CN109471994A - Network key nodal test method and system - Google Patents
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- CN109471994A CN109471994A CN201811230309.4A CN201811230309A CN109471994A CN 109471994 A CN109471994 A CN 109471994A CN 201811230309 A CN201811230309 A CN 201811230309A CN 109471994 A CN109471994 A CN 109471994A
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Abstract
This application discloses a kind of network key nodal test methods.This method includes S1. in set of network nodes to be determined, obtains a network node to be determined；S2. the degree of the network node is obtained；S3. the nucleus number of the network node is obtained according to the degree according to the first preset rules；S4. the nucleus number entropy of the network node is obtained according to the nucleus number according to the second preset rules；S5. in the set of network nodes to be determined, next network node to be determined is obtained, S2 to S4 is repeated, until the network node in the set of network nodes all obtains nucleus number entropy；S6. the nucleus number entropy of more each network node obtains network key node according to third preset rules.Present invention also provides a kind of network key nodal test systems using the above method.Present application addresses the high network nodes of similarity in the related technology cannot be distinguished, the technical problem of information communication effect difference caused by key node judgement inaccuracy.
Description
Technical field
This application involves fields of communication technology, in particular to a kind of network key nodal test method and system.
Background technique
In recent years, Complex Networks Theory penetrates into the every field of social life, especially as various social networks, enterprise
The high speed development of office network, event network, human society can almost be abstracted as a huge network world.In life
Network can be mainly divided into Types Below: 1) from macroscopic perspective, including Internet network, WWW, electric power networks, traffic
Network etc.；2) from microcosmic angle, including protein network, neural network, metabolism network, gene genetic network etc.；3) from
Human society angle, including transmission, gossip propagation, Actor Collaboration Network network etc..
It can be seen that many systems in social life can be abstracted as a network, element can be taken out in system
As for node, and connection relationship rich and varied between these elements can be abstracted as side.Since system is stronger in real event
Complexity, the network abstracted from these systems may have a large amount of node and side, and crisscross multiple between node
Miscellaneous connection relationship makes network have complicated topological structure.Under normal circumstances, by these networks with complex topology structure
Referred to as complex network.Important node has large effect to the structure and function of network in network, therefore excavates important node
It can be that people's lives solve more problems and provide strong foundation.Due to the heterogeneity of network, knot of the node to network
Structure and function have different influences, and those there is the node especially influenced to be referred to as crucial section the structure and function of network
Point.And excavating the key node in network becomes the problem of can not be ignored.The outburst for such as controlling pandemic propagates, avoids advising greatly
The power failure of mould is excavated on social networks to propagating most influential user, the most basic protein that prediction sustains life etc..
The shortest path between node is often only considered when choosing key node group in the related technology when calculate node similitude
Electrical path length, cause choose key node between it is similar, can not achieve information propagation efficiency maximization；Other the relevant technologies
In when considering node similitude, only give up and selected the biggish node of key node similarity, be unable to fully reduce crucial section
Aggregation extent between point group interior joint, the key node group propagation efficiency of selection are low.
For the problems in above-mentioned the relevant technologies, currently no effective solution has been proposed.
Summary of the invention
The main purpose of the application is to provide a kind of network key nodal test method and system, at least the above to solve
One of the problems in the relevant technologies.
To achieve the goals above, according to the one aspect of the application, a kind of network key nodal test method is provided,
This method comprises:
S1. in set of network nodes to be determined, a network node to be determined is obtained；S2. the network is obtained
The degree of node；S3. the nucleus number of the network node is obtained according to the degree according to the first preset rules；S4. according to second
Preset rules obtain the nucleus number entropy of the network node according to the nucleus number；S5. in the set of network nodes to be determined,
Next network node to be determined is obtained, S2 to S4 is repeated, until the network node in the set of network nodes all obtains
Obtain nucleus number entropy；S6. the nucleus number entropy of more each network node obtains network key node according to third preset rules.
Further, method as the aforementioned, the S6 include: S61. by each network node according to the big of nucleus number entropy
It is small to be ranked up；S62. it according to the 4th preset rules, calculates in the ranking results between the two neighboring network node
Distance；S63. the network key node is obtained according to the distance and the nucleus number entropy.
Further, method as the aforementioned, the S63 include: S631. according to the 5th preset rules according to the distance and
The nucleus number entropy calculates group's centrality of each network node；S632. by the group center of each network node
Property sequence, obtain the network key node according to the 6th preset rules.
Further, method as the aforementioned, the S632 include: by group's centrality of each network node according to
Size carries out descending arrangement, and choosing preceding preset number node in the sequence is the network key node.
Further, method as the aforementioned, the S62 include: that S621. obtains node similarity；S622. pre- according to the 7th
If the distance between two neighboring described network node in the regular ranking results according to the node similarity calculation.
To achieve the goals above, according to the another aspect of the application, a kind of network key nodal test system is provided,
The system includes Traversal Unit, information acquisition unit, the first computing unit, the second computing unit and key point judging unit,
In:
The Traversal Unit, in set of network nodes to be determined, obtaining each and every one network node to be determined；Institute
Information acquisition unit is stated, for obtaining the degree of the network node；First computing unit, for according to the first default rule
The nucleus number of the network node is then obtained according to the degree；Second computing unit, for according to the second preset rules root
The nucleus number entropy of the network node is obtained according to the nucleus number；The key point judging unit is used for more each network section
The nucleus number entropy of point obtains network key node according to third preset rules.
Further, system as the aforementioned, the key point judging unit, including the first sequencing unit and third calculate single
Member, in which: first sequencing unit, for each network node to be ranked up according to the size of nucleus number entropy；It is described
Third computing unit, for calculating in the ranking results between the two neighboring network node according to the 4th preset rules
Distance；The key point judging unit is also used to obtain the network key node according to the distance and the nucleus number entropy.
Further, system as the aforementioned, the key point judging unit further include the 4th computing unit and the second sequence
Unit, in which: the 4th computing unit, it is each for being calculated according to the 5th preset rules according to the distance and the nucleus number entropy
Group's centrality of a network node；Second sequencing unit, for by the group center of each network node
Property sequence；The key point judging unit is also used to the ranking results according to second sequencing unit according to the 6th default rule
Then obtain the network key node.
Further, system as the aforementioned, second sequencing unit are also used to the group of each network node
Centrality carries out descending arrangement according to size；The key point judging unit is also used to choose preceding preset number in the sequence
A node is the network key node.
Further, system as the aforementioned, the third computing unit, is also used to obtain node similarity, according to
7th preset rules in the ranking results according to the node similarity calculation between the two neighboring network node away from
From.
In the embodiment of the present application, by the way of according to node degree, nucleus number calculate node nucleus number entropy, pass through phase same core
Several nodes are further distinguished, and have achieved the purpose that distinguish the high node of similarity, to realize reduction key node
The technical effect of the aggregation extent of group, so solve the high network node of similarity in the related technology cannot be distinguished, crucial section
The technical problem of information communication effect difference caused by point judgement inaccuracy.
Detailed description of the invention
The attached drawing constituted part of this application is used to provide further understanding of the present application, so that the application's is other
Feature, objects and advantages become more apparent upon.The illustrative examples attached drawing and its explanation of the application is for explaining the application, not
Constitute the improper restriction to the application.In the accompanying drawings:
Fig. 1 is a kind of flow diagram for network key nodal test method that the application one embodiment provides；
Fig. 2 is the example network node connection schematic diagram that the application one embodiment provides；
Fig. 3 is a kind of flow diagram for network key node judgment method that the application one embodiment provides；And
Fig. 4 is a kind of structural schematic diagram for network key nodal test system that the application one embodiment provides.
Specific embodiment
In order to make those skilled in the art more fully understand application scheme, below in conjunction in the embodiment of the present application
Attached drawing, the technical scheme in the embodiment of the application is clearly and completely described, it is clear that described embodiment is only
The embodiment of the application a part, instead of all the embodiments.Based on the embodiment in the application, ordinary skill people
Member's every other embodiment obtained without making creative work, all should belong to the model of the application protection
It encloses.
It should be noted that in the absence of conflict, the features in the embodiments and the embodiments of the present application can phase
Mutually combination.The application is described in detail below with reference to the accompanying drawings and in conjunction with the embodiments.
According to embodiments of the present invention, a kind of network key nodal test method is provided, as shown in Figure 1, this method includes
Following step:
S1. in set of network nodes to be determined, a network node to be determined is obtained；
S2. the degree of the network node is obtained；Specifically, the number of edges that network node is connect with the external world is the network
Degree, degree are most direct indexs in complex network, and degree centrality thinks that the connection number of edges of node is more, and node is more important.
S3. the nucleus number of the network node is obtained according to the degree according to the first preset rules；Specifically, nucleus number is benefit
It being calculated with shell decomposition method, the node importance that position is in network center is maximum, once successively decreases to network edge, each shell
The nucleus number of each node in layer is identical, and the nucleus number of each node in every shell is greater than or equal to the degree of the node.
S4. the nucleus number entropy of the network node is obtained according to the nucleus number according to the second preset rules；Specifically, nucleus number entropy
It is calculated by information entropy theory, the node of the different shell numbers of node connection is more, and the importance of node is bigger.For example,
As shown in Fig. 2, it is respectively 1,2 that node 12, which is connected to the number of nodes that nucleus number is 1,2, node 1 is connected to the node that nucleus number is 1,2
Quantity is respectively 2,4, and the nucleus number entropy of node 12 and node 1 is respectively 0.4665 and 0.6539.
S5. in the set of network nodes to be determined, next network node to be determined is obtained, repeats S2 extremely
S4, until the network node in the set of network nodes all obtains nucleus number entropy；
S6. the nucleus number entropy of more each network node obtains network key node according to third preset rules.
Further, as shown in figure 3, the S6 includes:
S61. each network node is ranked up according to the size of nucleus number entropy；
S62. according to the 4th preset rules, calculate in the ranking results between the two neighboring network node away from
From；
Further, as described in Figure 3, S621. obtains node similarity；Specifically, node similarity can be based on two
Similarity indices are calculated, and an index is the similarity indices based on local message, such as common neighbours' number of two nodes
Etc.；Another index is the similarity indices based on path, such as compares the path between two nodes and the neighbours of neighbouring 3 rank
The sum of etc..
S622. described in two neighboring in the ranking results according to the node similarity calculation according to the 7th preset rules
The distance between network node.Specifically, macroscopically the similarity of two nodes is bigger, and the distance between two network nodes are just
It is closer, the numerical relation of experience acquisition between the two can be calculated according to specific.
S63. the network key node is obtained according to the distance and the nucleus number entropy.
Further, as shown in figure 3, S631. according to the 5th preset rules according to the distance and the nucleus number entropy meter
Calculate group's centrality of each network node；
S632. group's centrality of each network node is sorted, obtains the network according to the 6th preset rules
Key node；Group's centrality of each network node is subjected to descending arrangement according to size, before choosing in the sequence
Preset number node is the network key node.Specifically, some system edges (such as degree is 1) nodes, itself and its
The similarity of remaining node is just smaller, but will be bigger at a distance from node, but these fringe nodes are detrimental to information propagation
, so introducing group's centrality index, the index of nucleus number entropy and distance is subjected to equilibrium, equalizing coefficient can be calculated by experience
It obtains, rationally characterizes the importance of node whereby.
It can be seen from the above description that the present invention realizes following technical effect:
In the embodiment of the present application, by the way of according to node degree, nucleus number calculate node nucleus number entropy, pass through phase same core
Several nodes are further distinguished, and have achieved the purpose that distinguish the high node of similarity, to realize reduction key node
The technical effect of the aggregation extent of group；Local message index and path metric are combined when similarity calculation, between the phase node
Considering for composite factor has been carried out like degree, has effectively reduced the aggregation extent of key node；It is comprehensive after converting distance for similarity
It closes node itself nucleus number entropy and then considers, the factor for judging node importance more fully, solves similarity in the related technology
The technical issues of high network node cannot be distinguished, information communication effect caused by key node judgement inaccuracy is poor, low efficiency.
It should be noted that the description and claims of this application and term " first " in above-mentioned attached drawing, "
Two " etc. be to be used to distinguish similar objects, without being used to describe a particular order or precedence order.For example, the first default rule
It is then only used for distinguishing the rule for calculating nucleus number and nucleus number entropy with the second preset rules, it should be appreciated that and non-depicted two regular productions
Raw sequence, for use in embodiments herein described herein.In addition, term " includes " and " having " and theirs is any
Deformation, it is intended that cover it is non-exclusive include, for example, S63 may include being not clearly listed for obtaining network key
The intrinsic other steps of the method for node, distance, nucleus number entropy as described in acquisition etc. as described in acquisition.
It should be noted that step shown in the flowchart of the accompanying drawings can be in such as a group of computer-executable instructions
It is executed in computer system, although also, logical order is shown in flow charts, and it in some cases, can be with not
The sequence being same as herein executes shown or described step.
According to embodiments of the present invention, a kind of network pass for implementing above-mentioned network key nodal test method is additionally provided
Key nodal test system, as shown in figure 4, the system includes Traversal Unit, information acquisition unit, the first computing unit, the second meter
Calculate unit and key point judging unit, in which:
The Traversal Unit, in set of network nodes to be determined, obtaining each and every one network node to be determined；
The information acquisition unit, for obtaining the degree of the network node；Specifically, network node is connect with the external world
Number of edges be the network degree, degree is most direct index in complex network, and degree centrality thinks the connection side of node
Number is more, and node is more important.
First computing unit, for obtaining the core of the network node according to the degree according to the first preset rules
Number；Specifically, nucleus number is to be calculated using shell decomposition method, and the node importance that position is in network center is maximum, to network
Edge once successively decreases, and the nucleus number of each node in each shell is identical, and the nucleus number of each node in every shell is greater than or waits
In the degree of the node.
Second computing unit, for obtaining the core of the network node according to the nucleus number according to the second preset rules
Number entropy；Specifically, nucleus number entropy is calculated by information entropy theory, and the node of the different shell numbers of node connection is more, node
Importance is bigger.For example, node 1 connects as shown in Fig. 2, it is respectively 1,2 that node 12, which is connected to the number of nodes that nucleus number is 1,2,
Having connect the number of nodes that nucleus number is 1,2 is respectively 2,4, and the nucleus number entropy of node 12 and node 1 is respectively 0.4665 and 0.6539.
The key point judging unit, for the nucleus number entropy of more each network node, according to third preset rules
Obtain network key node.
Further, the key point judging unit, including the first sequencing unit, third computing unit, the 4th calculating list
Member and the second sequencing unit, in which:
First sequencing unit, for each network node to be ranked up according to the size of nucleus number entropy；
The third computing unit, it is two neighboring described in the ranking results for calculating according to the 4th preset rules
The distance between network node；Further, the third computing unit, is also used to obtain node similarity, pre- according to the 7th
If the distance between two neighboring described network node in the regular ranking results according to the node similarity calculation.Specifically
Ground, node similarity can be calculated based on two similarity indices, and an index is the similarity indices based on local message,
Common neighbours' number of such as two nodes；Another index is the similarity indices based on path, such as compare two nodes with
The sum of path between the neighbours of neighbouring 3 rank etc.；Macroscopically the similarity of two nodes is bigger, between two network nodes
Distance is closer, can calculate the numerical relation of experience acquisition between the two according to specific.
4th computing unit, it is each for being calculated according to the 5th preset rules according to the distance and the nucleus number entropy
Group's centrality of the network node；
Second sequencing unit, for group's centrality of each network node to sort；
Second sequencing unit is also used to group's centrality of each network node carrying out descending according to size
Arrangement；
The key point judging unit, being also used to choose preceding preset number node in the sequence is the network key
Node.
Specifically, some system edges (such as degree is 1) nodes, itself is just smaller with the similarity of remaining node, but with
The distance of node will be bigger, but these fringe nodes are detrimental to information propagation, so group's centrality index is introduced,
The index of nucleus number entropy and distance is subjected to equilibrium, equalizing coefficient can be calculated by experience, rationally characterize the weight of node whereby
The property wanted.
Obviously, those skilled in the art should be understood that each module of the above invention or each step can be with general
Computing device realize that they can be concentrated on a single computing device, or be distributed in multiple computing devices and formed
Network on, optionally, they can be realized with the program code that computing device can perform, it is thus possible to which they are stored
Be performed by computing device in the storage device, perhaps they are fabricated to each integrated circuit modules or by they
In multiple modules or step be fabricated to single integrated circuit module to realize.In this way, the present invention is not limited to any specific
Hardware and software combines.
The foregoing is merely preferred embodiment of the present application, are not intended to limit this application, for the skill of this field
For art personnel, various changes and changes are possible in this application.Within the spirit and principles of this application, made any to repair
Change, equivalent replacement, improvement etc., should be included within the scope of protection of this application.
Claims (10)
1. a kind of network key nodal test method characterized by comprising
S1. in set of network nodes to be determined, a network node to be determined is obtained；
S2. the degree of the network node is obtained；
S3. the nucleus number of the network node is obtained according to the degree according to the first preset rules；
S4. the nucleus number entropy of the network node is obtained according to the nucleus number according to the second preset rules；
S5. in the set of network nodes to be determined, next network node to be determined is obtained, repeats S2 to S4, directly
Nucleus number entropy is all obtained to the network node in the set of network nodes；
S6. the nucleus number entropy of more each network node obtains network key node according to third preset rules.
2. the method according to claim 1, wherein the S6 includes:
S61. each network node is ranked up according to the size of nucleus number entropy；
S62. according to the 4th preset rules, the distance between two neighboring described network node in the ranking results is calculated；
S63. the network key node is obtained according to the distance and the nucleus number entropy.
3. according to the method described in claim 2, it is characterized in that, the S63 includes:
S631. it is calculated in the group of each network node according to the 5th preset rules according to the distance and the nucleus number entropy
Disposition；
S632. group's centrality of each network node is sorted, obtains the network key according to the 6th preset rules
Node.
4. according to the method described in claim 3, it is characterized in that, the S632 includes: by the group of each network node
Body centrality carries out descending arrangement according to size, and choosing preceding preset number node in the sequence is the network key section
Point.
5. according to the method described in claim 2, it is characterized in that, the S62 includes:
S621. node similarity is obtained；
S622. the two neighboring network in the ranking results according to the node similarity calculation according to the 7th preset rules
The distance between node.
6. a kind of network key nodal test system, which is characterized in that calculated including Traversal Unit, information acquisition unit, first
Unit, the second computing unit and key point judging unit, in which:
The Traversal Unit, in set of network nodes to be determined, obtaining each and every one network node to be determined；
The information acquisition unit, for obtaining the degree of the network node；
First computing unit, for obtaining the nucleus number of the network node according to the degree according to the first preset rules；
Second computing unit, for obtaining the nucleus number of the network node according to the nucleus number according to the second preset rules
Entropy；
The key point judging unit is obtained for the nucleus number entropy of more each network node according to third preset rules
Network key node.
7. system according to claim 6, which is characterized in that the key point judging unit, including the first sequencing unit
With third computing unit, in which:
First sequencing unit, for each network node to be ranked up according to the size of nucleus number entropy；
The third computing unit, for calculating the two neighboring network in the ranking results according to the 4th preset rules
The distance between node；
The key point judging unit is also used to obtain the network key node according to the distance and the nucleus number entropy.
8. system according to claim 7, which is characterized in that the key point judging unit further includes that the 4th calculating is single
Member and the second sequencing unit, in which:
4th computing unit, it is each described for being calculated according to the 5th preset rules according to the distance and the nucleus number entropy
Group's centrality of network node；
Second sequencing unit, for group's centrality of each network node to sort；
The key point judging unit is also used to be obtained according to the ranking results of second sequencing unit according to the 6th preset rules
Obtain the network key node.
9. system according to claim 8, which is characterized in that second sequencing unit is also used to each net
Group's centrality of network node carries out descending arrangement according to size；
The key point judging unit, being also used to choose preceding preset number node in the sequence is the network key section
Point.
10. system according to claim 7, which is characterized in that the third computing unit is also used to obtain node
Similarity, according to the 7th preset rules two neighboring network section in the ranking results according to the node similarity calculation
The distance between point.
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| CN114826936A (en) * | 2022-04-14 | 2022-07-29 | 中国人民解放军国防科技大学 | Method and system for determining key node set of weighted network communication efficiency |
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| CN111126758A (en) * | 2019-11-15 | 2020-05-08 | 中南大学 | Academic team influence propagation prediction method, device and storage medium |
| CN114466022A (en) * | 2021-12-31 | 2022-05-10 | 苏州浪潮智能科技有限公司 | Method, device and medium for acquiring server seed nodes in cluster |
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| CN114866437A (en) * | 2022-04-19 | 2022-08-05 | 北京博睿宏远数据科技股份有限公司 | Node detection method, device, equipment and medium |
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Application publication date: 20190315 |