CN114553717A - Network node dividing method, device, equipment and storage medium - Google Patents
Network node dividing method, device, equipment and storage medium Download PDFInfo
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Abstract
The embodiment of the invention discloses a network node dividing method, a device, equipment and a storage medium. Generating a correlation matrix matched with a network node set to be partitioned according to attribute data of each network node in the network node set to be partitioned; respectively encoding each correlation element in the correlation matrix, and determining the size relationship among the correlation elements so as to establish a node similarity network matched with a network node set to be partitioned; acquiring neighbor node sets and node importance of each network node in the node similarity network, and distributing identifiers for each network node in the node similarity network; and according to the identification and the correlation between each network node and the neighbor node, performing identification and re-marking on each network node to realize network node division. The technical scheme of the embodiment of the invention realizes the effect of effectively dividing the nodes with higher similarity in the network into the same community.
Description
Technical Field
Embodiments of the present invention relate to computer technologies, and in particular, to a method, an apparatus, a device, and a storage medium for dividing network nodes.
Background
The complex network is one of the important expression forms of the complex system in the real society, and the community division mode has important research significance. At present, network nodes of a complex network are subjected to community division, and when all neighbor nodes of the network nodes have a plurality of same maximum labels, one of the labels with the most frequent occurrence is randomly selected as a label of the network node.
In the process of implementing the invention, the inventor finds that the prior art has the following technical defects: randomly selecting a label with the most frequent occurrence from all the neighbor nodes of the network node as the label of the network node, so that two or more disconnected node groups may have the same label finally, and an avalanche effect can be brought.
Disclosure of Invention
Embodiments of the present invention provide a network node partitioning method, apparatus, device, and storage medium, so as to achieve an effect of effectively partitioning nodes with higher similarity in a network into a same community.
In a first aspect, an embodiment of the present invention provides a network node partitioning method, where the method includes:
generating a correlation matrix matched with the network node set to be partitioned according to the attribute data of each network node in the network node set to be partitioned;
respectively encoding each correlation element in the correlation matrix, and determining the size relationship among the correlation elements so as to establish a node similarity network matched with a network node set to be partitioned;
acquiring neighbor node sets and node importance of each network node in the node similarity network, and distributing identifiers for each network node in the node similarity network;
and according to the identification and the correlation between each network node and the neighbor node, performing identification and re-marking on each network node to realize network node division.
In a second aspect, an embodiment of the present invention further provides a network node partitioning apparatus, where the apparatus includes:
the correlation matrix generation module is used for generating a correlation matrix matched with the network node set to be partitioned according to the attribute data of each network node in the network node set to be partitioned;
the node similarity network establishing module is used for respectively encoding each correlation element in the correlation matrix and determining the size relationship among the correlation elements so as to establish a node similarity network matched with a network node set to be divided;
the identification distribution module is used for acquiring neighbor node sets and node importance of each network node in the node similarity network and distributing identification to each network node in the node similarity network;
and the identification re-marking module is used for identifying and re-marking each network node according to the identification and the correlation between each network node and the neighbor node so as to realize network node division.
In a third aspect, an embodiment of the present invention further provides an electronic device, where the electronic device includes:
one or more processors;
storage means for storing one or more programs;
when executed by the one or more processors, cause the one or more processors to implement a network node partitioning method as in any embodiment of the invention.
In a fourth aspect, the present invention further provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements a network node partitioning method according to any embodiment of the present invention.
According to the embodiment of the invention, a correlation matrix matched with a network node set to be partitioned is generated according to attribute data of each network node in the network node set to be partitioned; respectively encoding each correlation element in the correlation matrix, and determining the size relationship among the correlation elements so as to establish a node similarity network matched with a network node set to be partitioned; acquiring neighbor node sets and node importance of each network node in the node similarity network, and distributing identifiers for each network node in the node similarity network; according to the identification and the correlation between each network node and the neighbor node, each network node is identified and re-marked to realize network node division, so that the problems that when the neighbor node of one network node in a complex network in the prior art has a plurality of same maximum labels, one of the labels with the most frequent occurrence is randomly selected as the label of the neighbor node, two or more disconnected node groups possibly have the same label finally, and an avalanche effect is brought are solved, and the effect of effectively dividing the nodes with higher similarity in the network into the same community is realized.
Drawings
Fig. 1 is a flowchart of a network node partitioning method according to an embodiment of the present invention;
fig. 1a is a schematic diagram of a specific application scenario of a network node partitioning method according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of a network node partitioning apparatus according to a second embodiment of the present invention;
fig. 3 is a schematic structural diagram of an electronic device according to a third embodiment of the present invention.
Detailed Description
The embodiments of the present invention will be described in further detail with reference to the drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of and not restrictive on the broad invention. It should be further noted that, for convenience of description, only some structures, not all structures, relating to the embodiments of the present invention are shown in the drawings.
Example one
Fig. 1 is a flowchart of a network node partitioning method according to an embodiment of the present invention, where the embodiment is applicable to a case where a large amount of data is partitioned into communities, and the method may be implemented by a network node partitioning apparatus, where the apparatus may be implemented by software and/or hardware and is integrated in a device that executes the method, and in the embodiment, the device that executes the method may be a server. Referring to fig. 1, the method specifically includes the following steps:
s110, generating a correlation matrix matched with the network node set to be partitioned according to the attribute data of each network node in the network node set to be partitioned.
The set of network nodes to be partitioned may be a set composed of a plurality of devices in a network. Wherein, a network node may refer to a communication device in a network.
Attribute data may refer to data generated or collected by a network node while operating. For example, the CPU (central processing unit), the memory, the disk and key objects in the network may be concerned, such as attribute data of disk utilization, page file utilization, number of processes, average load rate and memory utilization; the sensor nodes may be interested in data monitored during sensor operation, such as attribute data of temperature, humidity, wind speed, and the like.
The correlation matrix may be a matrix constructed from the correlations of a plurality of network nodes with each other.
In this embodiment, attribute data corresponding to each network node may be obtained, and the attribute data is used to calculate the correlation between the network nodes, and generate a correlation matrix matching the set of network nodes to be partitioned.
In an optional implementation manner of this embodiment, generating a correlation matrix matched with the network node set to be partitioned according to attribute data of each network node in the network node set to be partitioned may include:
acquiring attribute data of each network node in a network node set to be divided; calculating the correlation among all target nodes in the network node set to be divided according to the attribute data; and forming a correlation matrix matched with the network node set to be partitioned according to the correlation among the target nodes.
The target node may refer to a currently processed node selected from the network node set to be partitioned when calculating the correlation between the network nodes. The correlation may represent a degree of similarity between two network nodes, and typically may be calculated using a maximum information coefficient method.
Specifically, the corresponding node attribute data may be locally obtained from the network node, and the attribute data may be preprocessed, for example, obviously abnormal data or missing data may be deleted. Therefore, the correlation between every two network nodes can be sequentially calculated by a maximum information coefficient method according to the preprocessed network node attribute data. And then a matrix can be generated according to the correlation between every two network nodes and is used as a correlation matrix matched with the network node set to be divided.
And S120, respectively encoding each correlation element in the correlation matrix, and determining the size relationship among the correlation elements so as to establish a node similarity network matched with the network node set to be partitioned.
Wherein, the correlation element may refer to a correlation value in the correlation matrix. The node similarity network may be a network established with each network node as a vertex and a correlation element as an edge.
Optionally, each correlation element in the correlation matrix may be encoded, and the size relationship of each correlation element is determined according to the encoding result, so that a node similarity network is established by using the network nodes as vertices and the correlation elements as edges according to the correlation sizes between the network nodes and the network nodes.
In an optional implementation manner of this embodiment, the encoding processing is performed on each correlation element in the correlation matrix, and a size relationship between the correlation elements is determined, so as to establish a node similarity network matched with a network node set to be partitioned, where the method includes:
generating a 0 code set and a 1 code set corresponding to each correlation element in the correlation matrix; determining the size relation between the correlation elements according to the 0 coding set and the 1 coding set of each correlation element; and establishing a node similarity network matched with the network node set to be divided according to the size relationship among the correlation elements.
Specifically, a corresponding 0 coding set and a corresponding 1 coding set may be generated for each correlation element in the correlation matrix, and the size relationship between the correlation elements may be determined through the respective 0 coding set and 1 coding set of the two correlation elements. And thus, establishing a similarity network of the connected edges according to the determined size relationship.
Illustratively, assuming there are two values x and y, x > y when the intersection of the 1-coded set of x and the 0-coded set of y is not empty; when the 1 coding set of x and the 1 coding set of y are equal and the 0 coding set of x and the 0 coding set of y are also equal, x is equal to y; otherwise, x < y.
The advantage of this arrangement is that privacy protection can be achieved by using a 0-1 coding method when determining the correlation size between the network nodes.
S130, acquiring neighbor node sets and node importance of each network node in the node similarity network, and distributing identifiers for each network node in the node similarity network.
The neighbor node set may be a set of all nodes connected with the currently processed target node with edges in the node similarity network. The node importance may refer to the importance of the node in the node similarity network. Identification may refer to the identity of a node, e.g., node 1, node 2, … …, and node n.
Optionally, a neighbor node set corresponding to each network node may be determined from the node similarity network, the importance of each network node in the node similarity network may be measured, and a corresponding identifier is allocated to each network node according to the importance.
In an optional implementation manner of this embodiment, the obtaining a neighbor node set and a node importance of each network node in the node similarity network, and allocating an identifier to each network node in the node similarity network may include:
acquiring a neighbor node set of each network node in the node similarity network; measuring the importance of the nodes according to the number of the neighbor nodes of each network node and the correlation between the network nodes and the neighbor nodes; and arranging the network nodes in a descending order according to the importance of the nodes, and distributing identifiers for the network nodes in the node similarity network according to the arrangement order.
The number of the neighbor nodes can be obtained by statistics according to a neighbor node set of each network node, the number of the neighbor nodes can be used for measuring the importance of the node, illustratively, the number of the neighbor nodes of the node 1 is 5, the number of the neighbor nodes of the node 3 is 1, and it can be determined that the importance of the node 1 is higher than that of the node 3. The ranking order may refer to an order in which network nodes in the node similarity network are ranked from large to small according to the importance of the nodes.
Specifically, neighbor node sets respectively corresponding to each network node may be determined from the node similarity network, each neighbor node set may be counted to obtain the number of neighbor nodes of each network node, and the node importance of each network node may be measured in combination with the correlation between the network node and the corresponding neighbor node. Therefore, the network nodes are arranged in a descending order according to the importance of the nodes, and identifiers are sequentially allocated to the network nodes in the node similarity network according to the arrangement order, namely, the node 1, the node 2, the node … … and the node n are sequentially allocated.
The node importance of each network node is measured by combining the correlation between the network node and the corresponding neighbor node, and it can be understood that if the number of the neighbor nodes of two nodes is the same, the node importance can be measured according to the correlation between the two nodes and the corresponding neighbor nodes, and the node importance corresponding to the larger correlation is higher.
S140, according to the identification and the correlation between each network node and the adjacent nodes, the identification is carried out on each network node, and network node division is realized.
Optionally, the network nodes may be marked again according to the identifier corresponding to each network node and the correlation between each network node and the neighboring node, so as to implement network node division.
In an optional implementation manner of this embodiment, the performing identification and re-labeling on each network node according to the identifier and the correlation between each network node and a neighboring node to implement network node partitioning may include:
acquiring a current processing network node from the node similarity network, and acquiring a neighbor node identifier with the maximum correlation with the current processing network node according to the correlation between the current processing network node and each neighbor node; according to the neighbor node identification, carrying out identification re-marking on the current processing network node; sequentially traversing each network node and the matched neighbor node set in the node similarity network, and completing identification re-marking on all network nodes; and returning to execute the operation of acquiring the currently processed network node from the node similarity network until the identifier of each network node is not changed any more, and finishing the division processing of the network nodes.
The network nodes can be marked again by the algorithm so as to realize the division of the network nodes. Typically, such as LPA (Label Propagation) algorithm. Specifically, a network node may be sequentially obtained from the node similarity network according to the node identifier as a currently processed network node, a neighbor node set of the currently processed network node is queried, a neighbor node having the highest correlation with the currently processed network node item is obtained from the neighbor node set, and the identifier of the neighbor node is marked to the currently processed network node, so as to implement identifier re-marking of the currently processed network node. And traversing each network node in the node similarity network, and after the identifier re-marking is completed, returning to execute the operation of acquiring the currently processed network node from the node similarity network, namely, performing iteration for multiple times until the identifier of each network node is not changed any more, and completing the division processing of the network nodes.
The method has the advantages that the nodes with higher importance can be identified and updated as soon as possible, the convergence speed of the algorithm is increased, and the time complexity of the algorithm is reduced.
According to the technical scheme of the embodiment of the invention, a correlation matrix matched with a network node set to be partitioned is generated according to the attribute data of each network node in the network node set to be partitioned; respectively encoding each correlation element in the correlation matrix, and determining the size relationship among the correlation elements so as to establish a node similarity network matched with a network node set to be partitioned; acquiring neighbor node sets and node importance of each network node in the node similarity network, and distributing identifiers for each network node in the node similarity network; according to the identification and the correlation between each network node and the neighbor node, each network node is identified and re-marked to realize network node division, so that the problems that when the neighbor node of one network node in a complex network in the prior art has a plurality of same maximum labels, one of the labels with the most frequent occurrence is randomly selected as the label of the neighbor node, two or more disconnected node groups possibly have the same label finally, and an avalanche effect is brought are solved, and the effect of effectively dividing the nodes with higher similarity in the network into the same community is realized.
Fig. 1a is a schematic diagram of a specific application scenario of a network node partitioning method according to an embodiment of the present invention. The method comprises the steps of obtaining attribute data of network nodes, preprocessing the attribute data, calculating similarity (namely correlation) between the network nodes through a maximum information coefficient method according to the preprocessed attribute data of the network nodes, comparing the similarity based on numerical value comparison of privacy protection, constructing an adjacency matrix of a graph, establishing a complex network (namely a node similarity network), and detecting the constructed complex network through an improved LPA algorithm to enable the network nodes with higher correlation to be located in the same community. The similarity of the network nodes in the same community is high, and the repulsion of the network nodes in different communities is high, namely the similarity is low.
Example two
Fig. 2 is a schematic structural diagram of a network node partitioning apparatus according to a second embodiment of the present invention, which is capable of executing the network node partitioning method in the foregoing embodiments. Referring to fig. 2, the apparatus includes: a correlation matrix generation module 210, a node similarity network establishment module 220, an identity assignment module 230, and an identity re-labeling module 240. Wherein:
a correlation matrix generation module 210, configured to generate a correlation matrix matched with the network node set to be partitioned according to attribute data of each network node in the network node set to be partitioned;
a node similarity network establishing module 220, configured to encode each correlation element in the correlation matrix, and determine a size relationship between the correlation elements, so as to establish a node similarity network matched with a network node set to be partitioned;
an identifier allocating module 230, configured to acquire neighbor node sets and node importance of each network node in the node similarity network, and allocate an identifier to each network node in the node similarity network;
and the identifier re-marking module 240 is configured to perform identifier re-marking on each network node according to the identifier and the correlation between each network node and the neighboring node, so as to implement network node division.
According to the technical scheme of the embodiment of the invention, a correlation matrix matched with a network node set to be partitioned is generated according to the attribute data of each network node in the network node set to be partitioned; respectively encoding each correlation element in the correlation matrix, and determining the size relationship among the correlation elements so as to establish a node similarity network matched with a network node set to be partitioned; acquiring neighbor node sets and node importance of each network node in the node similarity network, and distributing identifiers for each network node in the node similarity network; according to the identification and the correlation between each network node and the neighbor node, each network node is identified and re-marked to realize network node division, so that the problems that when the neighbor node of one network node in a complex network in the prior art has a plurality of same maximum labels, one of the labels with the most frequent occurrence is randomly selected as the label of the neighbor node, two or more disconnected node groups possibly have the same label finally, and an avalanche effect is brought are solved, and the effect of effectively dividing the nodes with higher similarity in the network into the same community is realized.
In the above apparatus, optionally, the correlation matrix generating module 210 may be specifically configured to:
acquiring attribute data of each network node in a network node set to be divided;
calculating the correlation among all target nodes in the network node set to be divided according to the attribute data;
and forming a correlation matrix matched with the network node set to be partitioned according to the correlation among the target nodes.
In the foregoing apparatus, optionally, the node similarity network establishing module 220 may be specifically configured to:
generating a 0 code set and a 1 code set corresponding to each correlation element in the correlation matrix;
determining the size relation between the correlation elements according to the 0 coding set and the 1 coding set of each correlation element;
and establishing a node similarity network matched with the network node set to be divided according to the size relationship among the correlation elements.
In the foregoing apparatus, optionally, the identifier assigning module 230 may be specifically configured to:
acquiring a neighbor node set of each network node in the node similarity network;
measuring the importance of the nodes according to the number of the neighbor nodes of each network node and the correlation between the network nodes and the neighbor nodes;
and arranging the network nodes in a descending order according to the importance of the nodes, and distributing identifiers for the network nodes in the node similarity network according to the arrangement order.
In the foregoing apparatus, optionally, the identifier re-labeling module 240 may be specifically configured to:
acquiring a current processing network node from the node similarity network, and acquiring a neighbor node identifier with the maximum correlation with the current processing network node according to the correlation between the current processing network node and each neighbor node;
according to the neighbor node identification, carrying out identification re-marking on the current processing network node;
sequentially traversing each network node and the matched neighbor node set in the node similarity network, and completing identification re-marking on all network nodes;
and returning to execute the operation of acquiring the currently processed network node from the node similarity network until the identifier of each network node is not changed any more, and finishing the division processing of the network nodes.
The network node dividing device provided by the embodiment of the invention can execute the network node dividing method provided by any embodiment of the invention, and has corresponding functional modules and beneficial effects of the execution method.
EXAMPLE III
Fig. 3 is a schematic structural diagram of an electronic device according to a third embodiment of the present invention, as shown in fig. 3, the electronic device includes a processor 310, a storage device 320, an input device 330, and an output device 340; the number of the processors 310 in the device may be one or more, and one processor 310 is taken as an example in fig. 3; the processor 310, the storage 320, the input 330 and the output 340 of the apparatus may be connected by a bus or other means, as exemplified by the bus connection in fig. 3.
The storage device 320 is a computer-readable storage medium, and can be used to store software programs, computer-executable programs, and modules, such as program instructions/modules corresponding to the network node dividing method in the embodiment of the present invention (for example, the correlation matrix generating module 210, the node similarity network establishing module 220, the identifier assigning module 230, and the identifier re-labeling module 240 in the network node dividing device). The processor 310 executes various functional applications and data processing of the device by executing software programs, instructions and modules stored in the storage device 320, so as to implement the above-mentioned network node division method, which includes:
generating a correlation matrix matched with the network node set to be partitioned according to the attribute data of each network node in the network node set to be partitioned;
respectively encoding each correlation element in the correlation matrix, and determining the size relationship among the correlation elements so as to establish a node similarity network matched with a network node set to be partitioned;
acquiring neighbor node sets and node importance of each network node in the node similarity network, and distributing identifiers for each network node in the node similarity network;
and according to the identification and the correlation between each network node and the neighbor node, performing identification and re-marking on each network node to realize network node division.
The storage device 320 may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created according to the use of the terminal, and the like. Further, the storage 320 may include high speed random access memory and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other non-volatile solid state storage device. In some examples, the storage 320 may further include memory located remotely from the processor 310, which may be connected to the device over a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The input device 330 may be used to receive input numeric or character information and generate key signal inputs related to user settings and function controls of the apparatus. The output device 340 may include a display device such as a display screen.
Example four
A fourth embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, where the computer program is used to execute a network node partitioning method when executed by a processor, and the method includes:
generating a correlation matrix matched with the network node set to be partitioned according to the attribute data of each network node in the network node set to be partitioned;
respectively encoding each correlation element in the correlation matrix, and determining the size relationship among the correlation elements so as to establish a node similarity network matched with a network node set to be partitioned;
acquiring neighbor node sets and node importance of each network node in the node similarity network, and distributing identifiers for each network node in the node similarity network;
and according to the identification and the correlation between each network node and the neighbor node, performing identification and re-marking on each network node to realize network node division.
Of course, the computer-readable storage medium provided by the embodiment of the present invention stores thereon a computer program, where the computer program is not limited to the method operations described above, and may also perform related operations in the network node partitioning method provided by any embodiment of the present invention.
From the above description of the embodiments, it is obvious for those skilled in the art that the present invention can be implemented by software and necessary general hardware, and certainly, can also be implemented by hardware, but the former is a better embodiment in many cases. Based on such understanding, the technical solutions of the present invention may be embodied in the form of a software product, which can be stored in a computer-readable storage medium, such as a floppy disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a FLASH Memory (FLASH), a hard disk or an optical disk of a computer, and includes several instructions for enabling a computer device (which may be a personal computer, a server, or a network device) to execute the methods according to the embodiments of the present invention.
It should be noted that, in the embodiment of the foregoing network node dividing apparatus, each included unit and each included module are only divided according to functional logic, but are not limited to the foregoing division, as long as the corresponding functions can be implemented; in addition, specific names of the functional units are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present invention.
It is to be noted that the foregoing is only illustrative of the preferred embodiments of the present invention and the technical principles employed. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, although the present invention has been described in greater detail by the above embodiments, the present invention is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present invention, and the scope of the present invention is determined by the scope of the appended claims.
Claims (10)
1. A method for network node partitioning, comprising:
generating a correlation matrix matched with the network node set to be partitioned according to the attribute data of each network node in the network node set to be partitioned;
respectively encoding each correlation element in the correlation matrix, and determining the size relationship among the correlation elements so as to establish a node similarity network matched with a network node set to be partitioned;
acquiring neighbor node sets and node importance of each network node in the node similarity network, and distributing identifiers for each network node in the node similarity network;
and according to the identification and the correlation between each network node and the neighbor node, performing identification and re-marking on each network node to realize network node division.
2. The method of claim 1, wherein generating a correlation matrix matching the set of network nodes to be partitioned according to the attribute data of each network node in the set of network nodes to be partitioned comprises:
acquiring attribute data of each network node in a network node set to be divided;
calculating the correlation among all target nodes in the network node set to be divided according to the attribute data;
and forming a correlation matrix matched with the network node set to be partitioned according to the correlation among the target nodes.
3. The method according to claim 1, wherein the encoding processing is performed on each correlation element in the correlation matrix, and a size relationship between the correlation elements is determined to establish a node similarity network matching with a network node set to be partitioned, including:
generating a 0 code set and a 1 code set corresponding to each correlation element in the correlation matrix;
determining the size relation between the correlation elements according to the 0 coding set and the 1 coding set of each correlation element;
and establishing a node similarity network matched with the network node set to be partitioned according to the size relationship among the correlation elements.
4. The method of claim 1, wherein obtaining neighbor node sets and node importance of each network node in the node similarity network and assigning an identifier to each network node in the node similarity network comprises:
acquiring a neighbor node set of each network node in the node similarity network;
measuring the importance of the nodes according to the number of the neighbor nodes of each network node and the correlation between the network nodes and the neighbor nodes;
and arranging the network nodes in a descending order according to the importance of the nodes, and distributing identifiers for the network nodes in the node similarity network according to the arrangement order.
5. The method of claim 1, wherein the identifying and re-labeling each network node according to the identifier and the correlation between each network node and the neighboring nodes to implement network node partitioning comprises:
acquiring a current processing network node from the node similarity network, and acquiring a neighbor node identifier with the maximum correlation with the current processing network node according to the correlation between the current processing network node and each neighbor node;
according to the neighbor node identification, carrying out identification re-marking on the current processing network node;
sequentially traversing each network node and the matched neighbor node set in the node similarity network, and completing identification re-marking on all network nodes;
and returning to execute the operation of acquiring the currently processed network node from the node similarity network until the identifier of each network node is not changed any more, and finishing the division processing of the network nodes.
6. A network node partitioning apparatus, comprising:
the correlation matrix generation module is used for generating a correlation matrix matched with the network node set to be partitioned according to the attribute data of each network node in the network node set to be partitioned;
the node similarity network establishing module is used for respectively encoding each correlation element in the correlation matrix and determining the size relationship among the correlation elements so as to establish a node similarity network matched with a network node set to be divided;
the identification distribution module is used for acquiring neighbor node sets and node importance of each network node in the node similarity network and distributing identification to each network node in the node similarity network;
and the identification re-marking module is used for identifying and re-marking each network node according to the identification and the correlation between each network node and the neighbor node, so as to realize network node division.
7. The apparatus of claim 6, wherein the correlation matrix generation module is specifically configured to:
acquiring attribute data of each network node in a network node set to be divided;
calculating the correlation among all target nodes in the network node set to be divided according to the attribute data;
and forming a correlation matrix matched with the network node set to be partitioned according to the correlation among the target nodes.
8. The apparatus of claim 6, wherein the node similarity network establishing module is specifically configured to:
generating a 0 code set and a 1 code set corresponding to each correlation element in the correlation matrix;
determining the size relation between the correlation elements according to the 0 coding set and the 1 coding set of each correlation element;
and establishing a node similarity network matched with the network node set to be divided according to the size relationship among the correlation elements.
9. An electronic device, characterized in that the electronic device comprises:
one or more processors;
storage means for storing one or more programs;
when executed by the one or more processors, cause the one or more processors to implement a network node partitioning method as recited in any one of claims 1-5.
10. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out a network node partitioning method according to any one of claims 1 to 5.
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