CN109347680B - Network topology reconstruction method and device and terminal equipment - Google Patents
Network topology reconstruction method and device and terminal equipment Download PDFInfo
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Abstract
The invention is suitable for the technical field of information, and provides a network topology reconstruction method, a device and terminal equipment, wherein the method comprises the following steps: acquiring the number of network edges and the number of network nodes of a target network; simulating a process of spreading a plurality of pieces of information in a target network to obtain an information recording matrix and an information arrival matrix, wherein the information recording matrix records data of each network node infected by one or more pieces of information, and the information arrival matrix records the time of each network node infected by each piece of information; selecting any two network nodes infected by the same information, and calculating the time difference of the two network nodes infected according to the information recording matrix and the information arrival matrix; selecting two network nodes with time difference as rated parameters as similar node pairs; calculating the time similarity of the similar node pairs; and reconstructing the topological structure of the target network according to the time similarity and the network edge number. The method and the device can improve the accuracy of predicting the missing connection in the network topology structure.
Description
Technical Field
The present invention relates to the field of information technologies, and in particular, to a network topology reconfiguration method, an apparatus, and a terminal device.
Background
When investigating the propagation source of a disease or the propagation source of information in a complex network, important nodes are generally found and then limited, for example, for the control of certain disease sources and information sources. But the underlying information about the propagation network is often incomplete and therefore it is necessary to exploit the known information to reconstruct unknown or hidden topologies in the network. However, in the big data era, the speed and the breadth of information transmission are continuously increased, and the structure of a transmission network is huge, so that the reproduction of a network topology structure is very difficult.
Currently, for the reconstruction of the network topology, a link prediction method is generally adopted, which predicts missing connections in various networks according to a part of information of the networks. However, the existing link prediction method predicts the node relationship based on the static similarity measurement or the multi-step time similarity measurement, and the accuracy is unstable, so that the missing connection in the network topology structure cannot be accurately predicted.
Disclosure of Invention
The invention mainly aims to provide a network topology reconstruction method, a network topology reconstruction device and terminal equipment, and aims to solve the problems that the existing network topology reconstruction method is unstable in precision and can not accurately predict missing connections in a network topology structure.
In order to achieve the above object, a first aspect of the embodiments of the present invention provides a method for reconstructing a network topology, including:
acquiring the number of network edges and the number of network nodes of a target network;
simulating a process of spreading a plurality of pieces of information in the target network to obtain an information recording matrix and an information arrival matrix, wherein the information recording matrix records data of each network node infected by one or more pieces of information, and the information arrival matrix records the time of each network node infected by each piece of information;
selecting any two network nodes infected by the same information, and calculating the time difference of the two network nodes infected according to the information recording matrix and the information arrival matrix;
selecting two network nodes with the time difference as a rated parameter as a similar node pair;
calculating the time similarity of the similar node pairs;
and reconstructing the topological structure of the target network according to the time similarity and the network edge number.
With reference to the first aspect of the present invention, in a first embodiment of the first aspect of the present invention, the rated parameter is 1.
With reference to the first aspect of the present invention, in a second implementation manner of the first aspect of the present invention, before simulating an information propagation process in the target network and obtaining an information recording matrix and an information arrival matrix, the simulating includes:
setting the probability of the network node becoming an information source;
and calculating the number of the information of the simulated propagation according to the number of the network nodes and the probability.
With reference to the first aspect of the present invention, in a third implementation manner of the first aspect of the present invention, the calculating a time similarity formula of the pair of similar nodes is as follows:
wherein, | tiα-tjαI is the time difference of two network nodes infected, R is the information recording matrix, T is the information recording matrix, i and j represent nodes, α represents information representing a specific time difference, and TCN1 represents a one-step time similarity measure.
With reference to the first aspect of the present invention, in a fourth implementation manner of the first aspect of the present invention, the reconstructing a topology of the target network according to the time similarity and the number of network edges includes:
selecting N similar node pairs with larger numerical values in the time similarity as edge connecting node pairs, wherein N is the number of network edges;
and connecting the two network nodes of each edge connecting node pair to reconstruct the topological structure of the network.
A second aspect of the embodiments of the present invention provides a network topology reconfiguration apparatus, including:
the network information acquisition module is used for acquiring the number of network edges and the number of network nodes of a target network;
the matrix recording module is used for simulating the process of spreading a plurality of pieces of information in the target network to obtain an information recording matrix and an information arrival matrix, wherein the information recording matrix records the data of each network node infected by one or more pieces of information, and the information arrival matrix records the time of each network node infected by each piece of information;
the time difference calculation module is used for selecting any two network nodes infected by the same information and calculating the time difference of the two network nodes infected according to the information recording matrix and the information arrival matrix;
the similar node pair selection module is used for selecting two network nodes with the time difference as a rated parameter as similar node pairs;
the time similarity calculation module is used for calculating the time similarity of the similar node pairs;
and the network reconstruction module reconstructs the topological structure of the target network according to the time similarity and the network edge number.
With reference to the second aspect of the present invention, in a first embodiment of the second aspect of the present invention, the rated parameter is 1.
With reference to the second aspect of the present invention, in a second implementation manner of the second aspect of the present invention, the apparatus further includes a probability setting module and a propagation calculation module;
the probability setting module is used for setting the probability of the network node becoming an information source;
and the propagation calculation module is used for calculating the number of the information simulating propagation according to the number of the network nodes and the probability.
A third aspect of embodiments of the present invention provides a terminal device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the steps of the method provided in the first aspect when executing the computer program.
A fourth aspect of embodiments of the present invention provides a computer-readable storage medium storing a computer program which, when executed by a processor, implements the steps of the method as provided in the first aspect above.
The embodiment of the invention provides a network topology reconstruction method, which comprises the steps of simulating the propagation process of information in a target network, simultaneously recording an information recording matrix and an information arrival matrix, obtaining the arrival time of each piece of information in each network node, then calculating the difference between the arrival times of the same information on any two network nodes, selecting similar node pairs according to the time difference, and calculating the time similarity of the similar node pairs; the time similarity of all similar node pairs and the network edge number in the target network are combined, the reconstruction of the target network topological structure can be realized, the complete target network topological structure is obtained, meanwhile, the time measurement between the similar node pairs can be controlled by controlling the value of the rated parameter, namely, two network nodes with smaller rated parameters are selected as the similar node pairs, and therefore the accuracy of predicting the missing connection in the network topological structure is improved.
Drawings
Fig. 1 is a schematic flow chart illustrating an implementation process of a network topology reconfiguration method according to an embodiment of the present invention;
fig. 2 is a schematic flow chart illustrating an implementation of a network topology reconstruction method according to a second embodiment of the present invention;
fig. 3 is a schematic diagram of an algorithm implementation flow of a network topology reconstruction method according to a second embodiment of the present invention;
fig. 4 is a schematic diagram illustrating an effect of a conventional link prediction method in a BA network according to a third embodiment of the present invention;
fig. 5 is a schematic diagram illustrating an effect of a conventional link prediction method in an SW network according to a third embodiment of the present invention;
fig. 6 is a schematic diagram illustrating a comparison between reconstruction effects of a network topology reconstruction method and a conventional link prediction method according to a third embodiment of the present invention;
fig. 7 is a schematic diagram illustrating comparison between reconstruction effects of a network topology reconstruction method and a multi-step time similarity method according to a third embodiment of the present invention;
fig. 8 is a schematic diagram illustrating a comparison between reconstruction effects of a network topology reconstruction method and a time similarity method according to a larger probability value provided in the third embodiment of the present invention;
fig. 9 is a schematic structural diagram of a network topology reconfiguration device according to a fourth embodiment of the present invention.
The implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
Suffixes such as "module", "part", or "unit" used to denote elements are used herein only for the convenience of description of the present invention, and have no specific meaning in themselves. Thus, "module" and "component" may be used in a mixture.
In the following description, the serial numbers of the embodiments of the invention are merely for description and do not represent the merits of the embodiments.
Example one
As shown in fig. 1, an embodiment of the present invention provides a network topology reconstruction method, which improves accuracy of predicting missing connections in a network topology by controlling time differences between pairs of similar nodes, where the network topology reconstruction method includes, but is not limited to:
s101, acquiring the number of network edges and the number of network nodes of a target network.
In the step S101, the topology of the network is a structure formed by connecting nodes in the network, and mainly includes the network nodes and edges connected between the network nodes; for the network with the hidden topology structure, a part of information, such as the number of network edges and the number of network nodes in the embodiment of the present invention, can still be read.
S102, simulating the process of spreading a plurality of pieces of information in the target network to obtain an information recording matrix and an information arrival matrix.
In step S102, the information recording matrix records data of each network node infected by one or more pieces of information, and the information arrival matrix records a time when each piece of information infects each network node.
In one embodiment, the process of simulating the propagation of multiple pieces of information may be implemented by a SIR algorithm.
In a specific application, there are multiple pieces of information that are simulated to be propagated in a network, and from the perspective of information, each piece of information passes through different network nodes, and the same information arrives at different network nodes at different times.
In terms of nodes, a node may be infected with only one message or a plurality of different messages.
In the embodiment of the invention, an information recording matrix starts from the angle of nodes and records which piece or pieces of information infected data of each node; the information arrival matrix starts from the information angle and records the time of each piece of information arriving to infect a certain network node.
The data of each network node infected by one or more pieces of information, the time of each network node infected by each piece of information are recorded in the information recording matrix and the information arrival matrix as matrix elements RiαAnd TiαWhen a network node is not infected by a certain message, RiαIs 0, R is the value ofiαHas a value of 1. In practical applications, the content recorded by the information recording matrix can be expressed as:
suppose that network node a is infected by information a, information b, and information c; the network node B is infected by the information a and the information B; the network node C is infected with the information b and the information C, and the information recording matrix may be represented as:
similarly, the content recorded by the information arrival matrix can be represented as:
the information arrival matrix records the time of the information a infecting the network node A and the network node B; the time when the information B infects the network node A, the network node B and the network node C; information C infects network node B and network node C times.
S103, selecting any two network nodes infected by the same information, and calculating the time difference of the two network nodes infected according to the information recording matrix and the information arrival matrix.
In step S103, the time difference between the two network nodes infected by the same message is represented by a vector, and the time difference vector contains all the differences, for example, the time difference between the arrival of the same message at the two nodes may be 1, 2, 3 … ….
And S104, selecting two network nodes with the time difference as a rated parameter as similar node pairs.
And S105, calculating the time similarity of the similar node pairs.
In the above steps S104 to S105, the time difference of two network nodes infected by the same information can reflect the structural relationship between the two network nodes to a certain extent, and the time difference is represented by the rated parameter, and at this time, by controlling the value of the rated parameter, the time measurement between the similar node pairs can be controlled, that is, two network nodes with smaller rated parameters are selected as the similar node pairs, so as to improve the accuracy of predicting the missing connection in the network topology.
In one embodiment, the nominal parameter is 1.
In a specific application, two nodes are infected in steps with a time difference of 1 in an independent propagation process, and the two nodes have a high probability of having a connecting edge, which indicates that the contribution value of the time difference of 1 to the reconstructed network is the largest.
In one embodiment, the formula for calculating the time similarity of the pair of similar nodes may be:
wherein, | tiα-tjαI is the time difference between two network nodes infected, R is the information recording matrix, T is the information arrival matrix, i and j represent nodes, α represents information representing a specific time difference, and TCN1 represents a one-step time similarity measure.
And S106, reconstructing the topological structure of the target network according to the time similarity and the network edge number.
In one embodiment, reconstructing the topology of the target network according to the time similarity and the number of network edges may include:
selecting N similar node pairs with larger numerical values in the time similarity as edge connecting node pairs, wherein N is the number of network edges;
and connecting the two network nodes of each edge connecting node pair to reconstruct the topological structure of the target network.
According to the network topology reconstruction method provided by the embodiment of the invention, the information recording matrix and the information arrival matrix are recorded at the same time by simulating the information propagation process in a target network, the arrival time of each piece of information in each network node is obtained, then the difference between the arrival times of the same information on any two network nodes is calculated, similar node pairs are selected according to the time difference, and the time similarity of the similar node pairs is calculated; the time similarity of all similar node pairs and the network edge number in the target network are combined, the reconstruction of the target network topological structure can be realized, the complete target network topological structure is obtained, meanwhile, the time measurement between the similar node pairs can be controlled by controlling the value of the rated parameter, namely, two network nodes with smaller rated parameters are selected as the similar node pairs, and therefore the accuracy of predicting the missing connection in the network topological structure is improved.
Example two
As shown in fig. 2, an embodiment of the present invention provides a network topology reconfiguration method, including:
s201, acquiring the number of network edges and the number of network nodes of the target network.
S202, setting the probability of the network node becoming an information source;
s203, calculating the number of the information of the simulated propagation according to the number of the network nodes and the probability.
S204, simulating the process of spreading a plurality of pieces of information in the target network to obtain an information recording matrix and an information arrival matrix.
S205, selecting two network nodes infected by the same information, and calculating the time difference of the two network nodes.
And S206, selecting two network nodes with the time difference as a rated parameter as similar node pairs.
And S207, calculating the time similarity of the similar node pairs.
S208, reconstructing the topological structure of the target network according to the time similarity and the network edge number.
The specific implementation of the steps S201, S204 to S208 is completely the same as the steps S101 to S106 in the network topology reconfiguration method provided in the embodiment, and no further description is given in the embodiment of the present invention.
In the above steps S202 to S203, the probability that the network node becomes the information source is used to indicate: each node has probability to become an information source and then starts to propagate; assuming that the probability of the network node becoming the information source is f, in the process of simulating the propagation of multiple pieces of information, N × f different pieces of information are propagated in the whole target network, where N is the number of network nodes.
In a specific application, the simulated propagation process may be performed by the SIR algorithm, which is performed several times, each time as an independent propagation process.
As shown in fig. 3, an embodiment of the present invention further provides an algorithm flow of a network topology reconfiguration method when a rated parameter is 1, where the algorithm flow includes:
1. and acquiring information of the target network, wherein the information comprises the number of network nodes and the number of network edges.
2. Before the propagation process of the simulation information, the simulation times are calculated according to the probability of the network nodes becoming the information source and the number of the network nodes, and the formula is as follows: n × f, i.e., there are N × f pieces of information in the propagation process.
3. Performing SIR algorithm to simulate information transmission process and recording transmissionA matrix R in which each node is infected with a certain information and a time matrix T when infected, R being a matrix with N rows and N × f columns, R iα1 indicates that inode is infected with the α message, 0 indicates no infection, and T is the same size as R, but the values in the matrix indicate the arrival time at infection.
4. And calculating a time difference vector of any network node pair according to the R matrix and the T matrix.
5. If the time difference is not equal to 1, discarding the time difference; if the time difference is equal to 1, the node is taken as a similar node pair, and the time similarity S of all similar node pairs is calculatedijAnd the node pairs with the time similarity ranked in the top E can be considered to have connecting edges, so that the target network topology is reconstructed.
EXAMPLE III
In the embodiment of the present invention, aiming at the network topology reconstruction methods provided in the first embodiment and the second embodiment, two artificially synthesized networks, i.e., a scale-free network model BA and a small world model SW, are used to test the network topology reconstruction methods provided in the first embodiment and the second embodiment, which exemplarily illustrates the beneficial effects of the methods in practical application.
Based on the network topology reconstruction method provided in the first and second embodiments, in the process of independent information transmission, a certain network node has a probability to infect its neighboring network nodes, assuming that the probability is β, and the value of β is closely related to the transmission process, for example, as the value of β decreases, the transmission process dies faster.
As shown in fig. 4, it includes 8 subgraphs (a) to (h), each referring to the network topology reconstruction method in the first and second embodiments, compared with the effect of the conventional link prediction method applied in the BA network.
In the embodiment of the present invention, the conventional link prediction method includes a reconstruction method using a static metric (e.g., CN), and a reconstruction method using a temporal similarity metric; in the reconstruction method based on the time similarity measurement, a multi-step time similarity measurement (such as TCN) is used; the network topology reconstruction method in the first and second embodiments is a reconstruction method based on a one-step time similarity metric (e.g., TCN 1).
In fig. 4, each of the subgraphs from subgraph (a) to subgraph (h) shows a result curve of a static metric (e.g., CN), a multi-step time similarity metric (e.g., TCN), and a one-step time similarity metric (e.g., TCN1) corresponding to the network topology reconstruction methods in the first and second embodiments.
In an embodiment of the invention, graph (a) includes the resulting curves for COS metric, TCOS1 metric in the BA network, graph (b) includes the resulting curves for SSI metric, TSSI1 metric in the BA network, graph (b) includes the resulting curves for SSI metric, TSSI1 metric in the BA network, and so on, and the metrics in graph (c) are HPI, THPI 1; the metrics in graph (d) are CN, TCN 1; the metrics in graph (e) are JAC, TJAC 1; the metrics in graph (f) are HDI, THDI 1; the measurements in panel (g) are PA, TPA 1; metrics in graph (h) are LHN, TLHN, LHN 1; wherein, COS (Cosine Index), SSI (Sorensen Index), HPI (HubPromoted Index), JAC (Jaccard Index), CN (common neighbors), HDI (Hub decompressed Index), PA (preferred attachment), LHN (leichth-Holme-Newman Index), and LHN (lightweight Newman Index) are all measurement methods, T represents a multi-step time similarity algorithm, TX1 represents a one-step time similarity algorithm, and the X-axis of each subgraph graph represents the probability β that a network node infects its neighbor during propagation, and the Y-axis is the Precision value of the selected Precision Index after reconstruction by the measurement method.
For example, the static metric may also be COS, where the corresponding multi-step temporal similarity metric is TCOS and the one-step temporal similarity metric is TCOS 1.
In the embodiment of the present invention, for each considered metric structure, if the probability β is large enough, the network topology reconstruction result corresponding to the one-step time similarity metric (e.g., TCN1) is significantly better than the network topology reconstruction result corresponding to the multi-step time similarity metric (e.g., TCN). As the probability β decreases, the propagation process tends to die faster and it becomes increasingly difficult to reconstruct the underlying diffusion network correctly. At smaller values of β, the one-step temporal similarity metric (e.g., TCN1) and the multi-step temporal similarity metric (e.g., TCN) metrics perform similarly, and thus the temporal perception metric is significantly better than the static metric.
As shown in sub-graphs (a) to (h) in fig. 5, each of the sub-graphs refers to the effect of the network topology reconstruction method in the first and second embodiments compared with the effect of the conventional link prediction method applied in the SW network. Each subgraph represents a class of measurement methods, and the specific measurement methods are the same as those in fig. 4 and comprise COS, SSI, HPI, JAC, CN, HDI, PA and LHN. And the X-axis of each subgraph represents the probability beta that a network node infects its neighbors in the propagation process, and the Y-axis is the Precision value of the Precision index selected after the reconstruction by the measurement method. Similar to the results applied in BA networks, one-step temporal similarity is better than multi-step temporal similarity, but the gap is smaller than in BA networks.
The results on the BA network and the SW network show that the one-step temporal similarity metric reconstructs the composite network better than the multi-step temporal similarity metric. For most data sets, the one-step time similarity measurement significantly improves reconstruction accuracy relative to static measurements and multi-step time similarity measurements. The only condition that a multi-step temporal similarity metric may be better than having a one-step temporal similarity metric is that the network has low cluster coefficients, which is very obvious: for highly clustered networks, it is unlikely that a long propagation path will reach two non-connected nodes.
As shown in fig. 6 and 7, in the embodiment of the present invention, β is made to be 4 βcTo analyze 20 real contact networks of different nature, wherein βcIs the prevalence threshold.
Fig. 6 is a schematic diagram comparing reconstruction effects of a network topology reconstruction method and a conventional link prediction method, as shown in subgraph (a) to subgraph (h) in fig. 6, each subgraph represents a type of measurement method, the specific measurement method is the same as that in fig. 4, including COS, SSI, HPI, JAC, CN, HDI, PA and LHN, and the X axis represents an aggregation coefficient of a network, and the Y axis is a relative value of a difference between a Precision evaluation index expressed by the network topology reconstruction method in the first embodiment and the second embodiment and a Precision expressed by the conventional method; the network topology reconstruction method based on the one-step time similarity measurement is compared with the traditional method based on the static measurement and the multi-step time similarity measurement; the points above the horizontal line in the figure indicate that the network topology reconstruction method based on the one-step time similarity measure is superior to the traditional link prediction method.
Fig. 7 is a schematic diagram comparing reconstruction effects of the network topology reconstruction method and the multi-step time similarity method, as shown in subgraph (a) to subgraph (h) in fig. 7, similar to fig. 6, each subgraph represents a type of measurement method, the specific measurement method is the same as that in fig. 4, including COS, SSI, HPI, JAC, CN, HDI, PA, and LHN, and the X axis represents an aggregation coefficient of the network, and the Y axis is a relative value of a difference between a Precision evaluation index expressed by the network topology reconstruction method in the first embodiment and the second embodiment and a Precision expressed by the conventional method. In most networks, the network topology reconstruction method based on the one-step time similarity measurement is still superior to the multi-step time similarity method.
In the present embodiment, the experiment was performed using a larger β value, for example, β ═ 8 βc。
As shown in fig. 8, the probability value is larger (β ═ 8 β)c) The following schematic diagram for comparing the reconstruction effect of the network topology reconstruction method with that of the time similarity method is shown, each of the subgraphs (a) to (h) in fig. 8 represents a type of measurement method, the specific measurement method is the same as that in fig. 4, and includes COS, SSI, HPI, JAC, CN, HDI, PA and LHN, the X axis represents the aggregation coefficient of the network, the Y axis is the relative value of the difference between the Precision evaluation index expressed by the network topology reconstruction method in the above first and second embodiments and the Precision of the Precision evaluation index expressed by the conventional methodcThe effect is better.
Practice four
As shown in fig. 9, an embodiment of the present invention provides a network topology reconstructing apparatus 90, which includes, but is not limited to, a network information obtaining module 91, a matrix recording module 92, a time difference calculating module 93, a similar node pair selecting module 94, a time similarity calculating module 95, and a network reconstructing module 96, where:
the network information obtaining module 91 is configured to obtain the number of network edges and the number of network nodes of the target network.
The matrix recording module 92 is used for simulating a process of spreading a plurality of pieces of information in the target network to obtain an information recording matrix and an information arrival matrix;
the information recording matrix records data of each network node infected by one or more pieces of information, and the information arrival matrix records the time of each piece of information infecting each network node.
And the time difference calculating module 93 is configured to select any two network nodes infected by the same information, and calculate the time difference between the two network nodes infected according to the information recording matrix and the information arrival matrix.
And a similar node pair selection module 94, configured to select two network nodes with a time difference as a rated parameter as a similar node pair.
In one embodiment, the nominal parameter is 1.
In a specific application, two nodes are infected in steps with a time difference of 1 in an independent propagation process, and the two nodes have a high probability of having a connecting edge, which indicates that the contribution value of the time difference of 1 to the reconstructed network is the largest.
A time similarity calculation module 95 for calculating the time similarity of the pair of similar nodes.
In one embodiment, in the time similarity calculation module 95, the time similarity formula for calculating the pairs of similar nodes may be:
wherein, | tiα-tjαIs two network nodesInfected time difference, R is an information recording matrix, T is an information recording matrix, i and j represent nodes, α represents information representing a specific time difference, and TCN1 represents a one-step time similarity measure.
And the network reconstruction module 96 reconstructs the topological structure of the target network according to the time similarity and the network edge number.
In one embodiment, the network reconfiguration module 96 may include:
and the edge connecting node selection unit is used for selecting N similar node pairs with larger numerical values in time similarity as edge connecting node pairs, wherein N is the number of network edges.
And the network node connecting unit is used for connecting the two network nodes of each edge connecting node pair and reconstructing the topological structure of the network.
In one embodiment, the network topology reconstruction apparatus may further include a probability setting module and a propagation calculation module; wherein:
the probability setting module is used for setting the probability of the network node becoming the information source;
and the propagation calculation module is used for calculating the number of the information simulating propagation according to the number and the probability of the network nodes.
The embodiment of the present invention further provides a terminal device, which includes a memory, a processor, and a computer program stored in the memory and capable of running on the processor, and when the processor executes the computer program, each step in the network topology reconfiguration method described in the first embodiment is implemented.
An embodiment of the present invention further provides a storage medium, where the storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium, and when the computer program is executed by a processor, the steps in the network topology reconstruction method according to the first embodiment are implemented.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the same; although the foregoing embodiments illustrate the present invention in detail, those of ordinary skill in the art will understand that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present invention, and are intended to be included within the scope of the present invention.
Claims (10)
1. A method for reconstructing a network topology, comprising:
acquiring the number of network edges and the number of network nodes of a target network;
simulating a process of spreading a plurality of pieces of information in the target network to obtain an information recording matrix and an information arrival matrix, wherein the information recording matrix records data of each network node infected by one or more pieces of information, and the information arrival matrix records the time of each network node infected by each piece of information;
selecting any two network nodes infected by the same information according to the information recording matrix and the information arrival matrix, and calculating the time difference of the two infected network nodes;
selecting two network nodes with the time difference as a rated parameter as a similar node pair;
calculating the time similarity of the similar node pairs;
and reconstructing the topological structure of the target network according to the time similarity and the network edge number.
2. The method of network topology reconfiguration according to claim 1, characterized in that said nominal parameter is 1.
3. The method of claim 1, wherein simulating an information propagation process in the target network to obtain an information recording matrix and an information arrival matrix comprises:
setting the probability of the network node becoming an information source;
and calculating the number of the information of the simulated propagation according to the number of the network nodes and the probability.
4. The method for reconstructing a network topology according to claim 1, wherein said calculating a time similarity formula of said pair of similar nodes is:
wherein, | tiα-tjαI is the time difference of two network nodes infected, R is an information recording matrix, T is the information recording matrix, i and j represent nodes, α represents information and represents rated time difference, and TCN1 represents a one-step time similarity measure.
5. The method of claim 1, wherein said reconstructing the topology of the target network based on the time similarities and the number of network edges comprises:
selecting N similar node pairs with larger numerical values in the time similarity as edge connecting node pairs, wherein N is the number of network edges;
and connecting the two network nodes of each edge connecting node pair to reconstruct the topological structure of the network.
6. A network topology reconfiguration device, comprising:
the network information acquisition module is used for acquiring the number of network edges and the number of network nodes of a target network;
the matrix recording module is used for simulating the process of spreading a plurality of pieces of information in the target network to obtain an information recording matrix and an information arrival matrix, wherein the information recording matrix records the data of each network node infected by one or more pieces of information, and the information arrival matrix records the time of each network node infected by each piece of information;
the time difference calculation module is used for selecting any two network nodes infected by the same information and calculating the time difference of the two network nodes infected according to the information recording matrix and the information arrival matrix;
the similar node pair selection module is used for selecting two network nodes with the time difference as a rated parameter as similar node pairs;
the time similarity calculation module is used for calculating the time similarity of the similar node pairs;
and the network reconstruction module reconstructs the topological structure of the target network according to the time similarity and the network edge number.
7. The network topology reconfiguration device according to claim 6, wherein said nominal parameter is 1.
8. The network topology reconfiguration device according to claim 6, further comprising a probability setting module and a propagation calculation module;
the probability setting module is used for setting the probability of the network node becoming an information source;
and the propagation calculation module is used for calculating the number of the information simulating propagation according to the number of the network nodes and the probability.
9. A terminal device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps of the network topology reconstruction method according to any one of claims 1 to 5 when executing the computer program.
10. A storage medium being a computer readable storage medium having a computer program stored thereon, wherein the computer program, when being executed by a processor, performs the steps of the network topology reconstruction method according to any of the claims 1 to 5.
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