CN107896166B - Method and device for acquiring network core node - Google Patents
Method and device for acquiring network core node Download PDFInfo
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- CN107896166B CN107896166B CN201711221173.6A CN201711221173A CN107896166B CN 107896166 B CN107896166 B CN 107896166B CN 201711221173 A CN201711221173 A CN 201711221173A CN 107896166 B CN107896166 B CN 107896166B
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
- H04L41/14—Network analysis or design
- H04L41/142—Network analysis or design using statistical or mathematical methods
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
- H04L41/50—Network service management, e.g. ensuring proper service fulfilment according to agreements
- H04L41/5041—Network service management, e.g. ensuring proper service fulfilment according to agreements characterised by the time relationship between creation and deployment of a service
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L43/00—Arrangements for monitoring or testing data switching networks
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- H04L43/091—Measuring contribution of individual network components to actual service level
Abstract
The invention discloses a method and a device for acquiring a network core node. The method for acquiring the network core node comprises the following steps: responding to a core node acquisition instruction to acquire a target network; wherein, the target network comprises at least one network node; calculating the node influence of each network node, and setting M network nodes with the maximum node influence as first network core nodes respectively; wherein M is more than or equal to 1; calculating the node information redundancy of each first network core node, and setting the N first network core nodes with the minimum node information redundancy as second network core nodes respectively; wherein N is more than or equal to 1, and M is more than or equal to N. By adopting the invention, the accuracy of the acquired network core node can be improved.
Description
Technical Field
The present invention relates to the field of computer technologies, and in particular, to a method and an apparatus for acquiring a network core node.
Background
The technology of analyzing influence of network nodes has been widely applied to various fields such as economic analysis, social practice, biological research, and network communication. For example, in economic analysis, impact analysis techniques of network nodes may be used to find core countries that are most influential in promoting trade circulation and development; in social practice, the influence analysis technology of network nodes can be used for finding core customers capable of quickly spreading product information; in biological research, the influence analysis technology of network nodes can be used for finding key proteins for promoting biological evolution.
In the field of network communication, in the prior art, a K-shell algorithm is mostly adopted for automatically calculating and obtaining core nodes in a communication network, but due to the defects of the K-shell algorithm, the searched core nodes are probably not the most influential nodes in the communication network, and therefore the accuracy of the search result of the existing network core nodes is low.
Disclosure of Invention
The embodiment of the invention provides a method and a device for acquiring a network core node, which can improve the accuracy of the acquired network core node.
The method for acquiring a network core node provided by the embodiment of the invention specifically comprises the following steps:
responding to a core node acquisition instruction to acquire a target network; wherein, the target network comprises at least one network node;
calculating the node influence of each network node, and setting M network nodes with the maximum node influence as first network core nodes respectively; wherein M is more than or equal to 1;
calculating the node information redundancy of each first network core node, and setting the N first network core nodes with the minimum node information redundancy as second network core nodes respectively; wherein N is more than or equal to 1, and M is more than or equal to N.
Further, the target network is a communication network;
the obtaining a target network in response to the core node acquisition instruction specifically includes:
in response to the core node acquisition instruction, acquiring at least one communication record;
generating a communication record matrix according to all the communication records;
and setting the communication record matrix as the target network.
Further, the generating a communication record matrix according to all the communication records specifically includes:
calculating the frequency of occurrence of each of the communication records;
setting each communication record with the occurrence frequency larger than a preset threshold value as a high-frequency communication record respectively;
and generating the communication record matrix according to each high-frequency communication record.
Further, the calculating the occurrence frequency of each communication record specifically includes:
calculating the frequency of occurrence of each of the communication records using a TF-IDF algorithm.
Further, the total number of all the communication records is 1860.
Further, the calculating the node influence of each network node, and setting the M network nodes with the largest node influence as first network core nodes respectively includes:
calculating the node influence of each network node, and decomposing the target network according to the node influence of each network node to obtain a target network structure; wherein the target network structure comprises at least one sub-network structure; each of said sub-network structures comprising at least one of said network nodes; each sub-network structure in the target network structure is arranged from most to least according to the number of network nodes contained in each sub-network structure;
setting each network node arranged in a last sub-network structure in the target network structure as the first network core node, respectively; wherein the target network structure includes M network nodes arranged in the last sub-network structure.
Further, the calculating a node influence of each network node, and decomposing the target network according to the node influence of each network node to obtain a target network structure specifically includes:
and calculating the node influence of each network node by adopting a K-shell algorithm, and decomposing the target network according to the node influence of each network node to obtain the target network structure.
Further, the number of the sub-network structures in the target network structure is 13; the number of network nodes arranged in the last sub-network structure in the target network structure is 8.
Further, the node information redundancy is node information entropy redundancy.
Correspondingly, an embodiment of the present invention further provides an apparatus for acquiring a network core node, which specifically includes:
a target network obtaining module, configured to obtain a target network in response to a core node obtaining instruction; wherein, the target network comprises at least one network node;
a first network core node obtaining module, configured to calculate a node influence of each network node, and set, as a first network core node, each of the M network nodes having a largest node influence; wherein M is more than or equal to 1; and the number of the first and second groups,
a second network core node obtaining module, configured to calculate a node information redundancy of each first network core node, and set, as a second network core node, each of the N first network core nodes having the smallest node information redundancy; wherein N is more than or equal to 1, and M is more than or equal to N.
The embodiment of the invention has the following beneficial effects:
according to the method and the device for acquiring the network core nodes, provided by the embodiment of the invention, after the network core nodes in the target network are identified by using the node influence as the standard, the network core nodes are further screened by using the node information redundancy, namely, the core degree of each network node is judged by using the dual judgment standard, so that the accuracy of acquiring the network core nodes in the target network can be greatly improved.
Drawings
Fig. 1 is a schematic flow chart of a preferred embodiment of an acquisition method of a network core node provided in the present invention;
fig. 2 is a schematic diagram of a communication record matrix in the method for acquiring a network core node provided by the present invention;
fig. 3 is a schematic diagram of an acquisition result of a network core node in the method for acquiring a network core node according to the present invention;
fig. 4 is a comparison diagram of an information propagation result in the method for acquiring a network core node according to the present invention;
fig. 5 is a schematic structural diagram of a preferred embodiment of an acquisition apparatus of a network core node provided in the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 1, a schematic flowchart of a preferred embodiment of a method for acquiring a network core node provided by the present invention includes steps S11 to S13, which are specifically as follows:
s11: responding to a core node acquisition instruction to acquire a target network; wherein the target network comprises at least one network node.
Note that the present embodiment is executed by a data analysis device. The data analysis device may be any data analysis device in a server, a personal computer, a tablet computer, a smart phone, and the like, which is not limited herein.
In this embodiment, after receiving the core node acquisition instruction, the data analysis apparatus acquires a corresponding target network to be analyzed, and performs subsequent analysis on the target network, thereby acquiring a network core node in the target network.
It should be further noted that the core node acquisition instruction may be automatically generated by the data analysis apparatus at intervals, or may be triggered and generated by a user by clicking a specific button or executing a specific action.
S12: calculating the node influence of each network node, and setting M network nodes with the maximum node influence as first network core nodes respectively; wherein M is more than or equal to 1.
After obtaining the target network, the data analysis device calculates the node influence for each network node in the target network, so as to obtain the influence of each network node in the target network. And then, selecting M network nodes with the largest node influence from the network nodes as first network core nodes, thereby obtaining the network core nodes in the target network through preliminary calculation.
S13: calculating the node information redundancy of each first network core node, and setting the N first network core nodes with the minimum node information redundancy as second network core nodes respectively; wherein N is more than or equal to 1, and M is more than or equal to N.
The data analysis device calculates the node information redundancy of each first network core node by calculating the first network core node in the target network and then calculating the node information redundancy of the first network core nodes. Then, the N first network core nodes with the minimum node information redundancy are selected from the first network core nodes as second network core nodes, so that the network core nodes in the target network are finally obtained.
Further, information propagation in the target network can be well accomplished by using the obtained second network core node. For example, information dissemination of advertisements, messages, commodity recommendations, and the like can be performed using these second network core nodes.
More preferably, the node information redundancy is node information entropy redundancy.
It should be noted that, according to the definition of redundancy, the node information entropy redundancy h of a certain network node iiThe calculation formula of (2) is as follows:
hi＝1-Hi(1)
wherein HiIs the comprehensive information entropy of the network node i. Entropy is a measure of the uncertainty of the system. In information theory, information entropy, as defined by shannon, refers to the mathematical expectation of information. In general, the more ordered a system is, the lower its entropy, whereas the less ordered a system is, the higher its entropy. Therefore, the comprehensive information entropy H of the network node iiThe calculation formula of (2) is as follows:
wherein, Pi＝ps*pz，psFor the achievable rate of information propagation, pzIs the accuracy of information dissemination.
In particular, the achievable rate p of information propagationsThe calculation formula of (2) is as follows:
wherein liIs the time span for information to travel from network node i to its neighboring network nodes; a. thesIs the time span from the originating network node to the terminating network node for information to flow in the target network.
Accuracy p of information disseminationzThe calculation formula of (2) is as follows:
wherein k isiThe number of the network nodes directly connected with the network node i is; a. thezIs the total number of network nodes that need to be crossed when information flows in the target network from the originating network node to the terminating network node.
In this embodiment, after the network core nodes in the target network are identified by using the node influence as the standard, the network core nodes are further screened by using the node information redundancy, that is, the core degree of each network node is determined by using the dual determination standard, so that the accuracy of obtaining the network core nodes in the target network can be greatly improved.
In another preferred embodiment, the step S11 may further include sub-steps S1101 to S1103, specifically as follows:
s1101: at least one communication record is obtained in response to the core node obtaining instruction.
It should be noted that the target network is a communication network. The data analysis device obtains one or more communication records from a local carrier after receiving the core node obtaining instruction. The communication record is social data of the mobile terminal user, such as a call record of the mobile terminal user.
More preferably, the total number of all the communication records is 1860.
In this embodiment, the communication records obtained by the data analysis device from the local carrier may be randomly selected 1860 mobile terminal users' communication records of the latest month.
S1102: and generating a communication record matrix according to all the communication records.
It should be noted that, after obtaining one or more communication records, the data analysis device analyzes the communication records to obtain a corresponding communication record matrix.
Fig. 2 is a schematic diagram of a communication record matrix. When the value is 1, the communication record exists between the two corresponding users in the horizontal and vertical directions; a value of "0" indicates that there is no record of communication between the two users in the corresponding horizontal and vertical directions.
Further, the sub-step S1102 may further include steps S1102_1 to S1102_3, which are as follows:
s1102_ 1: calculating the frequency of occurrence of each of the communication records.
After the data analysis device obtains the communication records, the data analysis device may filter the communication records according to the occurrence frequency of each communication record, so as to filter out the low-frequency and useless communication records. For example, when the acquired communication records are call records of the mobile terminal user, low-frequency and useless call records such as express call records, intermediate call records, fraud call records and the like are filtered out by calculating the occurrence frequency of each call record.
Specifically, the above-described data analysis device first calculates the frequency of occurrence of each communication record after obtaining one or more communication records.
Still further, the step S1102_1 may further include a step S1102_101, which is as follows:
s1102_ 101: calculating the frequency of occurrence of each of the communication records using a TF-IDF algorithm.
It should be noted that the occurrence frequency of each communication record can be calculated by a TF-IDF (term frequency-inverse document frequency, a commonly used weighting technique for information retrieval and data mining) algorithm.
S1102_ 2: and setting each communication record with the occurrence frequency larger than a preset threshold value as a high-frequency communication record respectively.
The data analysis device calculates the occurrence frequency of each communication record, and then respectively judges whether the occurrence frequency of each communication record is greater than a preset threshold, if so, the communication record is considered as a high-frequency, normal and useful communication record, and therefore, the communication record is set as a high-frequency communication record; if not, the communication record is considered as a low-frequency, abnormal or useless communication record, and therefore the communication record is filtered.
S1102_ 3: and generating the communication record matrix according to each high-frequency communication record.
The data analysis device may filter out low-frequency, abnormal, or useless communication records among the communication records, and generate a corresponding communication record matrix from the remaining high-frequency communication records that are considered to be high-frequency, normal, and useful.
S1103: and setting the communication record matrix as the target network.
The data analysis device may obtain the communication record matrix and then set the communication record matrix as the target network.
In yet another preferred embodiment, the step S12 may further include the sub-steps S1201 to S1202 as follows:
s1201: calculating the node influence of each network node, and decomposing the target network according to the node influence of each network node to obtain a target network structure; wherein the target network structure comprises at least one sub-network structure; each of said sub-network structures comprising at least one of said network nodes; each sub-network structure in the target network structure is arranged from a plurality of sub-network structures according to the number of network nodes contained in each sub-network structure.
It should be noted that, after obtaining the target network, the data analysis apparatus calculates the node influence of each network node in the target network, and decomposes the target network according to the node influence of each network node obtained by calculation, so as to obtain a target network structure corresponding to the target network.
More preferably, the number of sub-network structures in the target network structure is 13; the number of network nodes arranged in the last sub-network structure in the target network structure is 8.
In this embodiment, the target network structure includes 13 sub-network structures, and the number of the network nodes included in the target network structure is from most to least, where the number of the network nodes included in the 13 th sub-network structure is 8.
Further, the sub-step S1201 may further include a step S1201_1, which is specifically as follows:
s1201_ 1: and calculating the node influence of each network node by adopting a K-shell algorithm, and decomposing the target network according to the node influence of each network node to obtain the target network structure.
It should be noted that the target network structure is obtained by calculating each network node in the target network by using a K-shell algorithm.
S1202: setting each network node arranged in a last sub-network structure in the target network structure as the first network core node, respectively; wherein the target network structure includes M network nodes arranged in the last sub-network structure.
After the target network structure is obtained, each network node arranged in the last sub-network structure in the target network structure is used as the first network core node. For example, each network node included in the above layer 13 sub-network structure is set as a first network core node.
Fig. 3 is a schematic diagram of an acquisition result of a network core node. Which contains 8 second network core nodes. These 8 second network core nodes are the network nodes whose entropy redundancy in the information in the 13 th sub-network structure in the target network structure is arranged in the first 8 small bits.
In a further preferred embodiment, after the computation of each second network core node in the target network, the propagation performance of the second network core nodes may be verified.
Specifically, 2, 4, 6, and 8 second network core nodes are randomly selected from the 8 second network core nodes shown in fig. 3, and after information propagation is performed by using the 2, 4, 6, and 8 second network core nodes, the number of network nodes that can be propagated in the target network is calculated and obtained. Wherein the 2, 4, 6 and 8 second network core nodes may be second network core nodes with information entropy redundancy arranged at the first 2 bits, the first 4 bits, the first 6 bits and the first 8 bits, respectively.
For comparison, the target network is calculated by independently using the existing K-shell algorithm, and 8 third network core nodes of the target network are obtained. Then, 2, 4, 6, and 8 third network core nodes are randomly selected from the 8 third network core nodes, and after information propagation is performed by using the 2, 4, 6, and 8 third network core nodes, the number of network nodes that can be propagated in the target network is calculated and obtained.
Through a comparative experiment, a comparative graph of the information dissemination results as shown in fig. 4 can be obtained. By comparison, it can be found that the information dissemination performance of the second network core node obtained by the calculation of the present embodiment is better than that of the third network core node obtained by the calculation of the prior art.
In the method for acquiring network core nodes provided in the embodiment of the present invention, after the network core nodes in the target network are identified by using the node influence as the standard, the network core nodes are further screened by using the node information redundancy, that is, the core degree of each network node is determined by using the dual determination standard, so that the accuracy of acquiring the network core nodes in the target network can be greatly improved.
Correspondingly, the invention also provides an acquisition device of the network core node, which can realize all the processes of the acquisition method of the network core node in the embodiment.
As shown in fig. 5, a schematic structural diagram of a preferred embodiment of an apparatus for acquiring a network core node provided in the present invention is specifically as follows:
a target network obtaining module 51, configured to respond to the core node obtaining instruction to obtain a target network; wherein, the target network comprises at least one network node;
a first network core node obtaining module 52, configured to calculate a node influence of each network node, and set, as a first network core node, each of the M network nodes having a largest node influence; wherein M is more than or equal to 1; and the number of the first and second groups,
a second network core node obtaining module 53, configured to calculate a node information redundancy of each first network core node, and set the N first network core nodes with the minimum node information redundancy as second network core nodes, respectively; wherein N is more than or equal to 1, and M is more than or equal to N.
Further, the target network is a communication network;
the target network obtaining module specifically includes:
a communication record acquisition unit, configured to acquire at least one communication record in response to the core node acquisition instruction;
the communication record matrix generating unit is used for generating a communication record matrix according to all the communication records; and the number of the first and second groups,
and the target network obtaining unit is used for setting the communication record matrix as the target network.
Further, the communication record matrix generating unit specifically includes:
a record occurrence frequency calculation subunit configured to calculate an occurrence frequency of each of the communication records;
the communication record filtering subunit is used for setting each communication record with the occurrence frequency larger than a preset threshold value as a high-frequency communication record; and the number of the first and second groups,
and the communication record matrix generating subunit is used for generating the communication record matrix according to each high-frequency communication record.
Further, the recording occurrence frequency calculating subunit specifically includes:
and the record occurrence frequency calculation subunit is used for calculating the occurrence frequency of each communication record by adopting a TF-IDF algorithm.
Further, the total number of all the communication records is 1860.
Further, the first network core node obtaining module specifically includes:
a target network structure obtaining unit, configured to calculate a node influence of each network node, and decompose the target network according to the node influence of each network node to obtain a target network structure; wherein the target network structure comprises at least one sub-network structure; each of said sub-network structures comprising at least one of said network nodes; each sub-network structure in the target network structure is arranged from most to least according to the number of network nodes contained in each sub-network structure; and the number of the first and second groups,
a first network core node setting unit, configured to set each network node arranged in a last sub-network structure in the target network structure as the first network core node, respectively; wherein the target network structure includes M network nodes arranged in the last sub-network structure.
Further, the target network structure obtaining unit specifically includes:
and the target network structure obtaining subunit is used for calculating the node influence of each network node by adopting a K-shell algorithm, and decomposing the target network according to the node influence of each network node to obtain the target network structure.
Further, the number of the sub-network structures in the target network structure is 13; the number of network nodes arranged in the last sub-network structure in the target network structure is 8.
Further, the node information redundancy is node information entropy redundancy.
The device for acquiring network core nodes provided by the embodiment of the invention further screens the network core nodes by using the node information redundancy after identifying the network core nodes in the target network by using the node influence as the standard, namely, the device judges the core degree of each network node by using the dual judgment standard, thereby greatly improving the accuracy of acquiring the network core nodes in the target network.
While the foregoing is directed to the preferred embodiment of the present invention, it will be understood by those skilled in the art that various changes and modifications may be made without departing from the spirit and scope of the invention.
Claims (9)
1. A method for acquiring a network core node is characterized by comprising the following steps:
responding to a core node acquisition instruction to acquire a target network; wherein, the target network comprises at least one network node; the target network is a communication network;
calculating the node influence of each network node, and setting M network nodes with the maximum node influence as first network core nodes respectively; wherein M is more than or equal to 1;
calculating the node information entropy redundancy of each first network core node, and setting the N first network core nodes with the minimum node information entropy redundancy as second network core nodes respectively; wherein N is more than or equal to 1, and M is more than or equal to N;
wherein, the node information entropy of a certain network node i is redundantDegree hiThe calculation formula of (2) is as follows:
hi＝1-Hi；
wherein HiIs the comprehensive information entropy of the network node i; comprehensive information entropy H of network node iiThe calculation formula of (2) is as follows:
wherein, Pi＝ps*pz，psFor the achievable rate of information propagation, pzAccuracy of information dissemination;
reachability of information dissemination psThe calculation formula of (2) is as follows:
wherein liIs the time span for information to travel from network node i to its neighboring network nodes; a. thesA time span from the start network node to the end network node for information flowing in the target network;
accuracy p of information disseminationzThe calculation formula of (2) is as follows:
wherein k isiThe number of the network nodes directly connected with the network node i is; a. thezIs the total number of network nodes that need to be crossed when information flows in the target network from the originating network node to the terminating network node.
2. The method for acquiring a core node of a network according to claim 1, wherein the acquiring a target network in response to the core node acquisition instruction specifically includes:
in response to the core node acquisition instruction, acquiring at least one communication record;
generating a communication record matrix according to all the communication records;
and setting the communication record matrix as the target network.
3. The method for acquiring a network core node according to claim 2, wherein the generating a communication record matrix according to all the communication records specifically includes:
calculating the frequency of occurrence of each of the communication records;
setting each communication record with the occurrence frequency larger than a preset threshold value as a high-frequency communication record respectively;
and generating the communication record matrix according to each high-frequency communication record.
4. The method according to claim 3, wherein the calculating the frequency of occurrence of each communication record specifically includes:
calculating the frequency of occurrence of each of the communication records using a TF-IDF algorithm.
5. The method according to any of claims 2 to 4, wherein the total number of all said communication records is 1860.
6. The method according to claim 1, wherein the calculating the node influence of each network node and setting the M network nodes with the largest node influence as first network core nodes respectively comprises:
calculating the node influence of each network node, and decomposing the target network according to the node influence of each network node to obtain a target network structure; wherein the target network structure comprises at least one sub-network structure; each of said sub-network structures comprising at least one of said network nodes; each sub-network structure in the target network structure is arranged from most to least according to the number of network nodes contained in each sub-network structure;
setting each network node arranged in a last sub-network structure in the target network structure as the first network core node, respectively; wherein the target network structure includes M network nodes arranged in the last sub-network structure.
7. The method according to claim 6, wherein the calculating the node influence of each network node and decomposing the target network according to the node influence of each network node to obtain the target network structure specifically includes:
and calculating the node influence of each network node by adopting a K-shell algorithm, and decomposing the target network according to the node influence of each network node to obtain the target network structure.
8. The method according to claim 6 or 7, wherein the number of sub-network structures in the target network structure is 13; the number of network nodes arranged in the last sub-network structure in the target network structure is 8.
9. An apparatus for acquiring a core node of a network, comprising:
a target network obtaining module, configured to obtain a target network in response to a core node obtaining instruction; wherein, the target network comprises at least one network node; the target network is a communication network;
a first network core node obtaining module, configured to calculate a node influence of each network node, and set, as a first network core node, each of the M network nodes having a largest node influence; wherein M is more than or equal to 1; and the number of the first and second groups,
a second network core node obtaining module, configured to calculate a node information entropy redundancy of each first network core node, and set, as second network core nodes, the N first network core nodes with the minimum node information entropy redundancy, respectively; wherein N is more than or equal to 1, and M is more than or equal to N;
wherein, the node information entropy redundancy h of a certain network node iiThe calculation formula of (2) is as follows:
hi＝1-Hi；
wherein HiIs the comprehensive information entropy of the network node i; comprehensive information entropy H of network node iiThe calculation formula of (2) is as follows:
wherein, Pi＝ps*pz，psFor the achievable rate of information propagation, pzAccuracy of information dissemination;
reachability of information dissemination psThe calculation formula of (2) is as follows:
wherein liIs the time span for information to travel from network node i to its neighboring network nodes; a. thesA time span from the start network node to the end network node for information flowing in the target network;
accuracy p of information disseminationzThe calculation formula of (2) is as follows:
wherein k isiThe number of the network nodes directly connected with the network node i is; a. thezIs the total number of network nodes that need to be crossed when information flows in the target network from the originating network node to the terminating network node.
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