CN115271987A - Cross-application group relation analysis method based on mobile phone data - Google Patents
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Abstract
The invention relates to the technical field of new generation information, and discloses a cross-application group relation analysis method based on mobile phone data, which comprises the following steps: step 1, screening and mining mobile phone application data and application groups: confirming the information of the person to which the mobile phone belongs and confirming the associated application under the condition of collecting the target information; step 2, constructing a character information collection: the personal information of the mobile phone and the associated application form a personal information aggregate; step 3, acquiring a standard information table in the total character information set; step 4, constructing an assimilation feature set of the associated application based on the standard information table in the step 3; step 5, establishing a dissimilarity characteristic set of the associated application based on the standard information table in the step 3; step 6, depicting a group association structure diagram; the method and the device can be used for rapidly determining the association relation and analyzing the characteristics of group characters according to the assimilation and dissimilarity of cross-application social groups in the mobile phone data in a complex social network environment.
Description
Technical Field
The invention relates to the technical field of new-generation information, in particular to a cross-application group relation analysis method based on mobile phone data.
Background
The analysis of the relationship among social people and the character characteristics of the existing relationship has important significance for mastering the group relationship and the group characteristics. In recent years, there have been patents disclosed relating to the characterization of relationships between characters from a single application or relationships between multiple networks. The invention discloses a microblog-social-based character relationship visualization method with the publication number of CN111506824A, and is named as a microblog-social-based character relationship visualization method. The invention has the publication number of CN201910323380.5 and is named as a cross-network character association method based on a social network knowledge graph, obtains attribute information and relationship information of a user in other social networks by associating the same user in different social networks, and analyzes the obtained information by using the social network knowledge graph so as to confirm character relationships.
However, with the rapid development of information technology, mobile phones have been further confirmed as mainstream communication tools. The social habits of people are also changed greatly, the number of social applications used in social contact is more and more, and the differentiation between the social applications is more and more obvious, so that in the original achievement, the relationship and the characteristics of people cannot be completely and accurately described under the condition of social differentiation characteristics based on social analysis of single application. Based on the cross-network social network knowledge graph analysis, although the difference between different social networks is considered, the relationship analysis of the same user between different networks is highlighted, and the relationship and the group characteristics of the cross-application group users are not mentioned.
Therefore, the character relationship and the characteristics among the groups are described based on the cross-application data of the group mobile phone data, and not only is the graph relationship in the social character group processed, but also the structure information of the graph is described. Firstly, the complexity of the social relationship determines that the roles of the characters in different mobile phone applications are different, so that the same characteristic analysis is required to be carried out on the characters based on the mobile phone application characteristics, and a group graph relationship can be formed; second, anisotropic analysis, as opposed to identity, can characterize the differences between different applications of a character, thereby forming the structural relationship of the graph.
Disclosure of Invention
Aiming at the defects in the prior art, the invention aims to provide a cross-application group relation analysis method based on mobile phone data.
In order to realize the purpose, the invention provides the following technical scheme: a cross-application group relation analysis method based on mobile phone data comprises the following steps:
step 1, screening and mining mobile phone application data and application groups, confirming the figure information of a mobile phone under the condition of collecting target information, and confirming associated application;
step 2, constructing a character information collection: the personal information and the associated application of the mobile phone form a personal information aggregate;
step 3, acquiring a standard information table in the total figure information set;
step 4, constructing an assimilation feature set of the associated application based on the standard information table in the step 3;
step 5, establishing a dissimilarity characteristic set of the associated application based on the standard information table in the step 3;
and 6, depicting a group association structure chart.
The step 1 of screening and mining the mobile phone application data and the application groups comprises the following steps:
s1, establishing an application data table with an association relation: acquiring a mobile phone common social application list based on a mobile phone application store;
s2, collecting target information, and drawing an APP social attribute three-dimensional space coordinate system through three information dimensions preset in the target information;
s3, putting all applications in the mobile phone common social application list into an APP social attribute three-dimensional space coordinate system;
s4, carrying out standardization processing on the putting result in the step S3, and confirming all applications belonging to the first quadrant as related applications;
and S5, determining the information of the person to which the mobile phone belongs based on the targeting information.
The step 2 of forming a personal information aggregate by the personal information to which the mobile phone belongs and the associated application comprises the following steps:
step U1, processing the target information in the associated application to form a first character information set;
step U2, processing the targeted information in the personal information of the mobile phone to form personal information diversity II;
and U3, summarizing the person information diversity I and the person information diversity II to form a person information summary set.
The step 3 of obtaining the standard information table in the total set of the personal information comprises the following steps:
v1, based on the target information, extracting influence characteristic factors and dimensionality coefficients from the character information of the mobile phone in the character information total set, and obtaining user weight values corresponding to the influence characteristic factors;
and V2, the standard information table is composed of influence characteristic factors, dimension coefficients and user weight values.
The step 4 of constructing an assimilation feature set of the associated application comprises the following steps:
w1, acquiring factors with isotropy in the factors influencing the characteristics based on the standard information table in the step 3;
step W2, calculating the homodromous strength of different homodromous dimensions of each character by analyzing the homodromous factors;
and W3, substituting the standard information table and the equidirectional strength into an equidirectional formula, calculating to obtain equidirectional characteristic values, and summarizing the equidirectional characteristic values to obtain an equidirectional characteristic set.
The step 5 of constructing the dissimilarity feature set of the associated application comprises the following steps:
step Z1, acquiring factors with anisotropy in the factors influencing the characteristics based on the standard information table in the step 3;
step Z2, calculating the dissimilarity strength of different anisotropic dimensions of each figure by analyzing anisotropic factors;
and step Z3, substituting the standard information table and the dissimilarity strength into a dissimilarity formula, calculating to obtain dissimilarity characteristic values, and summarizing the dissimilarity characteristic values to obtain a dissimilarity characteristic set.
The step 6 of depicting the group association structure diagram comprises the following steps:
step T1, aiming at a character information total set, an assimilation feature set and a dissimilarity feature set, respectively drawing images of the structural relationship of characters of different groups to obtain a group association structure chart;
and T2, archiving and outputting the group association structure chart.
Compared with the prior art, the invention has the following beneficial effects:
1. the invention utilizes assimilation and dissimilarity analysis to form an assimilation characteristic set and a dissimilarity characteristic set, thereby depicting group characteristics and clearly feeding back application group characteristics.
2. The method can be used for mining group relationships and knowing group composition in a social network, can reasonably deduce target groups and demand groups of each application software based on the character information of different mobile phones, can be used for regional difference analysis and industrial difference analysis, can depict group characteristics, and is applied to the fields of advertisement promotion, important group analysis and the like.
3. The method and the device can be used for rapidly determining the association relation and analyzing the characteristics of group characters according to the assimilation and dissimilarity of cross-application social groups in the mobile phone data in a complex social network environment.
Drawings
Fig. 1 is a flowchart of a method for analyzing a cross-application group relationship based on mobile phone data according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, a cross-application group relationship analysis method based on mobile phone data includes the following steps:
step 1, screening and mining mobile phone application data and application groups, confirming character information of a mobile phone under the condition of collecting target information, and confirming associated application:
s1, establishing an application data table with an association relation: acquiring a mobile phone common social application list based on a mobile phone application store;
s2, collecting target information, and drawing an APP social attribute three-dimensional space coordinate system through three information dimensions preset in the target information;
s3, putting all applications in the mobile phone common social application list into an APP social attribute three-dimensional space coordinate system;
s4, carrying out standardization processing on the putting result in the step S3, and confirming all applications belonging to the first quadrant as related applications;
and S5, determining the personal information of the mobile phone based on the target information.
Determining the personal information to which the mobile phone belongs refers to determining basic personal information such as the age, the working region (in provinces), the industry and the like of the personal to which the mobile phone belongs, wherein the target information is actually distinguished into personal target information and application target information, the personal target information comprises the content, and the application target information comprises all attributes of the application, including but not limited to the following:
real name system and anonymous system.
High visibility (all people are visible), such as micro blogs, people, strange. According to the social media with the weak social relationship, a user generally cannot publish privacy messages, hot recommendations are provided by microblogs and people, and unfamiliar people are viewed based on geographical positions and are reflected in high visibility;
medium visibility (access visible/friend visible), such as personal homepage/QQ space. Such products offer different visibility options that the user can set by himself;
low visibility (common friends visible), such as WeChat circle of friends. WeChat friend circles are more closed than QQ spaces, in the QQ spaces, all people can see comments of friends of themselves, and in WeChat, only comments of common friends can be seen.
Friend relationship hierarchy:
strangers-single concern-mutual concern;
strangers-two people without any relationship;
single attention-one party pays attention to the other;
mutual attention-both parties pay attention to each other;
private concerns-providing a non-public concern function;
the friend and the two parties are friends with each other, and the friend relationship is released by any party, so that the friend relationship is ended.
Core functionality: the functions of address book, dialogue, group chat, message publishing, message browsing, comment, private letter and the like.
Step 2, collecting target information, and drawing an APP social attribute three-dimensional space coordinate system through three information dimensions preset in the target information, wherein the three information dimensions can be application attributes set arbitrarily, and if the following three information are selected: if the real name system is adopted, chatting can be performed if the real name system is concerned with each other, and address book friends are acquired, if the real name system is in the X-axis positive direction in a three-dimensional space coordinate system, if the real name system is in the X-axis negative direction, chatting can be performed if the real name system is concerned with each other, in the Y-axis positive direction, and address book friends are acquired in the Z-axis positive direction, standardization processing is performed on the putting result in the step S3, wherein the standardization processing refers to placing according to a set quadrant standard, and finally all applications in a first quadrant are confirmed to be related applications.
Step 2, constructing a character information collection: and (3) forming a person information aggregate by the person information to which the mobile phone belongs and the associated application:
step U1, processing the target information in the associated application to form a first personal information set, and collecting and recording all attributes of the application in the associated application to form a first personal information set;
step U2, processing the targeted information in the personal information of the mobile phone to form a second personal information diversity, and recording basic personal information such as age, work region (in province), industry and the like in the personal information of the mobile phone to form the second personal information diversity;
and step U3, summarizing the person information diversity I and the person information diversity II to form a person information total set.
Step 3, obtaining a standard information table in the total set of the figure information:
v1, based on the target information, extracting influence characteristic factors and dimensionality coefficients from the character information of the mobile phone in the character information total set, and obtaining user weight values corresponding to the influence characteristic factors;
and V2, the standard information table is composed of influence characteristic factors, dimension coefficients and user weight values.
The standard information table is as follows:
| influencing characteristic factors | a1 | ... | an |
| Coefficient of dimension | b1 | ... | bn |
| User weight value | c1 | ... | cn |
n is not 0, a1 represents a first influence characteristic factor, an represents the first influence characteristic factor, b1 represents a preset dimensional coefficient corresponding to a1, c1 represents a weight value of a mobile phone user with a bias corresponding to a1, and b1+ b2+ b3+. + bn =1, such as a1 represents a real-name attribute in the targeting information of the application attribute, and c1 represents a ratio of the usage time of all applications with the real-name attribute to the total usage time of the mobile phone user in a unit time.
And 4, constructing an assimilation feature set of the associated application based on the standard information table in the step 3:
w1, acquiring factors with isotropy in the factors influencing the characteristics based on the standard information table in the step 3;
step W2, calculating the homodromous strength of different homodromous dimensions of each character by analyzing the homodromous factors;
and step W3, substituting the standard information table and the equidirectional strength into an equidirectional formula, calculating to obtain equidirectional characteristic values, and summarizing the equidirectional characteristic values to obtain an equidirectional characteristic set.
The attributes of the two applications only occupy partial attributes, namely partial influence characteristic factors, in the standard information table, the influence characteristic factors occupied by the two applications are extracted as factors with the same direction in the influence characteristic factors, the same-direction strength TXQ is obtained by summing the dimensional coefficient values of the influence characteristic factors occupied by the two applications, the same-direction formula is TXG = TXQ TXT, TXT is the sum of the difference values of user weight values of the influence characteristic factors occupied by the two applications under the same influence characteristic factor, if the influence characteristic factors occupied by the two applications are a1, a2, a3 and a7, the two applications respectively obtain user weight values corresponding to a1, a2, a3 and a7, and in order to distinguish, the obtained application one is marked as TXc 1, C2, C3 and C7, and the obtained application two applications are marked as C1, C2, C3 and C7, then T = (C1-C) + (C2-C3) + (C-C7) + (C-C7).
And 5, constructing a dissimilarity feature set of the associated application based on the standard information table in the step 3:
step Z1, acquiring factors with anisotropy in the factors influencing the characteristics based on the standard information table in the step 3;
step Z2, calculating the dissimilarity strength of different anisotropic dimensions of each character by analyzing anisotropic factors;
and step Z3, substituting the standard information table and the dissimilarity strength into a dissimilarity formula, calculating to obtain dissimilarity characteristic values, and summarizing the dissimilarity characteristic values to obtain a dissimilarity characteristic set.
Randomly extracting any two applications, respectively comparing the influence characteristic factors of the two applications with the standard information table, respectively extracting the same parts of the two applications and the standard information table, and then comparing the same parts to find different influence characteristic factors, wherein the different influence characteristic factors are used as anisotropy factors, for example, the influence characteristic factors occupied by one application are a1, a2, a3 and a7, the corresponding dimensional coefficients are B1, B2, B3 and B7, the user weight values are C1, C2, C3 and C7, the influence characteristic factors occupied by two applications are a1, a5, a8 and a11, for the purpose of distinguishing, the corresponding dimensional coefficients are B1, B5, B8 and B11, and the user weight values are C1, C5, C8 and C11, the dissimilarity strength YHQ is the sum of B2, B3, B7, B5, B8 and B11, and the dissimilarity formula is G YHQ = YHT, YHT = C2 × B3+ B7+ C7 + B7+ C5+ C8+ B8 + C11 + C8+ C11.
Step 6, depicting a group association structure diagram:
step T1, aiming at a character information total set, an assimilation feature set and a dissimilarity feature set, respectively drawing images of the structural relationship of characters of different groups to obtain a group association structure chart;
and T2, archiving and outputting the group association structure chart.
The image drawing of the structural relationship of people in different groups refers to the following steps:
two spheres which are symmetrically arranged are drawn in space and are set as central spheres to represent one of the associated applications, then a semicircular spherical surface is drawn outside the central spheres on the basis of the two central spheres, and other associated applications are drawn on the semicircular spherical surface respectively to serve as associated spheres, so that the distances from the associated spheres to the central spheres are ensured to be equal, a connecting column is built between the central spheres and the associated spheres, and the size and the color of the connecting column are set according to actual conditions, specifically: the connecting column between one central sphere and the associated sphere represents assimilation between two associated applications, the color adopts single color, the thicker the connecting column is, the greater the assimilation strength is, the thicker the connecting column between the other central sphere and the associated sphere represents dissimilarity between the two associated applications, the color adopts gradient color, the thicker the connecting column is, the greater the dissimilarity strength is, the two central spheres can be moved together until being coincided, at the moment, a complete sphere with a hollow structure is formed on the two semicircular spheres, finally, after the sphere is removed, a space structure which takes the central sphere as the center and surrounds the central sphere by the spherical surface structure of the other associated spheres is obtained, in order to better compare the dissimilarity and assimilation of the central sphere and one of the associated spheres, during drawing, the two associated spheres representing the same applications are drawn symmetrically, and finally, the group associated structure diagram is filed and output.
The above is only a preferred embodiment of the present invention, and the protection scope of the present invention is not limited to the above-mentioned embodiments, and all technical solutions belonging to the idea of the present invention belong to the protection scope of the present invention. It should be noted that modifications and embellishments within the scope of the present invention may occur to those skilled in the art without departing from the principle of the present invention, and such modifications and embellishments should also be considered as within the scope of the present invention.
Claims (7)
1. A cross-application group relation analysis method based on mobile phone data is characterized by comprising the following steps:
step 1, screening and mining mobile phone application data and application groups: confirming the information of the person to which the mobile phone belongs and confirming the associated application under the condition of collecting the target information;
step 2, constructing a character information collection: the personal information of the mobile phone and the associated application form a personal information aggregate;
step 3, acquiring a standard information table in the total character information set;
step 4, constructing an assimilation feature set of the associated application based on the standard information table in the step 3;
step 5, establishing a dissimilarity characteristic set of the associated application based on the standard information table in the step 3;
and 6, depicting a group association structure diagram.
2. The method for analyzing relationship between application groups based on mobile phone data according to claim 1, wherein the step 1 of screening and mining the mobile phone application data and the application groups comprises the following steps:
s1, establishing an application data table with an association relation: acquiring a mobile phone common social application list based on a mobile phone application store;
s2, collecting target information, and drawing an APP social attribute three-dimensional space coordinate system through three information dimensions preset in the target information;
s3, putting all applications in the mobile phone common social application list into an APP social attribute three-dimensional space coordinate system;
s4, carrying out standardization processing on the putting result in the step S3, and confirming all applications belonging to the first quadrant as related applications;
and S5, determining the information of the person to which the mobile phone belongs based on the targeting information.
3. The method for analyzing group relationship among applications based on mobile phone data as claimed in claim 1, wherein the step 2 of forming the personal information and the associated applications to which the mobile phone belongs into the personal information aggregate comprises the following steps:
step U1, processing the target information in the associated application to form character information diversity I;
step U2, processing the targeted information in the personal information of the mobile phone to form personal information diversity II;
and step U3, summarizing the person information diversity I and the person information diversity II to form a person information total set.
4. The method for analyzing relationships between cross-application groups based on mobile phone data as claimed in claim 1, wherein the step 3 of obtaining the standard information table in the total set of personal information comprises the following steps:
v1, based on the target information, extracting influence characteristic factors and dimensionality coefficients from the character information of the mobile phone in the character information total set, and obtaining user weight values corresponding to the influence characteristic factors;
and V2, forming a standard information table by the influence characteristic factors, the dimension coefficients and the user weight values.
5. The method for analyzing the relation between the cross-application groups based on the mobile phone data as claimed in claim 1, wherein the step 4 of constructing the assimilation feature set of the associated application comprises the following steps:
w1, acquiring factors with isotropy in the factors affecting the characteristics based on the standard information table in the step 3;
w2, analyzing the homodromous factors, and calculating the homodromous strength of different homodromous dimensions of each character;
and step W3, substituting the standard information table and the equidirectional strength into an equidirectional formula, calculating to obtain equidirectional characteristic values, and summarizing the equidirectional characteristic values to obtain an equidirectional characteristic set.
6. The method for analyzing the relation between the cross-application groups based on the mobile phone data as claimed in claim 1, wherein the step 5 of constructing the dissimilarity feature set of the associated application comprises the following steps:
step Z1, acquiring factors with anisotropy in the factors influencing the characteristics based on the standard information table in the step 3;
step Z2, calculating the dissimilarity strength of different anisotropic dimensions of each figure by analyzing anisotropic factors;
and step Z3, substituting the standard information table and the dissimilarity strength into a dissimilarity formula, calculating to obtain dissimilarity characteristic values, and summarizing the dissimilarity characteristic values to obtain a dissimilarity characteristic set.
7. The method for analyzing the relationship between the cross-application groups based on the mobile phone data as claimed in claim 1, wherein the step 6 of depicting the structure diagram of the association groups comprises the following steps:
step T1, aiming at a character information total set, an assimilation feature set and a dissimilarity feature set, respectively drawing images of the structural relationship of characters of different groups to obtain a group association structure chart;
and T2, archiving and outputting the group association structure chart.
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