CN114866437A - Node detection method, device, equipment and medium - Google Patents
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Abstract
The invention discloses a node detection method, a node detection device, a node detection equipment and a node detection medium. The method comprises the following steps: acquiring network association information of each node in a network to be detected; the network association information of each node comprises the weight of the associated edge of each node; calculating the basic entropy value of each node according to the weight of the associated edge of each node and a preset node entropy value calculation formula; sequencing the basic entropy values of all nodes according to the numerical value from large to small; carrying out normalization processing on the basic entropy value of each node according to the sequencing result to obtain a target entropy value of each node; and determining the most critical node and the negligible node in the network to be detected according to the target entropy of each node, and providing the target entropy, the most critical node and the negligible node of each node for a user. The method and the device can uniformly measure the importance of the nodes in different networks through the entropy values under the same data dimension, and intuitively embody the importance degree of each node in the network.
Description
Technical Field
The present invention relates to the field of computer technologies, and in particular, to a node detection method, apparatus, device, and medium.
Background
A network is a topology consisting of edges and nodes. Many systems can be abstracted as a network, with elements in the system abstracted as nodes in the network and connections between elements abstracted as edges in the network. Typically, different nodes have different effects on the structure and function of the network. Nodes that have a large impact on the structure and function of the network are referred to as critical nodes in the network. In order to better monitor and evaluate the network, it is necessary to determine the key nodes in the network and obtain the relevant information generated by the key nodes in the network in time.
In the related art, an evaluation index of a node and a fixed index threshold corresponding to the evaluation index are usually set, and a node whose evaluation index is greater than the index threshold is determined as a key node in a network. For different networks, the number of nodes in the network is different, and the evaluation index values of the nodes in the network may have a large difference. The detection of each node in the network is only carried out through the index threshold value which is fixedly set, the detection requirements of the nodes of different networks are difficult to meet in a unified mode, and the importance degree of the nodes cannot be visually reflected.
Disclosure of Invention
The invention provides a node detection method, a node detection device, a node detection equipment and a node detection medium, which are used for solving the problems that in the related technology, each node in a network is detected only through a fixedly set index threshold, the node detection requirements of different networks are difficult to meet in a unified mode, and the importance degree of the node cannot be intuitively reflected.
According to an aspect of the present invention, there is provided a node detection method, including:
acquiring network association information of each node in a network to be detected; wherein, the network association information of each node comprises the weight of the association edge of each node;
calculating the basic entropy value of each node according to the weight of the associated edge of each node and a preset node entropy value calculation formula;
sequencing the basic entropy values of the nodes according to the numerical values from large to small;
carrying out normalization processing on the basic entropy value of each node according to the sequencing result to obtain a target entropy value of each node;
and determining the most critical node and the negligible node in the network to be detected according to the target entropy of each node, and providing the target entropy of each node, the most critical node and the negligible node for a user.
According to another aspect of the present invention, there is provided a node detection apparatus, including:
the information acquisition module is used for acquiring network association information of each node in the network to be detected; wherein, the network association information of each node comprises the weight of the association edge of each node;
the entropy calculation module is used for calculating the basic entropy of each node according to the weight of the associated edge of each node and a preset node entropy calculation formula;
the entropy value sequencing module is used for sequencing the basic entropy values of the nodes according to the numerical value from large to small;
the entropy normalization module is used for carrying out normalization processing on the basic entropy of each node according to the sequencing result to obtain a target entropy of each node;
and the result providing module is used for determining the most critical node and the negligible node in the network to be detected according to the target entropy of each node and providing the target entropy of each node, the most critical node and the negligible node for a user.
According to another aspect of the present invention, there is provided an electronic apparatus including:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores a computer program executable by the at least one processor, the computer program being executable by the at least one processor to enable the at least one processor to perform the node detection method according to any of the embodiments of the present invention.
According to another aspect of the present invention, there is provided a computer-readable storage medium storing computer instructions for causing a processor to implement the node detection method according to any one of the embodiments of the present invention when the computer instructions are executed.
According to the technical scheme of the embodiment of the invention, the network association information of each node in the network to be detected is obtained, and the network association information of each node comprises the weight of the association edge of each node; then, calculating the basic entropy value of each node according to the weight of the associated edge of each node and a preset node entropy value calculation formula; sequencing the basic entropy values of all nodes according to the numerical value from large to small; carrying out normalization processing on the basic entropy value of each node according to the sequencing result to obtain a target entropy value of each node; finally, according to the target entropy of each node, determining the most critical node and the negligible node in the network to be detected, providing the target entropy, the most critical node and the negligible node of each node to a user, solving the problems that in the related technology, the detection of each node in the network is performed only through a fixedly set index threshold, the node detection requirements of different networks are difficult to be met uniformly, and the importance degree of the node cannot be reflected intuitively, obtaining the entropy value which is used for representing the influence of each node on the structure and the function of the network and the importance degree of the node after normalization processing according to the related information of each node in the network, and measuring the importance of the node in different networks uniformly through the entropy value under the same data dimension, and reflecting the importance degree of each node in the network intuitively, so that the user can use the entropy value information of each node, the method has the advantages of distinguishing the importance degree of each node, determining the key nodes in the network and preferentially processing the related information of the key nodes.
It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present invention, nor do they necessarily limit the scope of the invention. Other features of the present invention will become apparent from the following description.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the description below are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
Fig. 1A is a flowchart of a node detection method according to an embodiment of the present invention.
Fig. 1B is a topology diagram of a network according to an embodiment of the present invention.
Fig. 2 is a flowchart of a node detection method according to a second embodiment of the present invention.
Fig. 3 is a schematic structural diagram of a node detection apparatus according to a third embodiment of the present invention.
Fig. 4 is a schematic structural diagram of an electronic device implementing the node detection method according to the embodiment of the present invention.
Detailed Description
In order to make the technical solutions of the present invention better understood, 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.
It should be noted that the terms "object," "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Example one
Fig. 1A is a flowchart of a node detection method according to an embodiment of the present invention, where this embodiment is applicable to detecting each node in a network according to related information of each node in the network, and determining a degree of importance of each node in the network. As shown in fig. 1A, the method includes:
The network association information of each node includes the weight of the associated edge of each node.
Optionally, the network to be detected is one or more networks for which the importance of each node in the network needs to be determined. A network is a topology consisting of edges and nodes. The network to be detected comprises a plurality of nodes and a plurality of edges. The edges connected to the nodes are the associated edges of the nodes. Two nodes connected by an edge are associated nodes with each other.
Optionally, the network association information of the node is information related to a connection relationship of the node in the network. The network association information of the node contains the weight of the associated edge of the node. The weight of the associated edge may be a weight set by an administrator of the network for the associated edge.
Optionally, the administrator of the network to be detected sets a weight for each associated edge of each node of the network to be detected. The higher the weight of the associated edge is, the higher the importance is, which indicates that the connection relationship corresponding to the associated edge has a larger influence or is more valuable to the whole network to be detected.
Optionally, the acquiring network association information of each node in the network to be detected includes: and acquiring network associated information of each node in the network to be detected uploaded by a user. The user may be a manager of the network to be detected. And uploading the network association information of each node in the network to be detected to the electronic equipment by the user. The electronic equipment acquires network association information of each node in the network to be detected, which is uploaded by a user.
And 102, calculating the basic entropy value of each node according to the weight of the associated edge of each node and a preset node entropy value calculation formula.
Optionally, the preset node entropy calculation formula is a calculation formula for calculating an entropy of a node. The entropy of a node characterizes the influence of the node on the structure and function of the network in which the node is located. Thus, the entropy value of a node can be seen as a measure representing the degree of importance of the node. The larger the entropy value of the node is, the larger the influence of the node on the structure and the function of the network where the node is located is represented, and the higher the importance degree of the node is. The smaller the entropy value of the node is, the smaller the influence of the node on the structure and the function of the network where the node is located is represented, and the lower the importance degree of the node is. The basic entropy value of the node is the entropy value of the node obtained by direct calculation according to a preset node entropy value calculation formula.
Optionally, the calculating the basic entropy value of each node according to the weight of the associated edge of each node and a preset node entropy value calculation formula includes: the following operations are performed for each node in the network to be detected: calculating the probability of the node in the network to be detected according to the weight of the associated edge of the node and the weight of all edges in the network to be detected; and calculating the basic entropy value of the node according to the probability of the node in the network to be detected and a preset node entropy value calculation formula.
Optionally, calculating the probability of a node in the network to be detected according to the weight of the associated edge of the node and the weights of the associated edges of all nodes in the network to be detected, includes: calculating the probability of the node in the network to be detected by using the following calculation formula:
and pi is the probability of the node in the network to be detected, ki is the sum of the weights of the associated edges of the node, and | E | is the sum of the weights of all the edges in the network to be detected. And summing the weights of all the associated edges of the nodes to obtain the sum of the weights of the associated edges of the nodes. According to the weight of the associated edge of each node, the weight of each edge in the network to be detected can be determined. And summing the weights of all edges in the network to be detected to obtain the sum of the weights of all edges in the network to be detected.
Optionally, calculating a basic entropy of a node according to the probability of the node in the network to be detected and a preset node entropy calculation formula, includes: calculating a base entropy value of the node using the following node entropy value calculation formula:
wherein, Encopy is a basic Entropy value of a node, pi is a probability of the node in the network to be detected, ki is a sum of weights of associated edges of the node, and | Ei | is a sum of weights of all edges in a sub-network formed by the node and all associated nodes.
In one embodiment, as shown in fig. 1B, the network includes 9 nodes: node 1, node 2, node 3, node 4, node 5, node 6, node 7, node 8, and node 9. The weight of each edge is labeled in FIG. 1B. The association node of the node 2 includes: node 1, node 3, node 5, and node 6. The sum of the weights of the associated edges of node 2 is 1+2+3+5 is 11. The sum of the weights of all edges in the sub-network formed by the node and all the associated nodes is 1+2+3+5+1, which is 12.
Optionally, calculating a basic entropy of a node according to the probability of the node in the network to be detected and a preset node entropy calculation formula, includes: calculating a base entropy value of the node using the following node entropy value calculation formula:
Entropy＝-pi*log 2 (pi)-(1-pi)*log 2 (1-pi)，
and the Entropy is a basic Entropy value of the node, and pi is the probability of the node in the network to be detected.
And 103, sequencing the basic entropy values of the nodes according to the numerical value from large to small.
Optionally, the basic entropy values of the nodes are sorted according to the numerical value from large to small. The base entropy value located in the first bit of the ranking result is the largest and the base entropy value located in the last bit of the ranking result is the smallest.
And 104, carrying out normalization processing on the basic entropy value of each node according to the sequencing result to obtain a target entropy value of each node.
Optionally, the target entropy of each node is a basic entropy after normalization.
Optionally, the normalizing the basic entropy of each node according to the sorting result to obtain the target entropy of each node includes: performing the following for each node: the target entropy value for the node is calculated using the following normalization formula:
vEntropy＝Entropy/maxEntropy，
and the vEncopy is a target Entropy value of the node, the Encopy is a basic Entropy value of the node, and the maxEncopy is a basic Entropy value positioned at the first position of the sequencing result.
The value range of the target entropy of each node obtained by using the normalization processing mode is (0, 1), and the node with the target entropy of 1 is the node with the maximum target entropy in each node.
Optionally, for the target entropy of each node obtained by using the above normalization processing manner, determining the most critical node and the negligible node in the network to be detected according to the target entropy of each node includes: determining a node with a target entropy value of 1 in each node as a most key node in the network to be detected; and sequencing the target entropy values of the nodes according to the numerical values from small to large, and determining the node with the target entropy value positioned at the first position of the sequencing result as a negligible node in the network to be detected.
Optionally, the most critical node in the network to be detected is the node with the greatest influence on the structure and function of the network to be detected and the highest importance degree. The node with the target entropy value of 1 is the node with the maximum target entropy value in all nodes, and represents that the node has the maximum influence on the structure and the function of the network to be detected and has the highest importance degree. Therefore, the node with the target entropy value of 1 in each node is determined as the most key node in the network to be detected.
Optionally, the negligible node in the network to be detected is a node with the minimum influence on the structure and function of the network to be detected and the minimum importance degree. And sequencing the target entropy values of the nodes according to the numerical values from small to large, wherein the node positioned at the first position of the sequencing result is the node with the minimum target entropy value in the nodes, and the node has the minimum influence on the structure and the function of the network to be detected and has the minimum importance degree. Therefore, the node with the target entropy located at the first position of the ordering result is determined as the negligible node in the network to be detected.
Optionally, the normalizing the basic entropy of each node according to the sorting result to obtain the target entropy of each node includes: performing the following for each node: the target entropy value for the node is calculated using the following normalization formula:
vEntropy＝(Entropy-vmin)/(vmax-vmin)，
wherein vEncopy is a target Entropy value of a node, Encopy is a base Entropy value of the node, vmax is a base Entropy value located at the first bit of the ranking result, and vmin is a base Entropy value located at the last bit of the ranking result.
The range of the target entropy of each node obtained by using the normalization processing mode is [0,1], the node with the target entropy of 1 is the node with the maximum target entropy among the nodes, and the node with the target entropy of 0 is the node with the minimum target entropy among the nodes.
Optionally, for the target entropy of each node obtained by using the above normalization processing manner, determining the most critical node and the negligible node in the network to be detected according to the target entropy of each node includes: determining a node with a target entropy value of 1 in each node as a most key node in the network to be detected; and determining the node with the target entropy value of 0 in each node as a negligible node in the network to be detected.
And 105, determining the most critical node and the negligible node in the network to be detected according to the target entropy of each node, and providing the target entropy of each node, the most critical node and the negligible node for a user.
Optionally, the user may be a manager of the network to be detected.
Optionally, the providing the target entropy of each node, the most critical node, and the negligible node to the user includes: and displaying a page through a detection result, and providing the target entropy value of each node, the most key node and the negligible node to a user.
Optionally, the detection result presentation page is a page for presenting the node detection result related to each node in the network to be detected to the user. Displaying a page through a detection result, and providing the target entropy value of each node, the most critical node and the negligible node to a user, wherein the method comprises the following steps: displaying identification information of the most key node in a most key node display area in a detection result display page so that a user can determine the most key node in the network to be detected; displaying identification information of the negligible node in a detection result display area in a page so that a user can determine the negligible node in the network to be detected; and displaying the identification information of each node and the target entropy of each node in an entropy display area in a detection result display page so that a user can determine the importance degree of each node in the network to be detected according to the target entropy of each node.
Optionally, the most critical node display area is a page area for displaying relevant information of the most critical node. The negligible node display area is a page area for displaying information related to the negligible node. The entropy display area is a page area for displaying the target entropy of each node in the network to be detected. The identification information of the node is information for uniquely identifying the node. Different nodes can be distinguished according to the identification information of the nodes.
According to the technical scheme of the embodiment of the invention, the network association information of each node in the network to be detected is obtained, and the network association information of each node comprises the weight of the association edge of each node; then, calculating the basic entropy value of each node according to the weight of the associated edge of each node and a preset node entropy value calculation formula; sequencing the basic entropy values of all nodes according to the numerical value from large to small; carrying out normalization processing on the basic entropy value of each node according to the sequencing result to obtain a target entropy value of each node; finally, according to the target entropy of each node, determining the most critical node and the negligible node in the network to be detected, providing the target entropy, the most critical node and the negligible node of each node to a user, solving the problems that in the related technology, the detection of each node in the network is performed only through a fixedly set index threshold, the node detection requirements of different networks are difficult to be met uniformly, and the importance degree of the node cannot be reflected intuitively, obtaining the entropy value which is used for representing the influence of each node on the structure and the function of the network and the importance degree of the node after normalization processing according to the related information of each node in the network, and measuring the importance of the node in different networks uniformly through the entropy value under the same data dimension, and reflecting the importance degree of each node in the network intuitively, so that the user can use the entropy value information of each node, the method has the advantages of distinguishing the importance degree of each node, determining the key nodes in the network and preferentially processing the related information of the key nodes.
Example two
Fig. 2 is a flowchart of a node detection method according to a second embodiment of the present invention, which may be combined with various alternatives in one or more of the above embodiments. As shown in fig. 2, the method includes:
The network association information of each node includes the weight of the associated edge of each node.
Optionally, the user uploads network association information of each node in the network to be detected to the electronic device. The electronic equipment acquires network association information of each node in the network to be detected, which is uploaded by a user.
And 202, calculating a basic entropy value of each node according to the weight of the associated edge of each node and a preset node entropy value calculation formula.
And step 203, sequencing the basic entropy values of the nodes according to the numerical value from large to small.
And 204, carrying out normalization processing on the basic entropy of each node according to the sequencing result to obtain a target entropy of each node.
Optionally, the detection result presentation page is a page for presenting the node detection result related to each node in the network to be detected to the user.
Optionally, displaying a page through a detection result, and providing the target entropy of each node, the most critical node, and the negligible node to a user, includes: displaying identification information of the most key node in a most key node display area in a detection result display page so that a user can determine the most key node in the network to be detected; displaying identification information of the negligible node in a detection result display area in a page so that a user can determine the negligible node in the network to be detected; and displaying the identification information of each node and the target entropy of each node in an entropy display area in a detection result display page so that a user can determine the importance degree of each node in the network to be detected according to the target entropy of each node.
Optionally, the most critical node display area is a page area for displaying relevant information of the most critical node. The negligible node display area is a page area for displaying information related to the negligible node. The entropy display area is a page area for displaying the target entropy of each node in the network to be detected. The identification information of the node is information for uniquely identifying the node. Different nodes can be distinguished according to the identification information of the nodes.
According to the technical scheme of the embodiment of the invention, entropy values which are subjected to normalization processing and used for representing the influence of each node on the structure and the function of the network and the importance degree of the node are obtained according to the related information of each node in the network, the importance of the nodes in different networks can be uniformly measured through the entropy values under the same data dimension, and the importance degree of each node in the network can be intuitively embodied, so that a user can distinguish the importance degree of each node through the entropy value information of each node, determine the key node in the network and preferentially process the related information of the key node.
EXAMPLE III
Fig. 3 is a schematic structural diagram of a node detection apparatus according to a third embodiment of the present invention. The apparatus may be configured in an electronic device. As shown in fig. 3, the apparatus includes: an information acquisition module 301, an entropy calculation module 302, an entropy ordering module 303, an entropy normalization module 304, and a result providing module 305.
The information acquisition module 301 is configured to acquire network association information of each node in a network to be detected; wherein, the network association information of each node comprises the weight of the association edge of each node; an entropy calculation module 302, configured to calculate a basic entropy of each node according to the weight of the associated edge of each node and a preset node entropy calculation formula; an entropy sorting module 303, configured to sort the basic entropy of each node according to a numerical value from large to small; an entropy normalization module 304, configured to perform normalization processing on the basic entropy of each node according to the sorting result to obtain a target entropy of each node; a result providing module 305, configured to determine a most critical node and a negligible node in the network to be detected according to the target entropy of each node, and provide the target entropy of each node, the most critical node, and the negligible node to a user.
According to the technical scheme of the embodiment of the invention, the network association information of each node in the network to be detected is obtained, and the network association information of each node comprises the weight of the association edge of each node; then, calculating the basic entropy value of each node according to the weight of the associated edge of each node and a preset node entropy value calculation formula; sequencing the basic entropy values of all nodes according to the numerical value from large to small; carrying out normalization processing on the basic entropy value of each node according to the sequencing result to obtain a target entropy value of each node; finally, according to the target entropy of each node, determining the most critical node and the negligible node in the network to be detected, providing the target entropy, the most critical node and the negligible node of each node to a user, solving the problems that in the related technology, the detection of each node in the network is performed only through a fixedly set index threshold, the node detection requirements of different networks are difficult to be met uniformly, and the importance degree of the node cannot be reflected intuitively, obtaining the entropy value which is used for representing the influence of each node on the structure and the function of the network and the importance degree of the node after normalization processing according to the related information of each node in the network, and measuring the importance of the node in different networks uniformly through the entropy value under the same data dimension, and reflecting the importance degree of each node in the network intuitively, so that the user can use the entropy value information of each node, the method has the advantages of distinguishing the importance degree of each node, determining the key nodes in the network and preferentially processing the related information of the key nodes.
In an optional implementation manner of the embodiment of the present invention, optionally, the information obtaining module 301 is specifically configured to: and acquiring network associated information of each node in the network to be detected uploaded by a user.
In an optional implementation manner of the embodiment of the present invention, optionally, the entropy normalization module 304 is specifically configured to: performing the following for each node: the target entropy value for the node is calculated using the following normalization formula:
vEntropy＝Entropy/mamEntropy，
where vEncopy is a target Entropy value of a node, Encopy is a base Entropy value of a node, and maxEncopy is a base Entropy value located first in the ranking result.
In an optional implementation manner of the embodiment of the present invention, optionally, when the result providing module 305 determines the operation of the most critical node and the negligible node in the network to be detected according to the target entropy of each node, specifically, it is configured to: determining a node with a target entropy value of 1 in each node as a most key node in the network to be detected; and sequencing the target entropy values of the nodes according to the numerical values from small to large, and determining the node with the target entropy value positioned at the first position of the sequencing result as a negligible node in the network to be detected.
In an optional implementation manner of the embodiment of the present invention, optionally, the entropy normalization module 304 is specifically configured to: performing the following for each node: the target entropy value for the node is calculated using the following normalization formula:
vEntropy＝(Entropy-vmin)/(vmax-vmin)，
wherein vEncopy is a target Entropy value of a node, Encopy is a base Entropy value of the node, vmax is a base Entropy value located at the first bit of the ranking result, and vmin is a base Entropy value located at the last bit of the ranking result.
In an optional implementation manner of the embodiment of the present invention, optionally, when the result providing module 305 determines the operation of the most critical node and the negligible node in the network to be detected according to the target entropy of each node, specifically, it is configured to: determining a node with a target entropy value of 1 in each node as a most key node in the network to be detected; and determining the node with the target entropy value of 0 in each node as a negligible node in the network to be detected.
In an optional implementation manner of the embodiment of the present invention, optionally, when performing an operation of providing the target entropy values of the nodes, the most critical node, and the negligible node to a user, the result providing module 305 is specifically configured to: and displaying a page through a detection result, and providing the target entropy value of each node, the most critical node and the negligible node for a user.
With regard to the apparatus in the above-described embodiment, the specific manner in which each module performs the operation has been described in detail in the embodiment related to the method, and will not be elaborated here.
The node detection device can execute the node detection method provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects of executing the node detection method.
Example four
Fig. 4 shows a schematic structural diagram of an electronic device 10 that can be used to implement the node detection method according to the embodiment of the present invention. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smart phones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed herein.
As shown in fig. 4, the electronic device 10 includes at least one processor 11, and a memory communicatively connected to the at least one processor 11, such as a Read Only Memory (ROM)12, a Random Access Memory (RAM)13, and the like, wherein the memory stores a computer program executable by the at least one processor, and the processor 11 may perform various suitable actions and processes according to the computer program stored in the Read Only Memory (ROM)12 or the computer program built from the storage unit 18 into the Random Access Memory (RAM) 13. In the RAM 13, various programs and data necessary for the operation of the electronic apparatus 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other via a bus 14. An input/output (I/O) interface 15 is also connected to bus 14.
A number of components in the electronic device 10 are connected to the I/O interface 15, including: an input unit 16 such as a keyboard, a mouse, or the like; an output unit 17 such as various types of displays, speakers, and the like; a storage unit 18 such as a magnetic disk, an optical disk, or the like; and a communication unit 19 such as a network card, modem, wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information/data with other devices via a computer network such as the internet and/or various telecommunication networks.
The processor 11 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various processors running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, or the like. The processor 11 performs the various methods and processes described above, such as the node detection method.
In some embodiments, the node detection method may be implemented as a computer program tangibly embodied in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 10 via the ROM 12 and/or the communication unit 19. When the computer program is built into the RAM 13 and executed by the processor 11, one or more steps of the node detection method described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to perform the node detection method by any other suitable means (e.g. by means of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuitry, Field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), system on a chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.
A computer program for implementing the node detection method of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions/acts specified in the flowchart and/or block diagram block or blocks to be performed. A computer program can execute entirely on a machine, partly on a machine, as a stand-alone software package partly on a machine and partly on a remote machine or entirely on a remote machine or server.
In the context of the present invention, a computer-readable storage medium may be a tangible medium that can contain, or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. A computer readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, the computer readable storage medium may be a machine readable signal medium. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user can provide input to the electronic device. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic, speech, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), Wide Area Networks (WANs), blockchain networks, and the internet.
The computing system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical host and VPS service are overcome.
It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present invention may be executed in parallel, sequentially, or in different orders, and are not limited herein as long as the desired results of the technical solution of the present invention can be achieved.
The above-described embodiments should not be construed as limiting the scope of the invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made in accordance with design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A node detection method, comprising:
acquiring network association information of each node in a network to be detected; wherein, the network association information of each node comprises the weight of the association edge of each node;
calculating the basic entropy value of each node according to the weight of the associated edge of each node and a preset node entropy value calculation formula;
sequencing the basic entropy values of the nodes according to the numerical values from large to small;
carrying out normalization processing on the basic entropy value of each node according to the sequencing result to obtain a target entropy value of each node;
and determining the most critical node and the negligible node in the network to be detected according to the target entropy of each node, and providing the target entropy of each node, the most critical node and the negligible node for a user.
2. The method according to claim 1, wherein the acquiring network association information of each node in the network to be detected comprises:
and acquiring network associated information of each node in the network to be detected uploaded by a user.
3. The method according to claim 1, wherein the normalizing the basic entropy of each node according to the ranking result to obtain the target entropy of each node comprises:
performing the following for each node:
the target entropy value for the node is calculated using the following normalization formula:
vEntropy＝Entropy/maxEntropy，
where vEncopy is a target Entropy value of a node, Encopy is a base Entropy value of a node, and maxEncopy is a base Entropy value located first in the ranking result.
4. The method according to claim 3, wherein the determining the most critical node and the negligible node in the network to be detected according to the target entropy of each node comprises:
determining a node with a target entropy value of 1 in each node as a most key node in the network to be detected;
and sequencing the target entropy values of the nodes according to the numerical values from small to large, and determining the node with the target entropy value positioned at the first position of the sequencing result as a negligible node in the network to be detected.
5. The method according to claim 1, wherein the normalizing the basic entropy of each node according to the ranking result to obtain the target entropy of each node comprises:
performing the following for each node:
the target entropy value for the node is calculated using the following normalization formula:
vEntropy＝(Entropy-vmin)/(vmax-vmin)，
wherein vEncopy is a target Entropy value of a node, Encopy is a base Entropy value of the node, vmax is a base Entropy value located at the first bit of the ranking result, and vmin is a base Entropy value located at the last bit of the ranking result.
6. The method according to claim 5, wherein the determining the most critical node and the negligible node in the network to be detected according to the target entropy of each node comprises:
determining a node with a target entropy value of 1 in each node as a most key node in the network to be detected;
and determining the node with the target entropy value of 0 in each node as a negligible node in the network to be detected.
7. The method of claim 1, wherein providing the target entropy values for the nodes, the most critical nodes, and the negligible nodes to a user comprises:
and displaying a page through a detection result, and providing the target entropy value of each node, the most critical node and the negligible node for a user.
8. A node detection apparatus, comprising:
the information acquisition module is used for acquiring network association information of each node in the network to be detected; wherein, the network association information of each node comprises the weight of the association edge of each node;
the entropy calculation module is used for calculating the basic entropy of each node according to the weight of the associated edge of each node and a preset node entropy calculation formula;
the entropy value sequencing module is used for sequencing the basic entropy values of the nodes according to the numerical value from large to small;
the entropy normalization module is used for carrying out normalization processing on the basic entropy of each node according to the sequencing result to obtain a target entropy of each node;
and the result providing module is used for determining the most critical node and the negligible node in the network to be detected according to the target entropy of each node and providing the target entropy of each node, the most critical node and the negligible node for a user.
9. An electronic device, characterized in that the electronic device comprises:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the node detection method of any one of claims 1-7.
10. A computer-readable storage medium storing computer instructions for causing a processor to perform the node detection method of any one of claims 1-7 when executed.
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