Center calculation method based on intimacy and relation contribution degree between known individuals

Abstract

The invention provides a centrality calculation method and system based on known intimacy and relation contribution degree between individuals. The centrality calculation method comprises the following steps: converting collected social network data into two matrix data in response; the two pieces of matrix data comprise an inter-individual intimacy data matrix and a relation contribution degree data matrix, each element of the inter-individual intimacy data matrix represents the intimacy between two specific individuals, and each element of the relation contribution degree data matrix represents the contribution degree of the inter-individual intimacy by other individuals; and converting a calculation formula of degree centrality, proximity centrality and intermediary centrality into a formula for calculation based on the intimacy between known individuals. According to the scheme of the invention, the known intimacy between individuals in the social network and the contribution degree of common adjacent nodes to the intimacy are considered, and more efficient and convenient calculation of degree centrality, approach centrality and intermediary centrality is realized by converting complex graph calculation into simple matrix calculation.

Description

Technical Field

[0001] The present invention belongs to the technical field of center point calculation, and specifically relates to a center degree calculation method and system based on intimacy and relationship contribution between known individuals. Background Art

[0002] In social networks, there are always more important roles. They can be the center of interpersonal relationships, the bridge of interpersonal relationships, or the focus of interpersonal relationships. How to screen out the "central figures" by analyzing the centrality of a node in a social network has become an important topic in graph theory and network analysis. Therefore, metrics such as degree centrality, betweenness centrality, and closeness centrality have been proposed to measure the importance of a node in a social network.

[0003] Degree centrality is the most direct metric to characterize node centrality in network analysis, but it can often only find people who "frequently communicate with others" while ignoring "intermediate contacts" or "sources of messages". Although betweenness centrality and closeness centrality can find more people who play "important roles" in social networks, their calculation process involves shortest path analysis. Even if optimization algorithms such as Brandes algorithm are used, their calculation complexity is still Θ(|V|*|E|), where |V| is the number of nodes in the entire graph and |E| is the number of edges. This poses a huge efficiency problem in the analysis of large social networks.

[0004] In view of this, it is very meaningful to propose a centrality calculation method and system based on the intimacy and relationship contribution between known individuals. Summary of the invention

[0005] In order to solve the technical defects existing in the above-mentioned prior art, the present invention provides a centrality calculation method and system based on the intimacy and relationship contribution between known individuals.

[0006] In a first aspect, the present invention proposes a centrality calculation method based on the intimacy and relationship contribution between known individuals, the method comprising the following steps:

[0007] In response to converting the collected social network data into two matrix data;

[0008] The two matrix data include an inter-individual intimacy data matrix and a relationship contribution data matrix, wherein each element of the inter-individual intimacy data matrix represents the intimacy between two specific individuals, and each element of the relationship contribution data matrix represents the degree to which the intimacy between individuals is contributed by other individuals;

[0009] The calculation formulas of degree centrality, closeness centrality and betweenness centrality are converted into formulas based on the closeness between known individuals.

[0010] Preferably, in response to converting the collected social network data into two matrix data, the step specifically includes:

[0011] Matrixization of the intimacy data between individuals. It is known that in a collected social network data, there are g individuals N = {N 1 , N 2 ,......,N g} and the intimacy between any two individuals, Intimacy data, construct a g×g matrix R, each element R in R ij Represents individual N i and individual N j The degree of intimacy between them;

[0012] Matrixization of relationship contribution data, for any R in R ij , if there is an individual N i and individual N j The set of common adjacent nodes A ij , record A ij The kth individual in R ij The contribution of co ij (k), construct a g×g matrix G, each element G in G ij By an array [A ij ,co ij ] indicates that, among them, A ij ={A ij (1), A ij (2), ..., A ij (k)...}, co ij ={co ij (1), co ij (2), ..., co ij (k), ...}.

[0013] Preferably, converting the calculation formulas of degree centrality, closeness centrality and betweenness centrality into calculation formulas based on the intimacy between known individuals includes obtaining the calculation methods of degree centrality, closeness centrality and betweenness centrality according to graph theory knowledge, specifically including:

[0014] Degree centrality calculation method, calculation node N i The degree centrality C D (N i ),but:

[0015]

[0016] Among them, deg(N i ) represents node N i The degree of is the number of adjacent nodes;

[0017] Closeness centrality calculation method, closeness centrality C C (N i ) is calculated as follows:

[0018]

[0019] After standardization, it is still recorded as C C (N i ),but:

[0020]

[0021] Among them, d(N i , N j ) represents individual N i and individual N j The shortest distance between

[0022] Betweenness centrality calculation method, betweenness centrality C B (N i ) is calculated as follows:

[0023]

[0024] After standardization, it is still recorded as C B (N i ),but:

[0025]

[0026] Among them, m sj Represents the number of shortest paths between node s and node j, n sj (i) represents the number of shortest paths between nodes s and j that pass through node i.

[0027] Further preferably, converting the calculation formulas of degree centrality, closeness centrality and betweenness centrality into calculation formulas based on known intimacy between individuals includes converting the calculation method of degree centrality into matrix calculation, specifically including:

[0028] The degree centrality calculation method is to convert the formula into the intimacy data matrix R between individuals. Translates to:

[0029]

[0030] Among them, R ij The larger the value, the more individual N i and individual N j The higher the weight of the connecting edge between ij =0 indicates individual N i and individual N j There are no edges connecting them; Represents individual N i The sum of the intimacy with all adjacent nodes, in particular, R ij When the value range of is {0, 1},

[0031] Further preferably, converting the calculation formulas of degree centrality, closeness centrality and betweenness centrality into calculation formulas based on known intimacy between individuals includes converting the calculation method of closeness centrality into matrix calculation, specifically including:

[0032] The calculation method of closeness centrality is to convert the formula into Translates to:

[0033]

[0034] Among them, R ij The larger the value, the more individual N i and individual N j The shorter the distance between them; R ij =0 indicates individual N i and individual N j The distance between them is infinite; Equivalent to individual N i and individual N j The shortest distance between i , N j ).

[0035] Further preferably, converting the calculation formulas of degree centrality, closeness centrality and betweenness centrality into a calculation formula based on the intimacy between known individuals also includes converting the calculation method of betweenness centrality into a matrix calculation, specifically including:

[0036] The calculation method of betweenness centrality is to convert the formula into Translates to:

[0037]

[0038] in, and have the same boundaries; Represents the evaluation individual N s and individual N j The degree of intimacy between people is the contribution made by individual Ni. When s and individual N j The common adjacent nodes between sj (i) = R sj hour, At this time, individual N s and individual N j The intimacy between sj , and individual N i R ij Contribution of sj (i) Consistent, that is, individual N s and individual N j The relationship between the individual N i Decide.

[0039] In a second aspect, an embodiment of the present invention provides a centrality calculation system based on known intimacy and relationship contribution between individuals, the system comprising:

[0040] A data collection module configured to collect social network data;

[0041] A conversion module configured to convert the collected social network data into two matrix data;

[0042] The formula conversion module is configured to convert the calculation formulas of degree centrality, closeness centrality and betweenness centrality into formulas for calculation based on the intimacy between known individuals.

[0043] In a third aspect, an embodiment of the present invention provides an electronic device, comprising: one or more processors; a storage device for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the method described in any implementation manner in the first aspect.

[0044] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method described in any implementation manner in the first aspect.

[0045] Compared with the prior art, the beneficial results of the present invention are:

[0046] (1) The present invention proposes a method that utilizes the known intimacy between individuals and the contribution of other individuals to this relationship when measuring the intimacy between individuals. The method converts the calculation process that depends on complex indicators such as node degree, node distance, and shortest path between nodes into a simple calculation process that depends on the intimacy between individuals and the contribution of relationships. The method calculates degree centrality, closeness centrality, and betweenness centrality respectively, simplifies the calculation steps, reduces the calculation complexity, and improves the calculation efficiency. The method serves as a new indicator for measuring important roles in social networks, and excavates more potential and invisible central figures.

[0047] (2) Convert complex problems such as calculating node degree, distance between nodes, and shortest path between nodes into simple problems of calculating centrality using known intimacy and relationship contribution between individuals; convert complex calculation methods based on graph theory into simple calculation methods based on matrices.

[0048] (3) The solution of the present invention takes into account the known intimacy between individuals in the social network and the contribution of common adjacent nodes to this intimacy. By converting complex graph calculations into simple matrix calculations, more efficient and convenient degree centrality, closeness centrality, and betweenness centrality calculations are achieved.

[0049] (4) The degree centrality, closeness centrality and betweenness centrality of the present invention can be used as the basis for screening important roles in social networks. For example, degree centrality tends to select people who are good at communication; closeness centrality tends to select the central figures of social networks, who may not necessarily be celebrities but are willing to pass messages among the crowd; betweenness centrality tends to select the bridge of interpersonal relationships, who may not have much contact with the crowd but may be the messenger of messages between two groups, or the publisher of first-hand messages in a group; therefore, the degree centrality, closeness centrality and betweenness centrality of the present invention have application prospects in scenarios such as social network analysis, crowd structure analysis, and public opinion analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] The accompanying drawings are included to provide a further understanding of the embodiments and are incorporated into and constitute a part of this specification. The accompanying drawings illustrate the embodiments and are used together with the description to explain the principles of the present invention. It will be easy to recognize other embodiments and many expected advantages of the embodiments because they become better understood by reference to the following detailed description. The elements of the drawings are not necessarily to scale with each other. The same reference numerals refer to corresponding similar parts.

[0051] Figure 1 A schematic diagram of a flow chart of a method for calculating centrality based on known intimacy and relationship contribution between individuals according to an embodiment of the present invention;

[0052] Figure 2A schematic diagram of the changing trends of three centrality calculation results in the centrality calculation method based on the known intimacy and relationship contribution between individuals in an embodiment of the present invention;

[0053] Figure 3 A schematic diagram of the structure of a centrality calculation system based on known intimacy and relationship contribution between individuals according to an embodiment of the present invention;

[0054] Figure 4 It is a schematic diagram of the structure of a computer device suitable for implementing an electronic device of an embodiment of the present invention. DETAILED DESCRIPTION

[0055] In the following detailed description, reference is made to the accompanying drawings, which form a part of the detailed description and are shown by illustrative specific embodiments in which the present invention can be put into practice. To this end, directional terms, such as "top", "bottom", "left", "right", "up", "down", etc., are used with reference to the orientation of the figures described. Because the parts of the embodiments can be positioned in several different orientations, directional terms are used for the purpose of illustration and are by no means limiting. It should be understood that other embodiments may be utilized or logical changes may be made without departing from the scope of the present invention. Therefore, the following detailed description should not be adopted in a limiting sense, and the scope of the present invention is defined by the appended claims.

[0056] Degree centrality is the most direct metric to characterize node centrality in network analysis, but it can often only find people who "frequently communicate with others" while ignoring "intermediate contacts" or "sources of messages". Although betweenness centrality and closeness centrality can find more people who play "important roles" in social networks, their calculation process involves shortest path analysis. Even if optimization algorithms such as Brandes algorithm are used, their calculation complexity is still Θ(|V|*|E|) (where |V| is the number of nodes in the entire graph and |E| is the number of edges), which poses a huge efficiency problem in the analysis of large social networks.

[0057] In view of the defects of the above-mentioned prior art, an embodiment of the present invention proposes a method of utilizing the known intimacy between individuals and the contribution of other individuals to the relationship when measuring the intimacy between individuals, and converting the calculation process that depends on complex indicators such as "node degree", "distance between nodes" and "shortest path between nodes" into a simple calculation process that depends on "intimacy between individuals" and "relationship contribution", calculating degree centrality, closeness centrality and betweenness centrality respectively, simplifying the calculation steps, reducing the calculation complexity, and improving the calculation efficiency, so as to use it as a new indicator to measure "important roles" in social networks and to explore more potential and invisible central figures.

[0058] The technical solution disclosed in the present invention has the following advantages: it converts complex problems such as calculating "node degree", "distance between nodes", "shortest path between nodes" into simple problems of calculating centrality using known "closeness between individuals" and "relationship contribution"; it converts complex calculation methods based on graph theory into simple calculation methods based on matrices.

[0059] In a first aspect, an embodiment of the present invention discloses a method for calculating centrality based on the intimacy and relationship contribution between known individuals, such as Figure 1 As shown, the method comprises the following steps:

[0060] S1, in response to converting the collected social network data into two matrix data;

[0061] S2, the two matrix data include an inter-individual intimacy data matrix and a relationship contribution data matrix, each element of the inter-individual intimacy data matrix represents the intimacy between two specific individuals, and each element of the relationship contribution data matrix represents the degree to which the intimacy between individuals is contributed by other individuals;

[0062] S3. Convert the calculation formulas of degree centrality, closeness centrality and betweenness centrality into formulas based on the intimacy between known individuals.

[0063] Specifically, the embodiment of the present invention converts social network data into two matrix data, each element of the first matrix represents the intimacy between two specific individuals, and each element of the second matrix represents the degree to which the intimacy between individuals is contributed by other individuals. The calculation formulas of degree centrality, closeness centrality and betweenness centrality are converted into formulas for calculation based on the intimacy between known individuals.

[0064] Furthermore, the centrality algorithm proposed in the present invention includes the following three stages, which are described in detail below.

[0065] Phase 1: Matrix the intimacy data and relationship contribution data between individuals:

[0066] Step 1: Matrixing of intimacy data between individuals:

[0067] It is known that in a social network, there are g individuals N = {N 1 , N 2 ,.....,N g}, and the intimacy between any two individuals (total Intimacy data), construct a g×g matrix R, each element R in R ij Represents individual N i and individual N j The degree of intimacy between them;

[0068] Step 2: Matrixing of relationship contribution data:

[0069] For any R in step 1 ij , if there is an individual N i and individual N j The set of common adjacent nodes A ij , record A ij The kth individual in R ij The contribution of co ij (k).

[0070] Construct a g×g matrix G, where each element G ij By an array [A ij ,co ij ] indicates that: A ij ={A ij (1), A ij (2), ..., A ij (k)...}, co ij ={co ij (1), co ij (2), ..., co ij (k), ...}.

[0071] Stage 2: According to graph theory knowledge, we can get the calculation method of degree centrality, closeness centrality and betweenness centrality:

[0072] Step 1: Degree centrality calculation method:

[0073] According to the standardized measurement formula proposed by Stanley Wasserman and Katherine Faust (1994), the node N is calculated i The degree centrality C D (N i ), then:

[0074]

[0075] Among them, deg(N i ) represents node N i The degree is the number of adjacent nodes.

[0076] Step 2: Closeness center calculation method:

[0077] According to the closeness centrality C proposed by LC Freeman (1979) C (N i ) calculation formula is:

[0078]

[0079] After standardization, it is still recorded as C C (N i ), then:

[0080]

[0081] Among them, d(N i , N j ) represents individual N i and individual N j The shortest distance between.

[0082] Step 3: Betweenness centrality calculation method:

[0083] According to the betweenness centrality C proposed by LC Freeman (1979), B (N i ) calculation formula is:

[0084]

[0085] After standardization, it is still recorded as C B (N i ), then:

[0086]

[0087] Among them, m sj Represents the number of shortest paths between node s and node j, n sj (i) represents the number of shortest paths between nodes s and j that pass through node i.

[0088] Stage 3: Convert the calculation methods of degree centrality, closeness centrality, and betweenness centrality into matrix calculations:

[0089] Step 1: Degree centrality calculation method:

[0090] According to the matrix R obtained in step 1 of stage 1, formula (1) can be converted to:

[0091]

[0092] In this formula:

[0093] R ij The larger the value, the more individual N i and individual N j The higher the weight of the connecting edge between them;

[0094] R ij =0 indicates individual N i and individual N j There are no edges connecting them;

[0095] Represents individual N i The sum of the intimacy with all adjacent nodes, in particular, R ij When the value range of is {0, 1},

[0096] Step 2: Closeness center calculation method:

[0097] According to the matrix R obtained in step 1 of stage 1, formula (2) can be converted to:

[0098]

[0099] In this formula:

[0100] R ij The larger the value, the more individual N i and individual N j The shorter the distance between them;

[0101] R ij =0 indicates individual N i and individual N j The distance between them is infinite;

[0102] Equivalent to individual N i and individual N j The shortest distance between i , N j ).

[0103] Step 3: Betweenness centrality calculation method:

[0104] According to the matrix R obtained in step 1 of stage 1 and the matrix G obtained in step 2, formula (3) can be converted to:

[0105]

[0106] In formula (6):

[0107] 1) and have the same boundaries;

[0108] 2) It means "to evaluate individual N s and individual N j When the closeness between individuals N i the extent of the contribution made;

[0109] 3) When N i Not an individual s and individual Nj Common adjacent nodes between them;

[0110] 4) If and only if co sj (i) = R sj hour, At this time, individual N s and individual N j The intimacy between sj , and individual N i R ij Contribution of sj (i) Consistent, that is, individual N s and individual N j The relationship between the individual N i Decide.

[0111] Specifically, the embodiment of the present invention uses a data set of connections and friendships between high school students in Marseille, France in December 2013 (involving 204 students) to verify the effectiveness of the algorithm of the present invention. After other intimacy-related calculations, the data includes the following parts:

[0112] 1. Set X 1 : Intimacy between 204 students, totaling 8206 pairs of intimacy;

[0113] 2. Set X 2 : When calculating intimacy, if two students have common adjacent nodes, the contribution of each common adjacent node to this intimacy is calculated, and X 1 All 8206 intimacy levels have common neighboring nodes;

[0114] For set X 1 , set X 2 Perform reasonable preprocessing to meet the calculation requirements in the embodiment:

[0115] 1) According to step 1 in stage 1, set X 1 The intimacy matrix R should be a 204×204 matrix, containing 20706 intimacy. To ensure the correctness of the centrality calculation in stage 3, it is necessary to assign values ​​to the missing 12500 intimacy. The following scheme is adopted for assignment:

[0116] a) Count the minimum value R among 8206 pairs of intimacy min , is 0.011325764;

[0117] b) To avoid the intimacy after assignment affecting the accuracy of the result, it should be as close to 0 as possible and much smaller than the known minimum intimacy;

[0118] c) According to the requirements of b), the missing intimacy is uniformly assigned a value of 0.0000005, which satisfies 0.0000005×204≈0.0001≤100×R min That is, the sum of the missing intimacy values ​​(a total of 204 students) is still far less than the known minimum intimacy.

[0119] 2) According to step 2 in stage 1, set X 2 The contribution matrix G should be a 204×204 matrix, containing 20706 arrays [A ij ,Co ij ]. To ensure the correctness of the calculation of betweenness centrality in step 3 of phase 3, it is necessary to assign values ​​to the missing 12,500 contribution arrays. Considering the co ij As a molecule, the missing co ij The uniform value is {0}, and the corresponding A ij It can be uniformly defined as {N 0}, that is, assuming that a non-existent individual N 0 Obviously at this time This holds true for any s and j, so it does not affect the calculation results of the betweenness centrality.

[0120] According to the solution in this embodiment, the set X 1 and set X 2 As input, the degree centrality, closeness centrality, and betweenness centrality of these 204 students are output. Some of the results are shown in Table 1.

[0121] Table 1: Centrality calculation results of some individuals

[0122] individual Degree Centrality Closeness Centrality Betweenness Centrality 144 0.001190107 5.17857E-07 0 201 0.002079585 5.17857E-07 4.87734E-05 203 0.003390673 5.67038E-07 0 178 0.003566855 5.5163E07 4.87733E-05 179 0.006775079 6.11444E-07 1.51906E-05 58 0.008424886 5.60773E-07 7.34813E-05 188 0.009385506 5.79999E-07 9.75467E05 194 0.009885649 6.07783E-07 2.52614E-05 13 0.009966861 6.22697E-07 1.46366E-06 172 0.010111554 6.59087E-07 1.19828E-05 181 0.010347519 5.73445E-07 2.40639E-05 168 0.010675738 6.63395E-07 2.47937E-05 41 0.010974058 5.90114E-07 0.000380202 99 0.010974814 6.67759E-07 3.40142E-05 200 0.013059432 7.14784E-07 6.1532E-06 189 0.013063956 6.07783E-07 5.46436E-06 202 0.013255994 5.97058E-07 1.42161E05 198 0.013534861 5.3421E-07 5.59902E-05 57 0.013787382 5.73446E-07 0.000331654 3 0.013999399 5.93566E-07 0.000113103 98 0.014480535 7.4087E-07 5.74273E-05 156 0.0145856 6.95202E-07 3.86166E-05 25 0.01492316 6.50638E-07 0.000133868 191 0.01604395 5.79999E-07 3.91934E-05 190 0.016339371 5.76704E-07 0.00024778 32 0.016395636 6.07783E-07 2.66469E-05

[0123] Note: The centrality enumerated in Table 1 has been sorted in ascending order by degree centrality.

[0124] In order to verify the correlation between the degree centrality, closeness centrality and betweenness centrality of the present invention, the correlation coefficient between the three can be calculated, as shown in Table 2.

[0125] Table 2: Correlation coefficients between centralities

[0126] Correlation coefficient value Correlation coefficient between degree centrality and closeness centrality 0.734374444487189 Correlation coefficient between degree centrality and betweenness centrality 0.733019211509273 Correlation coefficient between closeness centrality and betweenness centrality 0.782677753951061

[0127] By observing Table 2, it is found that the correlation between closeness centrality and betweenness centrality is relatively high. This may be because both closeness centrality and betweenness centrality are calculated based on the "shortest distance" between nodes (while degree centrality is based on the number of adjacent nodes). Therefore, the algorithm described in the present invention reflects the similarity between closeness centrality and betweenness centrality in the evaluation basis.

[0128] In order to more intuitively observe the correlation between degree centrality, closeness centrality, and betweenness centrality described in the present invention, the calculation results of the three are normalized respectively, and a line graph of the three centralities is drawn with the ascending order of degree centrality as the horizontal axis, as shown in Figure 2 shown.

[0129] observe Figure 2 , we can find that:

[0130] 1) The degree centrality and closeness centrality have relatively stable fluctuations, while the betweenness centrality, which takes the “individual contribution” into account in the calculation formula (6), has more “dramatic” fluctuations.

[0131] 2) There are many "peak" points on the betweenness centrality line graph. The degree centrality and closeness centrality of these individuals may not be high (such as the middle section of the line graph), but they have very high betweenness centrality. Therefore, these nodes can be treated as potential "important roles" in the social network.

[0132] In a second aspect, the embodiment of the present invention also discloses a centrality calculation system based on the intimacy and relationship contribution between known individuals, such as Figure 3 As shown, the system includes: a data acquisition module 31, a conversion module 32 and a formula conversion module 33.

[0133] In a specific embodiment, the data collection module 31 is configured to collect social network data; the conversion module 32 is configured to convert the collected social network data into two matrix data; the formula conversion module 33 is configured to convert the calculation formulas of degree centrality, closeness centrality and betweenness centrality into formulas based on known intimacy between individuals.

[0134] Graph model is a common solution for social network related algorithms, but its computational cost and efficiency are high. The solution described in the present invention takes into account the known intimacy between individuals in the social network and the contribution of common adjacent nodes to this intimacy, and realizes more efficient and convenient degree centrality, closeness centrality and betweenness centrality calculation by converting complex graph calculation into simple matrix calculation.

[0135] The degree centrality, closeness centrality and betweenness centrality described in the present invention can be used as the basis for screening "important roles" in social networks. For example, degree centrality tends to select "people who are good at communication". Closeness centrality tends to select "central figures in social networks", who may not necessarily be celebrities, but are willing to pass messages among the crowd. Betweenness centrality tends to select "bridges of interpersonal relationships", who may not have much contact with the crowd, but may be the messenger of messages between two groups, or the "first-hand message publisher" in a group. Therefore, the degree centrality, closeness centrality and betweenness centrality described in the present invention have application prospects in scenarios such as social network analysis, crowd structure analysis, and public opinion analysis.

[0136] Reference below Figure 4 , which shows an electronic device (eg Figure 1 A schematic diagram of the structure of a computer device 600 (a server or terminal device as shown). Figure 4 The electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0137] like Figure 4 As shown, the computer device 600 includes a central processing unit (CPU) 601 and a graphics processing unit (GPU) 602, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 603 or a program loaded from a storage part 609 to a random access memory (RAM) 604. In the RAM 604, various programs and data required for the operation of the device 600 are also stored. The CPU 601, GPU 602, ROM 603 and RAM 604 are connected to each other through a bus 605. An input / output (I / O) interface 606 is also connected to the bus 605.

[0138] The following components are connected to the I / O interface 606: an input section 607 including a keyboard, a mouse, etc.; an output section 608 including a display such as a liquid crystal display (LCD), etc. and a speaker, etc.; a storage section 609 including a hard disk, etc.; and a communication section 610 including a network interface card such as a LAN card, a modem, etc. The communication section 610 performs communication processing via a network such as the Internet. A drive 611 may also be connected to the I / O interface 606 as needed. A removable medium 612, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 611 as needed so that a computer program read therefrom is installed into the storage section 609 as needed.

[0139] In particular, according to the embodiments disclosed in the present invention, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present invention include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication part 610, and / or installed from the removable medium 612. When the computer program is executed by the central processing unit (CPU) 601 and the graphics processing unit (GPU) 602, the above-mentioned functions defined in the method of the present invention are executed.

[0140] It should be noted that the computer-readable medium described in the present invention may be a computer-readable signal medium or a computer-readable medium or any combination of the above two. The computer-readable medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor device, apparatus or device, or any combination of the above. More specific examples of computer-readable media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present invention, a computer-readable medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution device, apparatus or device. In the present invention, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable program code. This propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The computer-readable signal medium may also be any computer-readable medium other than a computer-readable medium that can send, propagate or transmit a program for use by or in conjunction with an instruction execution device, apparatus or device. The program code contained on the computer-readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, optical cable, RF, etc., or any suitable combination of the above.

[0141] Computer program code for performing the operations of the present invention may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0142] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the device, method and computer program product according to various embodiments of the present invention. In this regard, each box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based device that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0143] The modules involved in the embodiments of the present invention may be implemented in software or hardware, and the modules described may also be arranged in a processor.

[0144] As another aspect, the present invention also provides a computer-readable medium, which may be included in the electronic device described in the above embodiment; or it may exist independently without being assembled into the electronic device. The above computer-readable medium carries one or more programs, and when the above one or more programs are executed by the electronic device, the electronic device: responds to converting the collected social network data into two matrix data; the two matrix data include an inter-individual intimacy data matrix and a relationship contribution data matrix, each element of the inter-individual intimacy data matrix represents the intimacy between two specific individuals, and each element of the relationship contribution data matrix represents the degree to which the intimacy between individuals is contributed by other individuals; converts the calculation formulas of degree centrality, closeness centrality and betweenness centrality into formulas based on known inter-individual intimacy.

[0145] The above description is only a preferred embodiment of the present invention and an explanation of the technical principles used. Those skilled in the art should understand that the scope of the invention involved in the present invention is not limited to the technical solution formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above inventive concept. For example, the above features are replaced with the technical features with similar functions disclosed in the present invention (but not limited to) to form a technical solution.

Claims

1. A method for calculating centrality based on the intimacy and relationship contribution between known individuals, characterized in that: The method comprises the following steps: In response to converting the collected social network data into two matrix data; The two matrix data include an inter-individual intimacy data matrix and a relationship contribution data matrix, wherein each element of the inter-individual intimacy data matrix represents the intimacy between two specific individuals, and each element of the relationship contribution data matrix represents the degree to which the intimacy between individuals is contributed by other individuals; The calculation formulas of degree centrality, closeness centrality and betweenness centrality are converted into formulas based on the closeness between known individuals.

2. The centrality calculation method based on the intimacy and relationship contribution between known individuals according to claim 1 is characterized in that: In response to converting the collected social network data into two matrix data, the method specifically includes: Matrixization of the intimacy data between individuals. It is known that in a collected social network data, there are g individuals N = {N1, N2, ..., N g } and the intimacy between any two individuals, Intimacy data, construct a g×g matrix R, each element R in R ij Represents individual N i and individual N j The degree of intimacy between them; Matrixization of relationship contribution data, for any R in R ij , if there is an individual N i and individual N j The set of common adjacent nodes A ij , remember A ij The kth individual in R ij The contribution of co ij (k), construct a g×g matrix G, each element G in G ij By an array [A ij ,co ij ] indicates that, among them, A ij ={A ij (1), A ij (2), ..., A ij (k)...}, co ij ={co ij (1), co ij (2), ..., co ij (k), ...}.

3. The centrality calculation method based on the intimacy and relationship contribution between known individuals according to claim 1 is characterized in that: The calculation formulas of degree centrality, closeness centrality and betweenness centrality are converted into calculation formulas based on the closeness between known individuals, including the calculation methods of degree centrality, closeness centrality and betweenness centrality obtained according to graph theory knowledge, specifically including: Degree centrality calculation method, calculation node N i The degree centrality C D (N i ),but: Among them, deg(N i ) represents node N i The degree of is the number of adjacent nodes; Closeness centrality calculation method, closeness centrality C C (N i ) is calculated as follows: After standardization, it is still recorded as C C (N i ),but: Among them, d(N i , N j ) represents individual N i and individual N j The shortest distance between Betweenness centrality calculation method, betweenness centrality C B (N i ) is calculated as follows: After standardization, it is still recorded as C B (N i ),but: Among them, m sj Represents the number of shortest paths between node s and node j, n sj (i) represents the number of shortest paths between nodes s and j that pass through node i.

4. The centrality calculation method based on the intimacy and relationship contribution between known individuals according to claim 3 is characterized in that: Converting the calculation formulas of degree centrality, closeness centrality, and betweenness centrality into calculation formulas based on the closeness between known individuals includes converting the calculation method of degree centrality into matrix calculation, specifically including: The degree centrality calculation method is to convert the formula into the intimacy data matrix R between individuals. Converts to: Among them, R ij The larger the value, the more individual N i and individual N j The higher the weight of the connecting edge between ij =0 indicates individual N i and individual N j There are no edges connecting them; Represents individual N i The sum of the intimacy with all adjacent nodes, in particular, R ij When the value range of is {0, 1}, 5. The centrality calculation method based on the intimacy and relationship contribution between known individuals according to claim 4 is characterized in that: Converting the calculation formulas of degree centrality, closeness centrality, and betweenness centrality into calculation formulas based on the closeness between known individuals includes converting the calculation method of closeness centrality into matrix calculation, specifically including: The calculation method of closeness centrality is to convert the formula into Converts to: Among them, R ij The larger the value, the more individual N i and individual N j The shorter the distance between them; R ij =0 indicates individual N i and individual N j The distance between them is infinite; Equivalent to individual N i and individual N j The shortest distance between i , N j ).

6. The centrality calculation method based on the intimacy and relationship contribution between known individuals according to claim 5 is characterized in that: Converting the calculation formulas of degree centrality, closeness centrality, and betweenness centrality into calculation formulas based on known intimacy between individuals also includes converting the calculation method of betweenness centrality into matrix calculation, specifically including: The calculation method of betweenness centrality is to convert the formula into Converts to: in, and have the same boundaries; Represents the evaluation individual N s and individual N j The degree of intimacy between people is the contribution made by individual Ni. When N i Not an individual s and individual N j The common adjacent nodes between sj (i) = R sj hour, At this time, individual N s and individual N j The intimacy between sj , and individual N i R ij Contribution of sj (i) Consistent, that is, individual N s and individual N j The relationship between the individual N i Decide.

7. A centrality calculation system based on the intimacy and relationship contribution between known individuals, characterized in that: The system includes: A data collection module configured to collect social network data; A conversion module configured to convert the collected social network data into two matrix data; The formula conversion module is configured to convert the calculation formulas of degree centrality, closeness centrality and betweenness centrality into formulas for calculation based on the intimacy between known individuals.

8. An electronic device, comprising: one or more processors; A storage device for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

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