A graph theory-based method and apparatus for analyzing the degree distribution of business data

Abstract

This invention discloses a method and apparatus for analyzing the degree distribution of business data based on graph theory. The method includes: acquiring business data to be analyzed and converting the business data into a network graph representation to obtain a business data network graph; calculating the degree of each node based on the business data network graph and drawing a business data degree distribution graph based on the degree of each node; obtaining network structure information based on the business data degree distribution graph and performing parameter fitting on the business data degree distribution graph based on the network structure information; correcting for missing edges on the fitted degree distribution graph to obtain the analysis results of the business data degree distribution; and then visualizing the analysis results of the business data degree distribution to complete the analysis of the business data degree distribution. This invention solves the technical problems of low accuracy and difficulty in analysis and visualization of the degree distribution of business data networks in the prior art.

Description

Technical Field

[0001] This invention relates to the field of graph theory data processing technology, and in particular to a method and apparatus for analyzing the degree distribution of business data based on graph theory. Background Technology

[0002] Degree distribution is a concept present in both graph theory and network theory. A graph is a binary tuple consisting of two sets and a set. The set generally consists of a finite number of elements, which are called vertices of the graph. The set is a finite set of elements, where each element is a non-negative integer. The mathematical framework of network theory is built on graph theory; the network in network theory is essentially the graph in graph theory.

[0003] Currently, in the process of analyzing and processing business data, graph theory is used to analyze its degree distribution. This degree distribution analysis aims to identify similarities or correlations between business data, thereby helping to uncover business problems. The relationship between the acquired data and the actual data is revealed through data analysis and mining. Currently, the method typically involves directly statistically analyzing the sum of connections between nodes in the graph theory. The degree of a node is usually defined as the sum of all connections it has. The degree distribution of a business data network is thus the probability or frequency distribution of the degrees of nodes in the network. However, this approach is not conducive to analyzing and displaying the characteristics of the degree distribution of business data, and it is difficult to verify and correct the statistical results of the degree of business data. This leads to low accuracy in the degree distribution analysis of business data networks, making it difficult to use in practical analytical applications.

[0004] Therefore, there is an urgent need for a method that can improve the accuracy of degree distribution analysis and the ease of display of business data. Summary of the Invention

[0005] This invention provides a graph theory-based method and apparatus for analyzing the degree distribution of business data, thereby solving the technical problems of low accuracy and difficulty in analysis and visualization of the degree distribution of business data networks in the prior art.

[0006] To address the aforementioned technical problems, embodiments of the present invention provide a graph theory-based method for analyzing the degree distribution of business data, including:

[0007] The business data to be analyzed is acquired and transformed into a network diagram representation, thereby obtaining the business data network diagram.

[0008] Based on the service data network diagram, calculate the degree of each node, and draw a service data degree distribution diagram based on the degree of each node;

[0009] Network structure information is obtained based on the service data degree distribution map, and parameters are fitted to the service data degree distribution map based on the network structure information.

[0010] The fitted degree distribution map is corrected for missing edges to obtain the analysis results of the business data degree distribution. The analysis results of the business data degree distribution are then visualized to complete the analysis of the business data degree distribution.

[0011] As a preferred embodiment, the business data includes social network data, internet routing relationship data, network behavior data, transaction behavior data, and trajectory synchronization data.

[0012] As a preferred embodiment, the step of calculating the degree of each node based on the service data network graph and drawing a service data degree distribution map based on the degree of each node is specifically as follows:

[0013] The degree of each node is calculated based on each node in the business data network graph and the sum of the edges connected to each node;

[0014] Based on the degree of each node, the degree value is used as the horizontal axis, and the corresponding number or proportion of nodes is used as the vertical axis to draw a degree distribution map of business data.

[0015] As a preferred embodiment, the step of obtaining network structure information based on the service data degree distribution map and performing parameter fitting on the service data degree distribution map based on the network structure information specifically involves:

[0016] The network structure information of the business data degree distribution map is obtained by analyzing the business data degree distribution map; the network structure information is one of power-law distribution, Poisson distribution or Gaussian distribution.

[0017] Based on the type of network structure information, the degree distribution parameters of the business data degree distribution map are fitted using the least squares fitting method, linear mixture model method, ridge regression method, singular value decomposition method, or maximum likelihood estimation method.

[0018] The fitted business data degree distribution map is validated by hypothesis testing, thereby completing the parameter fitting of the business data degree distribution map.

[0019] As a preferred embodiment, the step of correcting for missing edges in the fitted degree distribution map to obtain the analysis results of the degree distribution of business data is as follows:

[0020] The edges of each node in the business data network graph are verified based on the fitted degree distribution graph. The missing edges obtained from the verification are then used to correct the missing edges in the fitted degree distribution graph, thus obtaining the degree distribution graph after missing edge correction, which serves as the analysis result of the degree distribution of business data.

[0021] As a preferred embodiment, the visualization of the analysis results of the business data degree distribution is specifically as follows:

[0022] The analysis results of the degree distribution of the business data are visualized by using one or more of the following methods: histogram, network diagram, fitted curve, scatter plot, box plot or bar chart, so that the information of each node of the business data can be displayed.

[0023] Accordingly, the present invention also provides a business data degree distribution analysis device based on graph theory, comprising: an acquisition module, a drawing module, a fitting module, and a visualization module;

[0024] The acquisition module is used to acquire the business data to be analyzed and convert the business data into a network diagram representation to obtain a business data network diagram.

[0025] The drawing module is used to calculate the degree of each node based on the service data network graph, and draw a service data degree distribution graph based on the degree of each node.

[0026] The fitting module is used to obtain network structure information based on the service data degree distribution map, and to perform parameter fitting on the service data degree distribution map based on the network structure information.

[0027] The visualization module is used to correct missing edges on the fitted degree distribution map to obtain the analysis results of the business data degree distribution, and then visualize the analysis results of the business data degree distribution to complete the analysis of the business data degree distribution.

[0028] As a preferred embodiment, the business data includes social network data, internet routing relationship data, network behavior data, transaction behavior data, and trajectory synchronization data.

[0029] Accordingly, the present invention also provides a terminal device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor executes the computer program to implement the graph theory-based business data degree distribution analysis method as described above.

[0030] Accordingly, the present invention also provides a computer-readable storage medium comprising a stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to perform the graph theory-based business data degree distribution analysis method as described above.

[0031] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0032] The technical solution of this invention acquires and converts the business data to be analyzed into a network graph, thereby obtaining a business data network graph. After calculating the degree of each node, a business data degree distribution graph is drawn. Then, using the network structure information obtained from the business data degree distribution graph, parameter fitting is performed on the business data degree distribution graph to obtain the optimal distribution fitting result, thereby improving the accuracy of the degree distribution analysis of the business data network. The fitted degree distribution graph is then corrected for missing edges, thereby enabling the analysis of its degree distribution characteristics. It can also perform omission detection and correction. Finally, the analysis results of the business data degree distribution are visualized, allowing for the observation of its characteristics from different types of visualization results, thus deriving the characteristics of the degree distribution, and interpreting and analyzing the characteristics of the degree distribution for a more intuitive understanding of the degree distribution. Attached Figure Description

[0033] Figure 1 This is a flowchart illustrating the steps of a graph theory-based business data degree distribution analysis method provided in an embodiment of the present invention.

[0034] Figure 2 This is a structural diagram of a graph theory-based business data degree distribution analysis device provided in an embodiment of the present invention. Detailed Implementation

[0035] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0036] Example 1

[0037] Please refer to Figure 1 The present invention provides a graph theory-based method for analyzing the degree distribution of business data, comprising the following steps S101-S104:

[0038] Step S101: Obtain the business data to be analyzed and convert the business data into a network diagram representation to obtain the business data network diagram.

[0039] As a preferred embodiment, the business data includes social network data, Internet routing relationship data, network behavior data, transaction behavior data, and trajectory synchronization data.

[0040] It should be noted that the business data to be analyzed includes social network data, internet routing relationships, network behavior data, transaction behavior data, and trajectory peer data. This data covers multiple aspects such as social network relationships, network topology, user behavior, and transaction activities. The data transformation process mainly involves cleaning, filtering, and transforming the original transaction behavior data for subsequent analysis and mining. For example, when processing a transaction behavior data, key fields such as transaction time, transaction amount, and transaction type can be extracted from the original data, and data cleaning can be performed to remove outliers or erroneous data. Then, the transaction amount can be standardized and converted into a relative proportion or indicator for subsequent statistics and analysis. In network analysis, degree usually represents the degree of connectivity or correlation of nodes.

[0041] In this embodiment, degree can be used to represent the degree of connection between problems, that is, the similarity or relevance between different problems. By analyzing the degree between problems, it is possible to discover the connections and relevance between them, thereby classifying, clustering, or recommending related problems. The relationship between problems and the acquired business data is revealed through data analysis and mining. By processing and analyzing business data, the association and relevance between problems and data can be discovered. For example, in network behavior data, by analyzing users' click behavior and conversion behavior, the user's level of interest or tendency towards different problems can be inferred, and relevant problems or suggestions can be provided to the user based on this. This embodiment of the invention analyzes the degree distribution, thereby obtaining clear and accurate results of the degree distribution analysis, so that administrators can obtain the corresponding business data information, thereby making corresponding problem solutions or suggestions, optimizing the administrator's experience, reducing the administrator's related working time and costs, and improving work convenience.

[0042] It should be noted that trajectory traveler data is an important type of user data in big data mining. It is data obtained by sampling the movement process of one or more mobile users, including sampling point location, sampling time, speed, etc.

[0043] Step S102: Calculate the degree of each node based on the service data network diagram, and draw a service data degree distribution diagram based on the degree of each node.

[0044] In a preferred embodiment, the step of calculating the degree of each node based on the service data network graph and drawing a service data degree distribution graph based on the degree of each node specifically involves:

[0045] Based on each node in the business data network graph and the sum of the edges connected to each node, the degree of each node is calculated; based on the degree of each node, the degree value is used as the horizontal axis and the corresponding number or proportion of nodes is used as the vertical axis, thereby drawing a business data degree distribution map.

[0046] In this embodiment, the degree of a node is usually defined as the sum of all connections that the node is connected to. The formula for calculating the degree of a node is used to facilitate the calculation of the degree of a node. The degree distribution of the network is the probability distribution or frequency distribution of the degree of the nodes in the network. The degree of a node is usually defined as the sum of all connections that the node is connected to.

[0047] Step S103: Obtain network structure information based on the service data degree distribution map, and perform parameter fitting on the service data degree distribution map based on the network structure information.

[0048] In a preferred embodiment, the step of obtaining network structure information based on the service data degree distribution map and performing parameter fitting on the service data degree distribution map based on the network structure information specifically involves:

[0049] The business data degree distribution map is analyzed to obtain its network structure information. This network structure information is one of a power-law distribution, a Poisson distribution, or a Gaussian distribution. Based on the type of network structure information, the degree distribution parameters of the business data degree distribution map are fitted using the least squares fitting method, the linear mixture model method, the ridge regression method, the singular value decomposition method, or the maximum likelihood estimation method. The fitted business data degree distribution map is then validated using hypothesis testing, thus completing the parameter fitting of the business data degree distribution map.

[0050] In this embodiment, the network structure information includes, but is not limited to, one or more of the following: power-law distribution, Poisson distribution, and Gaussian distribution. A power-law distribution, in mathematical statistics, refers to a probability distribution where the probability distribution of a random variable has a power-law form. A characteristic of a power-law distribution is that the probability density function decreases with the power of the variable. In a power-law distribution, some events have low frequencies, while others have high frequencies; this distribution exhibits a "long tail." A Poisson distribution is a probability distribution used to describe the probability distribution of the number of events occurring within a certain time and space range. The Poisson distribution is usually represented by l ambda(λ), where l ambda represents the average event occurrence rate per unit time and unit space. In a Poisson distribution, events occur independently and sparsely, and the average rate of event occurrence is constant. The Gaussian distribution, also known as the normal distribution, is a continuous probability distribution. It is widely used in nature and social sciences. Its characteristic is that it has a symmetrical bell-shaped curve. In the Gaussian distribution, the mean and standard deviation are two important parameters describing the shape of the distribution. The characteristics of a network can be judged by observing the shape of the distribution. Network characteristics include, but are not limited to, the presence of core nodes, the pattern of core nodes, and one or more of the distribution characteristics of the network.

[0051] In this embodiment, the parameter estimation methods for degree distribution include, but are not limited to, one or more of the following: least squares fitting, linear mixture model, ridge regression, singular value decomposition, and maximum likelihood estimation. Least squares fitting is a method for estimating model parameters by minimizing the sum of squared residuals. It assumes that the observed data follows a linear model and minimizes the difference between the observed values ​​and the model's predicted values ​​as much as possible. Least squares fitting is commonly used to fit linear regression models. Linear mixture model is a parameter estimation method for handling data with random effects. It combines fixed and random effects and estimates parameters by minimizing the difference between the observed data and the model's predicted values. Ridge regression is a regularized linear regression method that introduces L2 regularization into the objective function of least squares fitting. The parameter estimation term controls the magnitude of parameter estimation. It can address multicollinearity problems and, in some cases, provide more stable parameter estimates. Singular value decomposition (SVD) is a matrix factorization-based method used to solve least squares fitting problems. It finds the optimal parameter estimate solution by decomposing a matrix into the product of three matrices. Maximum likelihood estimation (MLE) is a method for estimating model parameters by maximizing the likelihood function of observed data. It assumes that the observed data follows a certain probability distribution and seeks model parameters that maximize the probability of the observed data. MLE is widely used in statistical inference and can be used to estimate parameters, test hypotheses, and construct confidence intervals to obtain the best distribution fit results. Hypothesis testing methods can be used to verify the significance of the fitting results, thereby improving the accuracy of distribution analysis.

[0052] Step S104: Correct missing edges on the fitted degree distribution map to obtain the analysis results of the business data degree distribution, and then visualize the analysis results of the business data degree distribution to complete the analysis of the business data degree distribution.

[0053] As a preferred embodiment, the step of correcting for missing edges in the fitted degree distribution map to obtain the analysis results of the degree distribution of business data specifically involves:

[0054] The edges of each node in the business data network graph are verified based on the fitted degree distribution graph. The missing edges obtained from the verification are then used to correct the missing edges in the fitted degree distribution graph, thus obtaining the degree distribution graph after missing edge correction, which serves as the analysis result of the degree distribution of business data.

[0055] In this embodiment, the missing edge correction infers unobserved edges based on existing degree distribution information, making the degree distribution more accurate. In some business data network graphs, there is a problem that some edges are not observed due to sampling errors or other reasons. The accuracy of the degree distribution is improved by the missing edge correction.

[0056] As a preferred embodiment, the visualization of the analysis results of the business data degree distribution is specifically as follows:

[0057] The analysis results of the degree distribution of the business data are visualized by using one or more of the following methods: histogram, network diagram, fitted curve, scatter plot, box plot or bar chart, so that the information of each node of the business data can be displayed.

[0058] In this embodiment, visualization involves presenting the degree distribution and related analysis results visually. Visualization methods include, but are not limited to, one or more of the following: histograms, network diagrams, fitted curves, scatter plots, box plots, and bar charts. Histograms divide the degree range into different intervals; the horizontal axis represents the degree range, and the vertical axis represents the number or frequency of nodes within that range. Histograms provide a clear visual representation of the degree distribution and allow for preliminary observation and analysis of the data. Scatter plots represent the degree of nodes on the horizontal axis and other node attributes on the vertical axis. Scatter plots can display the relationship between degree and other attributes, as well as the distribution of different nodes within this relationship. Graphs represent nodes with points and edges with lines. They can represent the degree of different nodes in a network with different shapes, sizes, or colors, making the degree distribution of nodes more intuitive. Force-directed algorithms can be used to optimize the layout of the network graph, making the connections between nodes clearer. Box plots can display five key statistics of degree distribution: minimum, maximum, upper quartile, median, and lower quartile. Box plots can intuitively show the degree dispersion and the existence of outliers. Histograms can compare the number of nodes with different degrees. By setting different colors or shading to represent different degree values, the distribution of nodes with degree values ​​in different ranges can be quickly observed.

[0059] It should be noted that the interpretation results are reasonable interpretations and inferences made about the conclusions obtained from the analysis. Choosing to present the data through visualization can help to better understand and interpret the data. Therefore, various data visualization tools and programming languages ​​can be used to implement visualization. Depending on different needs and data characteristics, the appropriate visualization method can be selected to display the degree distribution and related analysis results, which can enable administrators to better understand and interpret the data.

[0060] It should be noted that the results of degree distribution analysis can be applied to business data analysis in social networks, internet routing relationships, network behavior analysis, and transaction behavior analysis. By analyzing the degree distribution of nodes in social networks, the importance and influence of nodes can be revealed. In social network marketing, the degree centrality of nodes can be used to identify important social media users or key nodes for targeted promotion and marketing strategies. By analyzing the degree distribution of nodes in internet routing relationships, the importance and stability of network nodes can be assessed. In network optimization and fault diagnosis, the degree information of nodes can be used to identify key nodes in the network for routing optimization or fault diagnosis. By analyzing the degree distribution of nodes in network behavior data, inferences can be made... User activity levels and connection status can be analyzed in user behavior and personalized recommendations by leveraging node degree information to identify active and relevant users for precise personalized recommendations or advertising. Analyzing the degree distribution of nodes in transaction behavior data can assess transaction risk and trust levels. In the financial sector or e-commerce, node degree information can be used to identify high-risk or abnormal transaction patterns for risk control measures or fraud detection. Furthermore, degree distribution analysis can be applied to various business data scenarios, including social networks, internet routing, network behavior, and transaction behavior, helping to reveal the importance, influence, and connectivity of each node in the network graph formed by business data, as well as assessing risk, optimizing routing, and providing personalized services.

[0061] Implementing the above embodiments has the following effects:

[0062] The technical solution of this invention acquires and converts the business data to be analyzed into a network graph, thereby obtaining a business data network graph. After calculating the degree of each node, a business data degree distribution graph is drawn. Then, using the network structure information obtained from the business data degree distribution graph, parameter fitting is performed on the business data degree distribution graph to obtain the optimal distribution fitting result, thereby improving the accuracy of the degree distribution analysis of the business data network. The fitted degree distribution graph is then corrected for missing edges, thereby enabling the analysis of its degree distribution characteristics. It can also perform omission detection and correction. Finally, the analysis results of the business data degree distribution are visualized, allowing for the observation of its characteristics from different types of visualization results, thus deriving the characteristics of the degree distribution, and interpreting and analyzing the characteristics of the degree distribution for a more intuitive understanding of the degree distribution.

[0063] Example 2

[0064] Please see Figure 2 The present invention provides a business data degree distribution analysis device based on graph theory, comprising: an acquisition module 201, a drawing module 202, a fitting module 203, and a visualization module 204.

[0065] The acquisition module 201 is used to acquire the business data to be analyzed and convert the business data into a network diagram representation to obtain a business data network diagram.

[0066] The drawing module 202 is used to calculate the degree of each node based on the service data network graph, and draw a service data degree distribution graph based on the degree of each node.

[0067] The fitting module 203 is used to obtain network structure information based on the service data degree distribution map, and to perform parameter fitting on the service data degree distribution map based on the network structure information.

[0068] The visualization module 204 is used to correct missing edges on the fitted degree distribution map to obtain the analysis results of the business data degree distribution, and then visualize the analysis results of the business data degree distribution to complete the analysis of the business data degree distribution.

[0069] As a preferred embodiment, the business data includes social network data, internet routing relationship data, network behavior data, transaction behavior data, and trajectory synchronization data.

[0070] As a preferred embodiment, the step of calculating the degree of each node based on the service data network graph and drawing a service data degree distribution map based on the degree of each node is specifically as follows:

[0071] The degree of each node is calculated based on each node in the business data network graph and the sum of the edges connected to each node;

[0072] Based on the degree of each node, the degree value is used as the horizontal axis, and the corresponding number or proportion of nodes is used as the vertical axis to draw a degree distribution map of business data.

[0073] As a preferred embodiment, the step of obtaining network structure information based on the service data degree distribution map and performing parameter fitting on the service data degree distribution map based on the network structure information specifically involves:

[0074] The network structure information of the business data degree distribution map is obtained by analyzing the business data degree distribution map; the network structure information is one of power-law distribution, Poisson distribution or Gaussian distribution.

[0075] Based on the type of network structure information, the degree distribution parameters of the business data degree distribution map are fitted using the least squares fitting method, linear mixture model method, ridge regression method, singular value decomposition method, or maximum likelihood estimation method.

[0076] The fitted business data degree distribution map is validated by hypothesis testing, thereby completing the parameter fitting of the business data degree distribution map.

[0077] As a preferred embodiment, the step of correcting for missing edges in the fitted degree distribution map to obtain the analysis results of the degree distribution of business data is as follows:

[0078] The edges of each node in the business data network graph are verified based on the fitted degree distribution graph. The missing edges obtained from the verification are then used to correct the missing edges in the fitted degree distribution graph, thus obtaining the degree distribution graph after missing edge correction, which serves as the analysis result of the degree distribution of business data.

[0079] As a preferred embodiment, the visualization of the analysis results of the business data degree distribution is specifically as follows:

[0080] The analysis results of the degree distribution of the business data are visualized by using one or more of the following methods: histogram, network diagram, fitted curve, scatter plot, box plot or bar chart, so that the information of each node of the business data can be displayed.

[0081] Accordingly, the present invention also provides a business data degree distribution analysis device based on graph theory, comprising: an acquisition module, a drawing module, a fitting module, and a visualization module;

[0082] The acquisition module is used to acquire the business data to be analyzed and convert the business data into a network diagram representation to obtain a business data network diagram.

[0083] The drawing module is used to calculate the degree of each node based on the service data network graph, and draw a service data degree distribution graph based on the degree of each node.

[0084] The fitting module is used to obtain network structure information based on the service data degree distribution map, and to perform parameter fitting on the service data degree distribution map based on the network structure information.

[0085] The visualization module is used to correct missing edges on the fitted degree distribution map to obtain the analysis results of the business data degree distribution, and then visualize the analysis results of the business data degree distribution to complete the analysis of the business data degree distribution.

[0086] As a preferred embodiment, the business data includes social network data, internet routing relationship data, network behavior data, transaction behavior data, and trajectory synchronization data.

[0087] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working process of the device described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0088] Implementing the above embodiments has the following effects:

[0089] The technical solution of this invention acquires and converts the business data to be analyzed into a network graph, thereby obtaining a business data network graph. After calculating the degree of each node, a business data degree distribution graph is drawn. Then, using the network structure information obtained from the business data degree distribution graph, parameter fitting is performed on the business data degree distribution graph to obtain the optimal distribution fitting result, thereby improving the accuracy of the degree distribution analysis of the business data network. The fitted degree distribution graph is then corrected for missing edges, thereby enabling the analysis of its degree distribution characteristics. It can also perform omission detection and correction. Finally, the analysis results of the business data degree distribution are visualized, allowing for the observation of its characteristics from different types of visualization results, thus deriving the characteristics of the degree distribution, and interpreting and analyzing the characteristics of the degree distribution for a more intuitive understanding of the degree distribution.

[0090] Example 3

[0091] Accordingly, the present invention also provides a terminal device, comprising: a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor executes the computer program to implement the graph theory-based business data degree distribution analysis method as described in any of the above embodiments.

[0092] The terminal device in this embodiment includes a processor, a memory, and a computer program and computer instructions stored in the memory and executable on the processor. When the processor executes the computer program, it implements the various steps described in Embodiment 1 above, for example... Figure 1 The steps S101 to S104 are shown. Alternatively, when the processor executes the computer program, it implements the functions of each module / unit in the above-described device embodiment, such as the fitting module 203.

[0093] For example, the computer program can be divided into one or more modules / units, which are stored in the memory and executed by the processor to complete the present invention. The one or more modules / units can be a series of computer program instruction segments capable of performing specific functions, which describe the execution process of the computer program in the terminal device. For example, the fitting module 203 is used to obtain network structure information based on the service data degree distribution map, and to perform parameter fitting on the service data degree distribution map based on the network structure information.

[0094] The terminal device may be a desktop computer, laptop, handheld computer, or cloud server, etc. The terminal device may include, but is not limited to, a processor and memory. Those skilled in the art will understand that the schematic diagram is merely an example of a terminal device and does not constitute a limitation on the terminal device. It may include more or fewer components than illustrated, or combine certain components, or different components. For example, the terminal device may also include input / output devices, network access devices, buses, etc.

[0095] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the terminal device, connecting all parts of the terminal device via various interfaces and lines.

[0096] The memory can be used to store the computer programs and / or modules. The processor implements various functions of the terminal device by running or executing the computer programs and / or modules stored in the memory and by calling data stored in the memory. The memory may mainly include a program storage area and a data storage area. The program storage area may store the operating system, at least one application program required for a function, etc.; the data storage area may store data created based on the use of the mobile terminal, etc. In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as hard disk, RAM, plug-in hard disk, smart media card (SMC), secure digital card (SD), flash card, at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0097] Wherein, if the modules / units integrated in the terminal device are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when the computer program is executed by a processor, it can implement the steps of the various method embodiments described above. Wherein, the computer program includes computer program code, which can be in the form of source code, object code, executable file, or some intermediate form, etc. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, portable hard drive, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content contained in the computer-readable medium can be appropriately added or removed according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium does not include electrical carrier signals and telecommunication signals.

[0098] Example 4

[0099] Accordingly, the present invention also provides a computer-readable storage medium comprising a stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to perform the graph theory-based business data degree distribution analysis method as described in any of the above embodiments.

[0100] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. In particular, it should be noted that any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention for those skilled in the art.

Claims

1. A method for analyzing the degree distribution of business data based on graph theory, characterized in that, include: The business data to be analyzed is acquired and transformed into a network diagram representation, thereby obtaining the business data network diagram. Based on the business data network diagram, calculate the degree of each node, and draw a business data degree distribution diagram based on the degree of each node. Network structure information is obtained based on the service data degree distribution map, and parameters are fitted to the service data degree distribution map based on the network structure information. The fitted degree distribution map is corrected for missing edges to obtain the analysis results of the business data degree distribution. The analysis results of the business data degree distribution are then visualized to complete the analysis of the business data degree distribution.

2. The graph theory-based business data degree distribution analysis method as described in claim 1, characterized in that, The business data includes social network data, internet routing relationship data, network behavior data, transaction behavior data, and trajectory synchronization data.

3. The graph theory-based business data degree distribution analysis method as described in claim 1, characterized in that, The process involves calculating the degree of each node based on the service data network graph, and then drawing a service data degree distribution map based on the degree of each node. Specifically: The degree of each node is calculated based on each node in the business data network graph and the sum of the edges connected to each node; Based on the degree of each node, the degree value is used as the horizontal axis, and the corresponding number or proportion of nodes is used as the vertical axis to draw a degree distribution map of business data.

4. The graph theory-based business data degree distribution analysis method as described in claim 3, characterized in that, The process of obtaining network structure information based on the service data degree distribution map and then fitting parameters to the service data degree distribution map based on the network structure information specifically involves: The network structure information of the business data degree distribution map is obtained by analyzing the business data degree distribution map; the network structure information is one of power-law distribution, Poisson distribution or Gaussian distribution. Based on the type of network structure information, the degree distribution parameters of the business data degree distribution map are fitted using the least squares fitting method, linear mixture model method, ridge regression method, singular value decomposition method, or maximum likelihood estimation method. The fitted business data degree distribution map is validated by hypothesis testing, thereby completing the parameter fitting of the business data degree distribution map.

5. The graph theory-based business data degree distribution analysis method as described in claim 4, characterized in that, The process of correcting for missing edges in the fitted degree distribution map to obtain the analysis results of the degree distribution of business data is as follows: The edges of each node in the business data network graph are verified based on the fitted degree distribution graph. The missing edges obtained from the verification are then used to correct the missing edges in the fitted degree distribution graph, thus obtaining the degree distribution graph after missing edge correction, which serves as the analysis result of the degree distribution of business data.

6. The graph theory-based business data degree distribution analysis method as described in claim 5, characterized in that, The visualization of the analysis results of the degree distribution of the business data is specifically as follows: The analysis results of the degree distribution of the business data are visualized by using one or more of the following methods: histogram, network diagram, fitted curve, scatter plot, box plot or bar chart, so that the information of each node of the business data can be displayed.

7. A business data degree distribution analysis device based on graph theory, characterized in that, include: The module includes an acquisition module, a plotting module, a fitting module, and a visualization module. The acquisition module is used to acquire the business data to be analyzed and convert the business data into a network diagram representation to obtain a business data network diagram. The drawing module is used to calculate the degree of each node based on the business data network graph, and draw a business data degree distribution graph based on the degree of each node. The fitting module is used to obtain network structure information based on the service data degree distribution map, and to perform parameter fitting on the service data degree distribution map based on the network structure information. The visualization module is used to correct missing edges on the fitted degree distribution map to obtain the analysis results of the business data degree distribution, and then visualize the analysis results of the business data degree distribution to complete the analysis of the business data degree distribution.

8. The graph theory-based business data degree distribution analysis device as described in claim 7, characterized in that, The business data includes social network data, internet routing relationship data, network behavior data, transaction behavior data, and trajectory synchronization data.

9. A terminal device, characterized in that, The method includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor, when executing the computer program, implements the graph theory-based business data degree distribution analysis method as described in any one of claims 1 to 6.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to perform the graph theory-based business data degree distribution analysis method as described in any one of claims 1 to 6.

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