R, n isrIs the number of nodes in the r-th level. If all the nodes are in the same importance level, the value of the discrimination index M is 0, and the corresponding evaluation method cannot distinguish the importance of each node. If each importance level only contains 1 node, and the value of the discrimination index M is 1, the corresponding evaluation method can effectively discriminate the importance of each node and has the strongest discrimination capability.
First, an example network shown in the third embodiment (fig. 3) is selected, the importance of the nodes of the example network is evaluated by the above 7 methods, the nodes are sorted according to the importance, and the sorting result is shown in table 4 (each column of table 4 corresponds to one importance evaluation method, nodes at the same level have the same importance, and "other" indicates all the remaining nodes). As can be seen from table 4, compared with the existing 6 typical methods, the method disclosed by the present invention can accurately and finely distinguish the importance of the network nodes, and exemplarily, the number of the nodes of each importance level is at most 2.
Table 4: ranking results of example network node importance
In order to further explain the beneficial effect of the method of the invention, 11 real networks (including Karate Club network, Dolphin network, Jazz network, Prison network, NetScience network, Book network, Celegan network, E-mail network, Blogs network, PGP network and Enron network) with different scales are selected, and the discrimination indexes M of the 7 importance evaluation methods are analyzed and compared. Table 5 shows the ability of 7 importance assessment methods to differentiate the importance of 11 real network nodes. It can be seen that: aiming at the 11 selected real networks, the method disclosed by the invention can obtain the maximum division value. Compared with other 6 node importance evaluation methods, the method provided by the invention can more carefully and accurately identify the importance of the real network node.
Table 5: capability of different importance evaluation methods for distinguishing importance of real network nodes
| Network name | Node point | Number of edges | M(PR) | M(KS) | M(H) | M(K) | M(KSIF) | M(MDD) | M(LK) |
| KarateClub | 34 | 78 | 0.9542 | 0.4958 | 0.5766 | 0.9542 | 0.9542 | 0.7536 | 0.9542 |
| Dolphins | 62 | 159 | 0.9979 | 0.3769 | 0.6841 | 0.9748 | 0.9979 | 0.9041 | 0.9979 |
| Prison | 67 | 182 | 0.9964 | 0.3070 | 0.6031 | 0.9722 | 0.9928 | 0.8672 | 0.9964 |
| Book | 105 | 441 | 1.0000 | 0.4949 | 0.7067 | 0.9952 | 1.0000 | 0.9077 | 1.0000 |
| Football | 115 | 613 | 1.0000 | 0.0003 | 0.2349 | 0.9316 | 0.9991 | 0.6089 | 1.0000 |
| Jazz | 198 | 2742 | 0.9993 | 0.7944 | 0.9383 | 0.9990 | 0.9993 | 0.9882 | 0.9993 |
| Celegan | 379 | 914 | 0.9951 | 0.6421 | 0.6825 | 0.9848 | 0.9944 | 0.8748 | 0.9950 |
| NetScience | 453 | 2025 | 0.9992 | 0.6962 | 0.7311 | 0.9959 | 0.9975 | 0.8215 | 0.9983 |
| 1133 | 10903 | 0.9999 | 0.8088 | 0.8583 | 0.9979 | 0.9996 | 0.9229 | 0.9999 | |
| Blogs | 1490 | 16718 | 0.9993 | 0.9058 | 0.9264 | 0.9991 | 0.9992 | 0.9443 | 0.9993 |
| PGP | 10680 | 24316 | 0.9997 | 0.4806 | 0.5172 | 0.9942 | 0.9935 | 0.6678 | 0.9981 |
In order to more intuitively show the beneficial effects of the method of the invention, the data in table 5 is shown by using a Complementary Cumulative Distribution Function (CCDF). Fig. 4 to 7 show CCDF of 4 networks (network in the third embodiment, Karate Club network, Dolphin network, and Celegan network), respectively. According to the principle of CCDF, if the number of nodes in the same importance level is larger, the CCDF will decrease faster, otherwise, the CCDF will decrease slowly along the diagonal. As can be seen from FIGS. 4-7, the CCDF of the method of the present invention decreases slowly along the diagonal line, which shows that the method of the present invention can distinguish the difference in importance between nodes in the social network.
In addition, an artificial social network is generated by means of the LFR network generator, and the method is evaluated by means of the artificial social network. The LFR network generator has 4 important parameters, which are the node size n (number of nodes), the average node degree k (average degree of nodes), the community structure mixture parameter μ (mismatch parameter of community structure) and the power-law distribution λ (power-law of hierarchy distribution). The change of the above 4 parameters will affect the topology of the artificial social network. Fig. 8 to 11 show the change of the discrimination index M of different node importance evaluation methods when 4 parameters are kept changed for 1 parameter and the remaining 3 parameters are not changed. It can be seen that: aiming at the artificial social network, the method can obtain the maximum division value. Compared with other 5 node importance evaluation methods (H, K, KS-IF and MDD), the method provided by the invention can be used for identifying the node importance of the artificial social network more carefully and accurately.
Those skilled in the art will appreciate that all or part of the flow of the method implementing the above embodiments may be implemented by hardware associated with computer program instructions, and the program may be stored in a computer readable storage medium. The computer readable storage medium is a magnetic disk, an optical disk, a read-only memory or a random access memory.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention.
Claims (10)
1. A social network node importance evaluation method is characterized by comprising the following steps:
solving a K index value of a node in a given social network;
determining the importance of the node to be evaluated according to the sum of the K index values of all the neighbor nodes of the node to be evaluated;
and evaluating the importance of the node to be evaluated based on the importance of the node to be evaluated.
2. The method of claim 1, wherein said deriving a K-index value for a node in a given social network comprises:
solving an H index value of a node in a given social network;
selecting neighbor nodes with the values not less than the H index value of the node from the neighbor node set of the node;
and determining the K index value of the node according to the sum of the values of the selected neighbor nodes.
3. The method according to claim 2, wherein the K index value of the selected neighbor node is determined according to the sum of the values of the selected neighbor node, and the formula is as follows:
in the formula, KiK index value, h, representing node i in social networkiRepresents the H index value of the node i,representing the sum of the values of the selected neighbor nodes.
4. The method according to one of claims 1 to 3, characterized in that the importance of the node to be evaluated is determined according to the sum of the K index values of all the neighbor nodes of the node to be evaluated, and the formula is:
in the formula, gamma(i)Set of neighbor nodes, K, for node i to be evaluatedjIs the K index value, LK, of the neighbor node j(i)Is the importance of the node i to be evaluated.
5. The method of claim 4, wherein said deriving the H index value for a node in a given social network comprises the steps of:
solving the values of all nodes in the social network;
and calculating the H index value of the node in the social network by adopting a binary search method.
6. A social network node importance evaluation system, comprising:
the node K index value obtaining module is used for obtaining a K index value of a node in a given social network;
the node importance determining module is used for determining the importance of the node to be evaluated according to the sum of the K index values of all the neighbor nodes of the node to be evaluated and outputting the importance to the node importance evaluating module;
and the node importance evaluation module evaluates the importance of the node to be evaluated according to the received importance of the node.
7. The system according to claim 6, wherein said node K index value obtaining module comprises an H index value obtaining unit, a neighbor node selecting unit, and a K index value determining unit;
the H index value obtaining unit is used for obtaining the H index value of the node in the given social network and outputting the H index value to the neighbor node selecting unit;
the neighbor node selection unit selects neighbor nodes with the values not less than the H index value of the node from the neighbor node set of the node;
and the K index value determining unit determines the K index value of the node according to the sum of the values of the neighbor nodes selected by the neighbor node selecting unit.
8. The system of claim 7, wherein the K index value of the selected neighbor node is determined according to the sum of the values of the selected neighbor node, and the formula is:
in the formula, KiK index value, h, representing node i in social networkiRepresents the H index value of the node i,representing the sum of the values of the selected neighbor nodes.
9. The system according to any of claims 6-8, wherein the importance of the node to be evaluated is determined according to the sum of the K index values of all neighboring nodes of the node to be evaluated, and the formula is:
in the formula, gamma(i)A set of neighbor nodes, K, of a node i to be evaluatedjIs the K index value, LK, of the neighbor node j(i)Is the importance of the node i to be evaluated.
10. The system according to claim 9, wherein said node H-exponent value evaluation unit comprises a value evaluation subunit, an H-exponent value evaluation subunit:
the value solving subunit is used for solving the values of all nodes in the social network and outputting the values to the H index value solving subunit;
and the H index value obtaining subunit is used for calculating the H index value of the node in the social network by adopting a binary search method.
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