CN111934937A - Dependent network node importance degree evaluation method and device based on importance iteration - Google Patents
Dependent network node importance degree evaluation method and device based on importance iteration Download PDFInfo
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Abstract
The application relates to a method and a device for evaluating the importance of a dependent network node based on importance iteration. The method comprises the following steps: the method comprises the steps of obtaining an event to be evaluated, constructing a multi-layer node network of the event to be evaluated, fusing the multi-layer node network according to dependent node pairs to obtain an aggregation node network, obtaining a node to be evaluated corresponding to the event to be evaluated in the aggregation node network, determining a neighbor node set of the node to be evaluated, feeding back the importance value of the neighbor node to the node to be evaluated when the importance value of the neighbor node is larger than the importance value of the node to be evaluated during importance iteration, feeding back the importance value of the node to be evaluated to the neighbor node to be evaluated when the importance value of the neighbor node is smaller than the importance value of the node to be evaluated, and evaluating the importance of the node to be evaluated according to the importance value of the node to be evaluated when a preset iteration stop condition is met. By adopting the method, the importance of the node can be accurately evaluated.
Description
Technical Field
The present application relates to the field of computer technologies, and in particular, to a method and an apparatus for evaluating importance of dependent network nodes based on importance iteration.
Background
When analyzing events in real life, the connection relationship between the nodes in the node network is often adopted to represent the events through the connection between the events, so that the problem of the events is solved in the node network. However, the interdependency of the networks brings new vulnerability factors to the systems, and the fault of the single-side network can generate chain fault of interactive propagation among the networks due to the coupling relationship among the systems, thereby enlarging the failure scale. The single-side network cannot analyze information of other networks, so that the importance analysis of network nodes is difficult.
Disclosure of Invention
Based on this, it is necessary to provide a method and an apparatus for evaluating importance of dependent network nodes based on importance iteration, which can solve the difficulty of analyzing the importance of network nodes having network dependencies.
A method for importance evaluation of dependent network nodes based on importance iteration, the method comprising:
acquiring an event to be evaluated, and constructing a multilayer node network of the event to be evaluated; including dependent node pairs in the multi-layer node network;
fusing the multilayer node network according to the dependent node pairs to obtain an aggregation node network;
acquiring a node to be evaluated corresponding to an event to be evaluated in the aggregation node network, and determining a neighbor node set of the node to be evaluated;
when the importance value of the neighbor node in the neighbor node set is larger than the importance value of the node to be evaluated, feeding the importance value of the neighbor node back to the node to be evaluated, and when the importance value of the neighbor node in the neighbor node set is smaller than the importance value of the node to be evaluated, feeding the importance value of the node to be evaluated back to the neighbor node;
and when a preset iteration stop condition is met, outputting the importance value of the node to be evaluated, and evaluating the importance of the node to be evaluated according to the importance value of the node to be evaluated.
In one embodiment, the method further comprises the following steps: and superposing the dependent node pairs in the multilayer node network, and reserving edges corresponding to the nodes in the dependent node pairs to obtain the aggregation node network.
In one embodiment, the method further comprises the following steps: setting an initial value of each node in the aggregation node network during importance iteration; determining a score expression of a node in the aggregation node network as:
wherein the content of the first and second substances,
representing the importance score of the node at time step t +1, R represents the importance feedback matrix,
representing the importance score of a node at time step t,
represents an initial value;
according to the score expression of the node, determining the importance score of the node to be evaluated at the time step of t +1 as follows:
wherein the content of the first and second substances,
representing the time step of the node i to be evaluated at t +1The importance score of (a) is determined,
a set of neighbor nodes representing a node i to be evaluated,
representing neighbor nodesjFeeding back to the node to be evaluatediIs given an importance score oflNetwork layer components, a and B respectively representing the network layers of a multi-layer node network,
representing nodesjThe number of participating network layers is such that,
representing network layerslAdjacent matrix ofiGo to the firstjElements of columns, nodesiAndjwhen a connecting edge exists between the two edges,
=1, otherwise 0,
to representlNode in layerjFeedback to the nodeiAn importance score of;
wherein the content of the first and second substances,
，
representing the heuristic centrality value of node i.
In one embodiment, the method further comprises the following steps: determining a score expression of a node in the aggregation node network as:
wherein the elements in the importance feedback matrix R
Comprises the following steps:
an apparatus for importance iteration-based dependent network node importance assessment, the apparatus comprising:
the network construction module is used for acquiring an event to be evaluated and constructing a multilayer node network of the event to be evaluated; including dependent node pairs in the multi-layer node network;
the aggregation module is used for fusing the multilayer node network according to the dependent node pairs to obtain an aggregation node network;
the neighbor node acquisition module is used for acquiring a node to be evaluated corresponding to the event to be evaluated in the aggregation node network and determining a neighbor node set of the node to be evaluated;
the iteration module is used for feeding back the importance scores of the neighbor nodes to the nodes to be evaluated when the importance scores of the neighbor nodes in the neighbor node set are larger than the importance scores of the nodes to be evaluated, and feeding back the importance scores of the nodes to be evaluated to the neighbor nodes when the importance scores of the neighbor nodes in the neighbor node set are smaller than the importance scores of the nodes to be evaluated;
and the output module is used for outputting the importance value of the node to be evaluated when a preset iteration stop condition is met, and evaluating the importance of the node to be evaluated according to the importance value of the node to be evaluated.
In one embodiment, the aggregation module is further configured to superimpose dependent node pairs in the multilayer node network, and edges corresponding to nodes in the dependent node pairs are reserved to obtain an aggregation node network.
In one embodiment, the iteration module is further configured to set an initial value of each node in the aggregation node network when the importance is iterated;
determining a score expression of a node in the aggregation node network as:
wherein the content of the first and second substances,
representing the importance score of the node at time step t +1, R represents the importance feedback matrix,
representing the importance score of a node at time step t,
represents an initial value;
according to the score expression of the node, determining the importance score of the node to be evaluated at the time step of t +1 as follows:
wherein the content of the first and second substances,
represents the importance score of the node i to be evaluated at the time step of t +1,
a set of neighbor nodes representing a node i to be evaluated,
representing neighbor nodesjFeeding back to the node to be evaluatediIs given an importance score oflNetwork layer components, a and B respectively representing the network layers of a multi-layer node network,
representing nodesjThe number of participating network layers is such that,
representing network layerslAdjacent matrix ofiGo to the firstjElements of columns, nodesiAndjwhen a connecting edge exists between the two edges,
=1, otherwise 0,
to representlNode in layerjFeedback to the nodeiAn importance score of;
wherein the content of the first and second substances,
，
representing the heuristic centrality value of node i.
In one embodiment, the iteration module is further configured to determine the score expression of the node in the aggregation node network as:
wherein the elements in the importance feedback matrix R
Comprises the following steps:
a computer device comprising a memory and a processor, the memory storing a computer program, the processor implementing the following steps when executing the computer program:
acquiring an event to be evaluated, and constructing a multilayer node network of the event to be evaluated; including dependent node pairs in the multi-layer node network;
fusing the multilayer node network according to the dependent node pairs to obtain an aggregation node network;
acquiring a node to be evaluated corresponding to an event to be evaluated in the aggregation node network, and determining a neighbor node set of the node to be evaluated;
when the importance value of the neighbor node in the neighbor node set is larger than the importance value of the node to be evaluated, feeding the importance value of the neighbor node back to the node to be evaluated, and when the importance value of the neighbor node in the neighbor node set is smaller than the importance value of the node to be evaluated, feeding the importance value of the node to be evaluated back to the neighbor node;
and when a preset iteration stop condition is met, outputting the importance value of the node to be evaluated, and evaluating the importance of the node to be evaluated according to the importance value of the node to be evaluated.
A computer-readable storage medium, on which a computer program is stored which, when executed by a processor, carries out the steps of:
acquiring an event to be evaluated, and constructing a multilayer node network of the event to be evaluated; including dependent node pairs in the multi-layer node network;
fusing the multilayer node network according to the dependent node pairs to obtain an aggregation node network;
acquiring a node to be evaluated corresponding to an event to be evaluated in the aggregation node network, and determining a neighbor node set of the node to be evaluated;
when the importance value of the neighbor node in the neighbor node set is larger than the importance value of the node to be evaluated, feeding the importance value of the neighbor node back to the node to be evaluated, and when the importance value of the neighbor node in the neighbor node set is smaller than the importance value of the node to be evaluated, feeding the importance value of the node to be evaluated back to the neighbor node;
and when a preset iteration stop condition is met, outputting the importance value of the node to be evaluated, and evaluating the importance of the node to be evaluated according to the importance value of the node to be evaluated.
According to the method, the device, the computer equipment and the storage medium for evaluating the importance of the dependent network node based on the importance iteration, when the event to be evaluated is obtained, a multilayer node network of the time to be evaluated is constructed, each event is a node in the multilayer node network, the importance of the event can be reflected through the importance of the node in the network, then the multilayer node networks are fused to obtain a aggregation node network, then the importance value of the node to be evaluated is obtained in an iteration mode, and therefore the importance of the node to be evaluated is evaluated. According to the embodiment of the invention, the global information of the network is obtained in an iteration mode from the neighbor node of the node to be evaluated, so that the importance evaluation of the node is more accurate.
Drawings
FIG. 1 is a flowchart illustrating a method for importance evaluation of dependent network nodes based on importance iteration according to an embodiment;
FIG. 2 is a block diagram of an aggregation node network in one embodiment;
FIG. 3 is a block diagram of an apparatus for importance evaluation of dependent network nodes according to an embodiment of the present invention;
FIG. 4 is a diagram illustrating an internal structure of a computer device according to an embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
In one embodiment, as shown in fig. 1, there is provided a method for evaluating importance of dependent network nodes based on importance iteration, comprising the following steps:
and 102, acquiring an event to be evaluated, and constructing a multilayer node network of the event to be evaluated.
The event to be evaluated can be a public opinion origin point analysis event, a network virus propagation event and the like, and for the event to be evaluated, other events related to the event to be evaluated can be obtained, so that a node network is established.
And 104, fusing the multilayer node network according to the dependent node pairs to obtain an aggregation node network.
Fusion refers to the aggregation of two nodes, for example: for node a and node b in the dependent node pair, a new node c is formed after aggregation, and the node c contains the information of both node a and node b.
And 106, acquiring a node to be evaluated corresponding to the event to be evaluated in the aggregation node network, and determining a neighbor node set of the node to be evaluated.
In the aggregation node network, for the determined node to be evaluated, the neighbor node set of the node to be evaluated can be determined.
And 108, during importance iteration, when the importance value of the neighbor node in the neighbor node set is larger than that of the node to be evaluated, feeding the importance value of the neighbor node back to the node to be evaluated, and when the importance value of the neighbor node in the neighbor node set is smaller than that of the node to be evaluated, feeding the importance value of the node to be evaluated back to the neighbor node.
And step 110, outputting the importance value of the node to be evaluated when the preset iteration stop condition is met, and evaluating the importance of the node to be evaluated according to the importance value of the node to be evaluated.
According to the method for evaluating the importance of the dependent network node based on the importance iteration, when the event to be evaluated is obtained, a multilayer node network of the time to be evaluated is built, each event is a node in the multilayer node network, the importance of the event can be reflected through the importance of the node in the network, then the multilayer node network is fused to obtain a cluster node network, then the importance value of the node to be evaluated is obtained in an iteration mode, and therefore the importance of the node to be evaluated is evaluated. According to the embodiment of the invention, the global information of the network is obtained in an iteration mode from the neighbor node of the node to be evaluated, so that the importance evaluation of the node is more accurate.
In one embodiment, as shown in fig. 2, the dependent node pairs in the multi-layer node network are superimposed, and edges corresponding to nodes in the dependent node pairs are reserved, so as to obtain an aggregation node network. In this embodiment, edges existing between each layer are reserved in the aggregation node network, and therefore the aggregation node network includes edge information of each layer of the network.
In one embodiment, in the importance iteration, an initial value of each node in the aggregation node network is set, and the score expression of the node in the aggregation node network is determined as follows:
wherein the content of the first and second substances,
representing the importance score of the node at time step t +1, R represents the importance feedback matrix,
representing the importance score of a node at time step t,
represents an initial value;
according to the score expression of the node, determining the importance score of the node to be evaluated at the time step of t +1 as follows:
wherein the content of the first and second substances,
represents the importance score of the node i to be evaluated at the time step of t +1,
a set of neighbor nodes representing a node i to be evaluated,
representing neighbor nodesjFeeding back to the node to be evaluatediIs given an importance score oflNetwork layer components, a and B respectively representing the network layers of a multi-layer node network,
representing nodesjThe number of participating network layers is such that,
representing network layerslAdjacent matrix ofiGo to the firstjElements of columns, nodesiAndjwhen a connecting edge exists between the two edges,
=1, otherwise 0,
to representlNode in layerjFeedback to the nodeiAn importance score of;
wherein the content of the first and second substances,
，
representing the heuristic centrality value of node i.
In this embodiment, a scheme for determining importance scores is provided, and a heuristic method is usedA centrality value, which can be adjusted
The importance value of the neighbor node is fed back to the node to be evaluated when the importance value of the neighbor node in the neighbor node set is larger than the importance value of the node to be evaluated, and the importance value of the node to be evaluated is fed back to the neighbor node when the importance value of the neighbor node in the neighbor node set is smaller than the importance value of the node to be evaluated.
Specifically, the heuristic centrality value may be a centrality, a k-shell value, or the like.
During specific calculation, because the aggregated dependent network is a heterogeneous network in nature and the edges have different connotations, importance feedback given to the target node by the neighbor nodes in the iterative process needs to be considered in a layering manner. Suppose that
Is a nodeiIs determined by the node of the neighbor node set,
is shown at time stept+1, neighbor nodesjFeedback to the nodeiThe importance score of, then nodeiAt iterationtScore after +1 step
Can be expressed as:
due to the presence of at least two different types of connected edges in the network, it follows:
in another embodiment, the score expression for a node in the aggregation node network is determined as:
wherein the elements in the importance feedback matrix R
Comprises the following steps:
by the method, the importance scores of all the nodes in the aggregation node network can be actually obtained, so that the importance of all the nodes is determined to be evaluated.
In summary, the basic idea of the invention is as follows: for the aggregated network, it is assumed that in the initial stage, each node in the network is assigned with an initial value of one unit, and then each node starts to receive the importance value fed back by the neighboring node, the value feedback rule is determined by the importance of the node, and the process considers the influence of neighboring edges of different types (different layers). And after multiple iterative feedbacks, until the scores of all the nodes reach a balance state, wherein the score of each node is the importance score of the node.
It should be understood that, although the steps in the flowchart of fig. 1 are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least a portion of the steps in fig. 1 may include multiple sub-steps or multiple stages that are not necessarily performed at the same time, but may be performed at different times, and the order of performance of the sub-steps or stages is not necessarily sequential, but may be performed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.
In one embodiment, as shown in fig. 3, there is provided an importance iteration-based dependent network node importance evaluation apparatus, including: a network construction module 302, an aggregation module 304, a neighbor node acquisition module 306, an iteration module 308, and an output module 310, wherein:
a network construction module 302, configured to obtain an event to be evaluated, and construct a multi-layer node network of the event to be evaluated; including dependent node pairs in the multi-layer node network;
the aggregation module 304 is configured to fuse the multi-layer node network according to the dependent node pairs to obtain an aggregation node network;
a neighbor node obtaining module 306, configured to obtain a node to be evaluated corresponding to an event to be evaluated in the aggregation node network, and determine a neighbor node set of the node to be evaluated;
the iteration module 308 is configured to, during importance iteration, feed back the importance scores of the neighbor nodes to the node to be evaluated when the importance scores of the neighbor nodes in the neighbor node set are greater than the importance scores of the node to be evaluated, and feed back the importance scores of the node to be evaluated to the neighbor nodes when the importance scores of the neighbor nodes in the neighbor node set are less than the importance scores of the node to be evaluated;
the output module 310 is configured to output the importance score of the node to be evaluated when a preset iteration stop condition is met, and evaluate the importance of the node to be evaluated according to the importance score of the node to be evaluated.
In one embodiment, the aggregation module 304 is further configured to superimpose dependent node pairs in the multi-layer node network, where edges corresponding to nodes in the dependent node pairs are reserved, so as to obtain an aggregation node network.
In one embodiment, the iteration module 308 is further configured to set an initial value of each node in the aggregation node network when iterating over importance;
determining a score expression of a node in the aggregation node network as:
wherein the content of the first and second substances,
representing the importance score of the node at time step t +1, R represents the importance feedback matrix,
representing the importance score of a node at time step t,
represents an initial value;
according to the score expression of the node, determining the importance score of the node to be evaluated at the time step of t +1 as follows:
wherein the content of the first and second substances,
represents the importance score of the node i to be evaluated at the time step of t +1,
a set of neighbor nodes representing a node i to be evaluated,
representing neighbor nodesjFeeding back to the node to be evaluatediIs given an importance score oflNetwork layer components, a and B respectively representing the network layers of a multi-layer node network,
representing nodesjThe number of participating network layers is such that,
representing network layerslAdjacent matrix ofiGo to the firstjElements of columns, nodesiAndjwhen a connecting edge exists between the two edges,
=1, otherwise 0,
to representlNode in layerjFeedback to the nodeiAn importance score of;
wherein the content of the first and second substances,
，
representing the heuristic centrality value of node i.
In one embodiment, the iteration module 308 is further configured to determine the score expression of the nodes in the aggregation node network as:
wherein the elements in the importance feedback matrix R
Comprises the following steps:
for specific limitations of the apparatus for evaluating importance of a dependent network node based on importance iteration, refer to the above limitations of the method for evaluating importance of a dependent network node based on importance iteration, which are not described herein again. The modules in the above-mentioned apparatus for evaluating the importance of dependent network nodes based on iteration of importance may be implemented in whole or in part by software, hardware and their combination. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
In one embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as shown in fig. 4. The computer device includes a processor, a memory, a network interface, a display screen, and an input device connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a dependent network node importance assessment method based on importance iteration. The display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, a key, a track ball or a touch pad arranged on the shell of the computer equipment, an external keyboard, a touch pad or a mouse and the like.
Those skilled in the art will appreciate that the architecture shown in fig. 4 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
In an embodiment, a computer device is provided, comprising a memory storing a computer program and a processor implementing the steps of the method in the above embodiments when the processor executes the computer program.
In an embodiment, a computer-readable storage medium is provided, on which a computer program is stored, which computer program, when being executed by a processor, carries out the steps of the method in the above-mentioned embodiments.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory, among others. Non-volatile memory can include read-only memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus Direct RAM (RDRAM), direct bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (10)
1. A method for evaluating the importance of a dependent network node based on importance iteration is characterized in that the method comprises the following steps:
acquiring an event to be evaluated, and constructing a multilayer node network of the event to be evaluated; including dependent node pairs in the multi-layer node network;
fusing the multilayer node network according to the dependent node pairs to obtain an aggregation node network;
acquiring a node to be evaluated corresponding to an event to be evaluated in the aggregation node network, and determining a neighbor node set of the node to be evaluated;
during importance iteration, when the importance value of the neighbor node in the neighbor node set is larger than the importance value of the node to be evaluated, the importance value of the neighbor node is fed back to the node to be evaluated, and when the importance value of the neighbor node in the neighbor node set is smaller than the importance value of the node to be evaluated, the importance value of the node to be evaluated is fed back to the neighbor node;
and when a preset iteration stop condition is met, outputting the importance value of the node to be evaluated, and evaluating the importance of the node to be evaluated according to the importance value of the node to be evaluated.
2. The method of claim 1, wherein fusing the multi-layer node network according to the dependent node pairs to obtain an aggregated node network comprises:
and superposing the dependent node pairs in the multilayer node network, and reserving edges corresponding to the nodes in the dependent node pairs to obtain the aggregation node network.
3. The method according to claim 1, wherein in importance iteration, when the importance score of the neighbor node in the neighbor node set is greater than the importance score of the node to be evaluated, the importance score of the neighbor node is fed back to the node to be evaluated, and when the importance score of the neighbor node in the neighbor node set is less than the importance score of the node to be evaluated, the importance score of the node to be evaluated is fed back to the neighbor node, and the method comprises:
setting an initial value of each node in the aggregation node network during importance iteration;
determining a score expression of a node in the aggregation node network as:
wherein the content of the first and second substances,
representing the importance score of the node at time step t +1, R represents the importance feedback matrix,
representing the importance score of a node at time step t,
represents an initial value;
according to the score expression of the node, determining the importance score of the node to be evaluated at the time step of t +1 as follows:
wherein the content of the first and second substances,
represents the importance score of the node i to be evaluated at the time step of t +1,
a set of neighbor nodes representing a node i to be evaluated,
representing neighbor nodesjFeeding back to the node to be evaluatediIs given an importance score oflNetwork layer components, a and B respectively representing the network layers of a multi-layer node network,
representing nodesjThe number of participating network layers is such that,
representing network layerslAdjacent matrix ofiGo to the firstjElements of columns, nodesiAndjwhen a connecting edge exists between the two edges,
=1, otherwise 0,
to representlNode in layerjFeedback to the nodeiAn importance score of;
wherein the content of the first and second substances,
，
representing the heuristic centrality value of node i.
4. The method of claim 3, further comprising:
determining a score expression of a node in the aggregation node network as:
wherein the elements in the importance feedback matrix R
Comprises the following steps:
5. an apparatus for evaluating importance of dependent network nodes based on iteration of importance, the apparatus comprising:
the network construction module is used for acquiring an event to be evaluated and constructing a multilayer node network of the event to be evaluated; including dependent node pairs in the multi-layer node network;
the aggregation module is used for fusing the multilayer node network according to the dependent node pairs to obtain an aggregation node network;
the neighbor node acquisition module is used for acquiring a node to be evaluated corresponding to the event to be evaluated in the aggregation node network and determining a neighbor node set of the node to be evaluated;
the iteration module is used for feeding back the importance scores of the neighbor nodes to the nodes to be evaluated when the importance scores of the neighbor nodes in the neighbor node set are larger than the importance scores of the nodes to be evaluated during importance iteration, and feeding back the importance scores of the nodes to be evaluated to the neighbor nodes when the importance scores of the neighbor nodes in the neighbor node set are smaller than the importance scores of the nodes to be evaluated;
and the output module is used for outputting the importance value of the node to be evaluated when a preset iteration stop condition is met, and evaluating the importance of the node to be evaluated according to the importance value of the node to be evaluated.
6. The apparatus of claim 5, wherein the aggregation module is further configured to superimpose dependent node pairs in the multi-layer node network, and edges corresponding to nodes in the dependent node pairs are reserved to obtain an aggregation node network.
7. The apparatus of claim 5, wherein the iteration module is further configured to set an initial value for each node in the aggregation node network when iterating over importance;
determining a score expression of a node in the aggregation node network as:
wherein the content of the first and second substances,
representing the importance score of the node at time step t +1, R represents the importance feedback matrix,
representing the importance score of a node at time step t,
represents an initial value;
according to the score expression of the node, determining the importance score of the node to be evaluated at the time step of t +1 as follows:
wherein the content of the first and second substances,
represents the importance score of the node i to be evaluated at the time step of t +1,
a set of neighbor nodes representing a node i to be evaluated,
representing neighbor nodesjFeeding back to the node to be evaluatediIs given an importance score oflNetwork layer components, a and B respectively representing the network layers of a multi-layer node network,
representing nodesjThe number of participating network layers is such that,
representing network layerslAdjacent matrix ofiGo to the firstjElements of columns, nodesiAndjwhen a connecting edge exists between the two edges,
=1, otherwise 0,
to representlNode in layerjFeedback to the nodeiAn importance score of;
wherein the content of the first and second substances,
，
representing the heuristic centrality value of node i.
8. The apparatus of claim 7, wherein the iteration module is further configured to determine the score expression for the nodes in the aggregation node network as:
wherein the elements in the importance feedback matrix R
Comprises the following steps:
9. a computer device comprising a memory and a processor, the memory storing a computer program, wherein the processor implements the steps of the method of any one of claims 1 to 4 when executing the computer program.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 4.
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