CN111782963B - Social network data mining method and system based on SNS and service equipment - Google Patents
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Abstract
The embodiment of the application relates to a social network data mining method and system based on SNS and service equipment, wherein the method comprises the following steps: based on the interaction relation between any two users in the SNS, constructing a data model of the social network; based on the data model, performing social network data mining operations; the social network data mining operation includes, but is not limited to, social network visual display, hot character discovery, friend interaction index measurement, community discovery based on friend interaction indexes, friend recommendation and personalized information recommendation, and the embodiment of the application can be implemented without reducing coverage and business volume.
Description
Technical Field
The application relates to the technical field of internet, in particular to a social network data mining method, a social network data mining system and service equipment based on SNS.
Background
Social networking services (Social Network Services, SNS for short) are a class of web application services that can help users build a friend relationship network and can share information about hobbies, interests, activities, and status among friends. In recent years, with the development of information technologies such as the Internet, various social network applications have rapidly developed, and how to more fully mine social network data, so that SNS has wider application prospects and market spaces, and is a research direction of comparing fronts and hotspots in recent years in academia.
Disclosure of Invention
The embodiment of the application discloses a social network data mining method and a social network data mining system based on SNS, which can more fully mine social network data, so that the SNS has wider application prospect and market space.
The first aspect of the embodiment of the application discloses a social network data mining method based on SNS, which comprises the following steps:
based on the interaction relation between any two users in the SNS, constructing a data model of the social network;
based on the data model, performing social network data mining operations;
the social network data mining operation includes, but is not limited to, social network visual presentation, hot person discovery, friend interaction index measurement, community discovery based on friend interaction index, friend recommendation and personalized information recommendation.
In combination with the first aspect of the embodiments of the present application, in some preferred embodiments, each user in the SNS corresponds to a node, when the user i actively sends an online interaction to the user j, the node i corresponding to the user i is corresponding to an edge of the node j corresponding to the user j, the weight W (i, j) of the edge is at least related to three factors including an interaction type, an interaction frequency and an interaction time, and the weight W (i, j) of the edge represents a social affinity degree between the user i and the user j.
With reference to the first aspect of the embodiments of the present application, in some preferred embodiments, the constructing a data model of a social network based on an interaction relationship between any two users in an SNS includes:
defining that a user i actively sends weights V (i, j) corresponding to different interaction types to a user j in an SNS network as follows:
V(i,j)∈[1,2,3,4,5]
wherein the value of V (i, j) is an integer between 1 and 5, and the larger the value of V (i, j) is, the higher the social affinity between the user i and the user j is;
the factor TF (t) defining the interaction time is as follows:
wherein t: representing the month of occurrence of the kth interaction; t (T) 1 Representing the month; t (T) 0 Representing the time start month; the numerator and the denominator are added with 1 respectively to prevent the situation that the numerator or the denominator is equal to 0; the later the interaction time, the stronger the interaction timeliness, and the larger the factor TF (t) of the interaction timeliness;
defining the weight w for user i to actively initiate the kth interaction to user j k (i, j, t) is as follows:
w k (i,j,t)＝V(i,j)×TF(t)
define the total weight W (i, j) that user i actively initiates all interactions to user j as follows:
k: the method comprises the steps that a user i actively initiates K interactions to a user j, namely the interaction frequency; wherein K is greater than or equal to K.
Based on the total weight W (i, j) of all interactions initiated by the user i to the user j in the SNS, a weighted directed social network diagram is constructed and used as a data model of the social network.
With reference to the first aspect of the embodiments of the present application, in some preferred embodiments, if the social network data mining operation includes social network visual presentation, the performing, based on the data model, the social network data mining operation includes:
representing the difference of the weights of the edges in the data model by the difference of the lengths, the thicknesses or the colors of the edges; wherein the shorter the length of the edge, the greater the weight of the edge; the thicker the edge, the greater the weight representing the edge; the darker the color of the edge, the greater the weight representing the edge;
and, representing the liveness or popularity of the user with the size or color of the nodes being different; wherein a larger node represents a more active or popular representation of a user; the darker the node indicates the more active or popular the user.
With reference to the first aspect of the embodiments of the present application, in some preferred embodiments, if the social network data mining operation further includes hot spot person discovery, the method further includes:
mining hot spot figures in the SNS network according to the entering degree and the exiting degree of each node;
wherein, the ingress degree indecree (i) of the node i: defining the sum of all the weights of the edges pointing to the node i;
Wherein, the outbound degree outdepth (i) of the node i: defining as the sum of the weights of all edges issued from node i;
the higher the outbound degree Outdegree (i) of the node i is, the more active the user i corresponding to the node i is in the social relationship; the higher the incoming degree Inregre (i) of the node i is, the higher the popularity of the user i corresponding to the node i is; the first few users with highest incorporations index (i) in the SNS network act as hotspot characters in the SNS network.
With reference to the first aspect of the embodiments of the present application, in some preferred embodiments, if the social network data mining operation further includes a friend interaction index metric, the method further includes:
defining a secondary interactive user set of the user i; the users with direct interaction with the user i form a set S, and all other users with direct interaction with any user in the set S form a secondary interaction user set of the user i; wherein, user j is located in the set S, there is bidirectional direct interaction between node i corresponding to user i and node j corresponding to user j, and the total weight between user i and user j is W (i, j) +w (j, i); the user p is located in the secondary interaction user set of the user i, bidirectional direct interaction exists between a node p corresponding to the user p and a node j corresponding to the user j, and the total weight between the user p and the user j is W (j, p) +W (p, j); the user m is neither located in the set S nor in the secondary interaction user set of the user i, bidirectional direct interaction exists between a node m corresponding to the user m and a node p corresponding to the user p, and the total weight between the user m and the user p is W (p, m) +W (m, p);
Defining an attenuation factor rho epsilon (0, 1), and then friend interaction index Inter (i, p) between the user i and the user p is as follows:
Inter(i,p)＝(W(i,j)+W(j,i))*1+(W(j,p)+W(p,j))*ρ
and, friend interaction index Inter (i, m) between user i and user m is:
Inter(i,m)＝(W(i,j)+W(j,i))*1+(W(j,p)+W(p,j))*ρ+(W(p,m)+W(m,p))*ρ 2 。
with reference to the first aspect of the embodiments of the present application, in some preferred embodiments, if the social network data mining operation further includes community discovery and friend recommendation based on a friend interaction index, the method includes:
acquiring users with friend interaction indexes which exceed a specified threshold beta with the user i, and forming a user community of the user i; the user community of the user i represents a user community with the strongest interaction relationship with the user i and the highest social affinity degree, namely a community taking the user i as a center;
and recommending other users which are not established as friends with the user i to the user i as friends if the other users which are not established as friends with the user i exist in the community which takes the user i as a center.
With reference to the first aspect of the embodiments of the present application, in some preferred embodiments, if the social network data mining operation further includes personalized information recommendation, the method includes:
and recommending the target object which is recently accessed in the website by the user in the community to the user i in the community taking the user i as the center.
A second aspect of an embodiment of the present application discloses an SNS-based social network data mining system, including:
the model building unit is used for building a data model of the social network based on the interaction relationship between any two users in the SNS;
the data mining unit is used for performing social network data mining operation based on the data model;
the social network data mining operation includes, but is not limited to, social network visual presentation, hot person discovery, friend interaction index measurement, community discovery based on friend interaction index, friend recommendation and personalized information recommendation.
In combination with the second aspect of the embodiments of the present application, in some preferred embodiments, each user in the SNS corresponds to a node, when the user i actively sends an online interaction to the user j, the node i corresponding to the user i is corresponding to an edge of the node j corresponding to the user j, the weight W (i, j) of the edge is at least related to three factors including an interaction type, an interaction frequency and an interaction timeliness, and the weight W (i, j) of the edge represents a social intimacy degree between two users of the user i and the user j.
In combination with the second aspect of the embodiments of the present application, in some preferred embodiments, the model building unit is specifically configured to:
Defining that a user i actively sends weights V (i, j) corresponding to different interaction types to a user j in an SNS network as follows:
V(i,j)∈[1,2,3,4,5]
wherein the value of V (i, j) is an integer between 1 and 5, and the larger the value of V (i, j) is, the higher the social affinity between the user i and the user j is;
the factor TF (t) defining the interaction time is as follows:
wherein t: representing the month of occurrence of the kth interaction; t (T) 1 Representing the month; t (T) 0 Representing the time start month; the numerator and the denominator are added with 1 respectively to prevent the situation that the numerator or the denominator is equal to 0; the later the interaction time, the stronger the interaction timeliness, and the larger the factor TF (t) of the interaction timeliness;
defining the weight w for user i to actively initiate the kth interaction to user j k (i, j, t) is as follows:
w k (i,j,t)＝V(i,j)×TF(t)
define the total weight W (i, j) that user i actively initiates all interactions to user j as follows:
k: the method comprises the steps that a user i actively initiates K interactions to a user j, namely the interaction frequency; wherein K is greater than or equal to K.
Based on the total weight W (i, j) of all interactions initiated by the user i to the user j in the SNS, a weighted directed social network diagram is constructed and used as a data model of the social network.
In combination with the second aspect of the embodiments of the present application, in some preferred embodiments, if the social network data mining operation includes social network visual presentation, the data mining unit includes a social network visual presentation unit, where the social network visual presentation unit is specifically configured to:
Representing the difference of the weights of the edges in the data model by the difference of the lengths, the thicknesses or the colors of the edges; wherein the shorter the length of the edge, the greater the weight of the edge; the thicker the edge, the greater the weight representing the edge; the darker the color of the edge, the greater the weight representing the edge;
and, representing the liveness or popularity of the user with the size or color of the nodes being different; wherein a larger node represents a more active or popular representation of a user; the darker the node indicates the more active or popular the user.
In combination with the second aspect of the embodiments of the present application, in some preferred embodiments, if the social network data mining operation further includes a hotspot persona discovery, the data mining unit further includes a hotspot persona discovery unit, where the hotspot persona discovery unit is specifically configured to:
mining hot spot figures in the SNS network according to the entering degree and the exiting degree of each node;
wherein, the ingress degree indecree (i) of the node i: defining the sum of all the weights of the edges pointing to the node i;
wherein, the outbound degree outdepth (i) of the node i: defining as the sum of the weights of all edges issued from node i;
the higher the outbound degree Outdegree (i) of the node i is, the more active the user i corresponding to the node i is in the social relationship; the higher the incoming degree Inregre (i) of the node i is, the higher the popularity of the user i corresponding to the node i is; the first few users with highest incorporations index (i) in the SNS network act as hotspot characters in the SNS network.
With reference to the second aspect of the embodiments of the present application, in some preferred embodiments, if the social network data mining operation further includes a friend interaction index measurement unit, the data mining unit further includes a friend interaction index measurement unit, where the friend interaction index measurement unit is specifically configured to:
defining a secondary interactive user set of the user i; the users with direct interaction with the user i form a set S, and all other users with direct interaction with any user in the set S form a secondary interaction user set of the user i; wherein, user j is located in the set S, there is bidirectional direct interaction between node i corresponding to user i and node j corresponding to user j, and the total weight between user i and user j is W (i, j) +w (j, i); the user p is located in the secondary interaction user set of the user i, bidirectional direct interaction exists between a node p corresponding to the user p and a node j corresponding to the user j, and the total weight between the user p and the user j is W (j, p) +W (p, j); the user m is neither located in the set S nor in the secondary interaction user set of the user i, bidirectional direct interaction exists between a node m corresponding to the user m and a node p corresponding to the user p, and the total weight between the user m and the user p is W (p, m) +W (m, p);
Defining an attenuation factor rho epsilon (0, 1), and then friend interaction index Inter (i, p) between the user i and the user p is as follows:
Inter(i,p)＝(W(i,j)+W(j,i))*1+(W(j,p)+W(p,j))*ρ
and, friend interaction index Inter (i, m) between user i and user m is:
Inter(i,m)＝(W(i,j)+W(j,i))*1+(W(j,p)+W(p,j))*ρ+(W(p,m)+W(m,p))*ρ 2 。
in combination with the second aspect of the embodiments of the present application, in some preferred embodiments, if the social network data mining operation further includes community discovery and friend recommendation based on a friend interaction index, the data mining unit further includes a community discovery unit and a friend recommendation unit, where:
the community discovery unit is specifically configured to obtain a user whose friend interaction index Inter with the user i exceeds a specified threshold β, so as to form a user community of the user i; the user community of the user i represents a user community with the strongest interaction relationship with the user i and the highest social affinity degree, namely a community taking the user i as a center;
the friend recommending unit is specifically configured to recommend other users that are not established as friends with the user i to the user i as friends if the other users exist in the community with the user i as centers.
With reference to the second aspect of the embodiments of the present application, in some preferred embodiments, if the social network data mining operation further includes a personalized information recommendation, the data mining unit further includes a personalized information recommendation unit, where the personalized information recommendation unit is specifically configured to:
And recommending the target object which is recently accessed in the website by the user in the community to the user i in the community taking the user i as the center.
A third aspect of the embodiments of the present application discloses a service device, where the service device includes any one of the social network data mining systems based on SNS disclosed in the second aspect of the embodiments of the present application.
Compared with the prior art, the embodiment of the application has the following beneficial effects:
in the embodiment of the application, the data model of the social network can be constructed based on the interaction relationship between any two users in the SNS; and based on the data model, social network data mining operations including but not limited to social network visual presentation, hot spot character discovery, friend interaction index measurement, community discovery based on friend interaction indexes, friend recommendation, personalized information recommendation and the like are executed, so that social network data can be more fully mined, and SNS has wider application prospects and market space.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow diagram of an SNS-based social network data mining method disclosed in an embodiment of the present application;
FIG. 2 is a schematic diagram of a data model of a social network disclosed in an embodiment of the present application;
FIG. 3 is a schematic diagram of a social networking data mining system based on SNS according to an embodiment of the present application;
fig. 4 is a schematic structural diagram of a service device according to an embodiment of the present application.
Detailed Description
The following description of the technical solutions in the embodiments of the present application will be made clearly and completely with reference to the drawings in the embodiments of the present application, and it is apparent that the described embodiments are only some embodiments of the present application, not all embodiments. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the present disclosure, are within the scope of the present disclosure.
The embodiment of the application discloses a social network data mining method and a social network data mining system based on SNS, which can more fully mine social network data, so that the SNS has wider application prospect and market space. The following detailed description is made with reference to the accompanying drawings.
Referring to fig. 1, fig. 1 is a flow chart of an SNS-based social network data mining method according to an embodiment of the present disclosure. In the social network data mining method based on SNS shown in fig. 1, the social network data mining method based on SNS is described with a service device as an execution subject. The service device may be a server, a service platform, a cloud service architecture, or other devices, which is not limited in the embodiments of the present application. As shown in fig. 1, the SNS-based social network data mining method may include the steps of:
101. The service equipment builds a data model of the social network based on the interaction relation between any two users in the SNS.
102. The service equipment performs social network data mining operation based on the data model; the social network data mining operation includes, but is not limited to, social network visual presentation, hot person discovery, friend interaction index measurement, community discovery based on friend interaction index, friend recommendation and personalized information recommendation.
In this embodiment of the present application, it is assumed that each user in the SNS corresponds to a node, when the user i actively sends an online interaction to the user j, the node i corresponding to the user i points to the edge of the node j corresponding to the user j, the weight W (i, j) of the edge is at least related to three factors including the interaction type, the interaction frequency and the interaction timeliness, and the weight W (i, j) of the edge represents the social intimacy degree between the two users of the user i and the user j.
In some embodiments, in step 101, the service device builds a data model of the social network based on an interaction relationship between any two users in the SNS, including:
defining that a user i actively sends weights V (i, j) corresponding to different interaction types to a user j in the SNS network (the weights can also be called as different interaction type factors) as follows:
V(i,j)∈[1,2,3,4,5] (1-1)
Wherein the value of V (i, j) is an integer between 1 and 5, and the larger the value of V (i, j) is, the higher the social affinity between the user i and the user j is; for example, specific interaction types in microblogs include: forwarding microblogs, comment microblogs, other users and the like; the interaction types in the social networking site include: praise, comment posting content, sending in-station letter, in-station chat and the like; the interaction types in the live website include: viewing live, praise, flower feed, commenting on live content, etc. If any predefined behavior exists between any two users, the two users can be considered to have an interactive relationship.
The factor TF (t) defining the interaction time is as follows:
wherein t: representing the month of occurrence of the kth interaction; t (T) 1 Representing the month; t (T) 0 Representing the time start month; the addition of 1 to the numerator and denominator prevents the numerator or denominatorA situation equal to 0 occurs; the later the interaction time, the more time-efficient the interaction, and the larger the factor TF (t) of the interaction time.
Defining the weight w for user i to actively initiate the kth interaction to user j k (i, j, t) is as follows:
w k (i,j,t)＝V(i,j)×TF(t) (1-3)
define the total weight W (i, j) that user i actively initiates all interactions to user j as follows:
k: the method comprises the steps that a user i actively initiates K interactions to a user j, namely the interaction frequency; wherein K is greater than or equal to K.
The method is also applicable to 3 interaction scenes of one-to-one, one-to-many and many-to-many, and only new weights are needed to be added between the corresponding two user nodes.
Based on the total weight W (i, j) of all interactions initiated by the user i to the user j in the SNS, a weighted directed social network diagram is constructed and used as a data model of the social network.
(1) In a preferred embodiment, in step 102, if the social network data mining operation includes social network visual presentation, the performing, based on the data model, the social network data mining operation includes:
representing the difference of the weights of the edges in the data model by the difference of the lengths, the thicknesses or the colors of the edges; wherein the shorter the length of the edge, the greater the weight of the edge; the thicker the edge, the greater the weight representing the edge; the darker the color of the edge, the greater the weight representing the edge;
and, representing the liveness or popularity of the user with the size or color of the nodes being different; wherein a larger node represents a more active or popular representation of a user; the darker the node indicates the more active or popular the user. This approach would be an interesting visual presentation of social networking data.
(2) In a preferred embodiment, in step 102, if the social network data mining operation further includes hot spot person discovery, the method further includes:
mining hot spot figures in the SNS network according to the entering degree and the exiting degree of each node;
wherein, the ingress degree indecree (i) of the node i: defining the sum of all the weights of the edges pointing to the node i;
wherein, the outbound degree outdepth (i) of the node i: defining as the sum of the weights of all edges issued from node i;
the higher the outbound degree Outdegree (i) of the node i is, the more active the user i corresponding to the node i is in the social relationship; the higher the incoming degree Inregre (i) of the node i is, the higher the popularity of the user i corresponding to the node i is; the first few users with highest degree indetree (i) in the SNS network act as hotspot characters in the SNS network, such as "net red" characters in the live web site. The method can effectively realize the discovery of the hot spot people.
(3) In a preferred embodiment, in step 102, if the social network data mining operation further includes a friend interaction index metric, the method further includes:
defining a secondary interactive user set of the user i; wherein, the users having direct interaction with the user i (whether the user i is active or passive) form a set S, and all other users having direct interaction with any one user in the set S form a secondary interaction user set of the user i; similarly, there may be the concept of "three-level interactions", "four-level interactions" … ….
Taking the data model of the social network shown in fig. 2 as an example, assume that the user j is located in the set S, and that there is bidirectional direct interaction between the node i corresponding to the user i and the node j corresponding to the user j, where the total weight between the user i and the user j is W (i, j) +w (j, i); assuming that a user p is located in the secondary interaction user set of the user i, bidirectional direct interaction exists between a node p corresponding to the user p and a node j corresponding to the user j, and the total weight between the user p and the user j is W (j, p) +W (p, j); assuming that the user m is neither located in the set S nor in the secondary interaction user set of the user i, bidirectional direct interaction exists between a node m corresponding to the user m and a node p corresponding to the user p, and the total weight between the user m and the user p is W (p, m) +w (m, p);
defining an attenuation factor rho epsilon (0, 1), and then friend interaction index Inter (i, p) between the user i and the user p is as follows:
Inter(i,p)＝(W(i,j)+W(j,i))*1+(W(j,p)+W(p,j))*ρ；
and, friend interaction index Inter (i, m) between user i and user m is:
Inter(i,m)＝(W(i,j)+W(j,i))*1+(W(j,p)+W(p,j))*ρ+(W(p,m)+W(m,p))*ρ 2 。
in the data model of the social network shown in fig. 2, if there are multiple paths between two user nodes, the shortest path (i.e., the shortest path) is used to calculate the friend interaction index Inter. If there is no path between two user nodes, the friend interaction index inter=0.
In the embodiment of the application, each time one level of interaction is added, the attenuation factor is multiplied once. It may be agreed that the "n-level interaction" relationship is not exceeded at most, such as n=3, because if two nodes are far apart, even if a certain path exists between them, the actual meaning of the friend interaction index Inter is not great.
(4) In a preferred embodiment, in the step 102, if the social network data mining operation further includes community discovery and friend recommendation based on the friend interaction index, the method includes:
acquiring users with friend interaction indexes which exceed a specified threshold beta with the user i, and forming a user community of the user i; the user community of the user i represents a user community with the strongest interaction relationship with the user i and the highest social affinity degree, namely a community taking the user i as a center; this may be used as a "community discovery" algorithm.
And recommending other users which are not established as friends with the user i to the user i as friends if the other users which are not established as friends with the user i exist in the community which takes the user i as a center.
Social circle mining is a very typical and popular research task in current social network research, commonly referred to as "community discovery. Many algorithms have been proposed by the academia to solve this problem, and they can be broadly divided into two major categories: a "single community" method and a "multiple communities" method. The "single community" method, that is, a node in a network structure can only belong to a certain community, and is not allowed to belong to a plurality of communities. The "multiple communities" approach allows users to be simultaneously affiliated with multiple communities.
It is apparent that the above-described "community discovery" algorithm belongs to the "multiple communities" approach, since user i may also be present in communities centered on another user j.
(5) In a preferred embodiment, in step 102, if the social network data mining operation further includes personalized information recommendation, the method includes:
in the community centering on the user i generated in the step (4), the target objects (such as the push text in the microblog, the posts in the social network site, the video content in the live network site and the like) which are recently accessed by the users in the community are pushed and recommended to the user i. As a method of personalized information recommendation. Thus, two users have more common topics when interacting in the future, and two users with frequent interaction generally have similar interest preferences more easily.
It can be seen that, in the method described in fig. 1, a data model of the social network may be constructed based on the interaction relationship between any two users in the SNS; and based on the data model, social network data mining operations including but not limited to social network visual presentation, hot spot character discovery, friend interaction index measurement, community discovery based on friend interaction indexes, friend recommendation, personalized information recommendation and the like are executed, so that social network data can be more fully mined, and SNS has wider application prospects and market space.
Referring to fig. 3, fig. 3 is a schematic structural diagram of an SNS-based social network data mining system according to an embodiment of the present application. Wherein the SNS-based social network data mining system shown in fig. 3 is used to implement the functions of the service device in the previous embodiments. As shown in fig. 3, the SNS-based social network data mining system may include:
the model building unit 301 is configured to build a data model of the social network based on an interaction relationship between any two users in the SNS;
a data mining unit 302, configured to perform a social network data mining operation based on the data model;
the social network data mining operation includes, but is not limited to, social network visual presentation, hot person discovery, friend interaction index measurement, community discovery based on friend interaction index, friend recommendation and personalized information recommendation.
In some embodiments, each user in the SNS corresponds to a node, when the user i actively sends an online interaction to the user j, the node i corresponding to the user i points to the edge of the node j corresponding to the user j, the weight W (i, j) of the edge is at least related to three factors including the interaction type, the interaction frequency and the interaction time, and the weight W (i, j) of the edge represents the social intimacy degree between the two users of the user i and the user j.
As a preferred embodiment, the model building unit 301 is specifically configured to:
defining that a user i actively sends weights V (i, j) corresponding to different interaction types to a user j in an SNS network as follows:
V(i,j)∈[1,2,3,4,5]
wherein the value of V (i, j) is an integer between 1 and 5, and the larger the value of V (i, j) is, the higher the social affinity between the user i and the user j is;
the factor TF (t) defining the interaction time is as follows:
wherein t: represents the kth timeThe month of interaction; t (T) 1 Representing the month; t (T) 0 Representing the time start month; the numerator and the denominator are added with 1 respectively to prevent the situation that the numerator or the denominator is equal to 0; the later the interaction time, the stronger the interaction timeliness, and the larger the factor TF (t) of the interaction timeliness;
defining the weight w for user i to actively initiate the kth interaction to user j k (i, j, t) is as follows:
w k (i,j,t)＝V(i,j)×TF(t)
define the total weight W (i, j) that user i actively initiates all interactions to user j as follows:
k: the method comprises the steps that a user i actively initiates K interactions to a user j, namely the interaction frequency; wherein K is greater than or equal to K.
Based on the total weight W (i, j) of all interactions initiated by the user i to the user j in the SNS, a weighted directed social network diagram is constructed and used as a data model of the social network.
(1) As a preferred embodiment, if the social network data mining operation includes a social network visual presentation, the data mining unit 302 includes a social network visual presentation unit, where the social network visual presentation unit is specifically configured to:
representing the difference of the weights of the edges in the data model by the difference of the lengths, the thicknesses or the colors of the edges; wherein the shorter the length of the edge, the greater the weight of the edge; the thicker the edge, the greater the weight representing the edge; the darker the color of the edge, the greater the weight representing the edge;
and, representing the liveness or popularity of the user with the size or color of the nodes being different; wherein a larger node represents a more active or popular representation of a user; the darker the node indicates the more active or popular the user.
(2) As a preferred embodiment, if the social network data mining operation further includes a hotspot persona discovery, the data mining unit 302 further includes a hotspot persona discovery unit, where the hotspot persona discovery unit is specifically configured to:
mining hot spot figures in the SNS network according to the entering degree and the exiting degree of each node;
Wherein, the ingress degree indecree (i) of the node i: defining the sum of all the weights of the edges pointing to the node i;
wherein, the outbound degree outdepth (i) of the node i: defining as the sum of the weights of all edges issued from node i;
the higher the outbound degree Outdegree (i) of the node i is, the more active the user i corresponding to the node i is in the social relationship; the higher the incoming degree Inregre (i) of the node i is, the higher the popularity of the user i corresponding to the node i is; the first few users with highest incorporations index (i) in the SNS network act as hotspot characters in the SNS network.
(3) As a preferred embodiment, if the social network data mining operation further includes a friend interaction index measurement, the data mining unit 302 further includes a friend interaction index measurement unit, where the friend interaction index measurement unit is specifically configured to:
defining a secondary interactive user set of the user i; the users with direct interaction with the user i form a set S, and all other users with direct interaction with any user in the set S form a secondary interaction user set of the user i; wherein, user j is located in the set S, there is bidirectional direct interaction between node i corresponding to user i and node j corresponding to user j, and the total weight between user i and user j is W (i, j) +w (j, i); the user p is located in the secondary interaction user set of the user i, bidirectional direct interaction exists between a node p corresponding to the user p and a node j corresponding to the user j, and the total weight between the user p and the user j is W (j, p) +W (p, j); the user m is neither located in the set S nor in the secondary interaction user set of the user i, bidirectional direct interaction exists between a node m corresponding to the user m and a node p corresponding to the user p, and the total weight between the user m and the user p is W (p, m) +W (m, p);
Defining an attenuation factor rho epsilon (0, 1), and then friend interaction index Inter (i, p) between the user i and the user p is as follows:
Inter(i,p)＝(W(i,j)+W(j,i))*1+(W(j,p)+W(p,j))*ρ
and, friend interaction index Inter (i, m) between user i and user m is:
Inter(i,m)＝(W(i,j)+W(j,i))*1+(W(j,p)+W(p,j))*ρ+(W(p,m)+W(m,p))*ρ 2 。
(4) As a preferred embodiment, if the social network data mining operation further includes community discovery and friend recommendation based on the friend interaction index, the data mining unit 302 further includes a community discovery unit and a friend recommendation unit, where:
the community discovery unit is specifically configured to obtain a user whose friend interaction index Inter with the user i exceeds a specified threshold β, so as to form a user community of the user i; the user community of the user i represents a user community with the strongest interaction relationship with the user i and the highest social affinity degree, namely a community taking the user i as a center;
the friend recommending unit is specifically configured to recommend other users that are not established as friends with the user i to the user i as friends if the other users exist in the community with the user i as centers.
(5) As a preferred embodiment, if the social network data mining operation further includes a personalized information recommendation, the data mining unit 302 further includes a personalized information recommendation unit, where the personalized information recommendation unit is specifically configured to:
And recommending the target object which is recently accessed in the website by the user in the community to the user i in the community taking the user i as the center.
Therefore, by implementing the system described in fig. 3, social network data can be more fully mined, so that the SNS has a wider application prospect and market space.
Referring to fig. 4, fig. 4 is a schematic structural diagram of a service device according to an embodiment of the present application. The service device shown in fig. 4 may include the SNS-based social network data mining system described in the previous embodiment. By implementing the service equipment described in fig. 4, social network data can be more fully mined, so that the SNS has wider application prospect and market space
The embodiments of the present invention have been described above with reference to the accompanying drawings, but the present invention is not limited to the above-described embodiments, which are merely illustrative and not restrictive, and many forms may be made by those having ordinary skill in the art without departing from the spirit of the present invention and the scope of the claims, which are to be protected by the present invention.
Claims (11)
1. An SNS-based social network data mining method, comprising:
Based on the interaction relation between any two users in the SNS, constructing a data model of the social network;
based on the data model, performing social network data mining operations;
the social network data mining operation comprises, but is not limited to, social network visual display, hot character discovery, friend interaction index measurement, community discovery based on friend interaction index, friend recommendation and personalized information recommendation;
each user in the SNS corresponds to a node, when the user i actively transmits one-time online interaction to the user j, the node i corresponding to the user i points to the edge of the node j corresponding to the user j, the weight W (i, j) of the edge is at least related to three factors of interaction type, interaction frequency and interaction timeliness, and the weight W (i, j) of the edge represents the social intimacy degree between the two users of the user i and the user j;
based on the interaction relation between any two users in the SNS, a data model of the social network is constructed, and the method comprises the following steps:
defining that a user i actively sends weights V (i, j) corresponding to different interaction types to a user j in an SNS network as follows:
V(i，j)∈[1，2，3，4，5]
wherein the value of V (i, j) is an integer between 1 and 5, and the larger the value of V (i, j) is, the higher the social affinity between the user i and the user j is;
The factor TF (t) defining the interaction time is as follows:
wherein t: representing the month of occurrence of the kth interaction; t (T) 1 Representing the month; t (T) 0 Representing the time start month; the numerator and the denominator are added with 1 respectively to prevent the situation that the numerator or the denominator is equal to 0; the later the interaction time, the stronger the interaction timeliness, and the larger the factor TF (t) of the interaction timeliness;
defining the weight w for user i to actively initiate the kth interaction to user j k (i, j, t) is as follows:
w k (i，j，t)＝V(i，j)×TF(t)
define the total weight W (i, j) that user i actively initiates all interactions to user j as follows:
k: the method comprises the steps that a user i actively initiates K interactions to a user j, namely the interaction frequency is higher than or equal to K;
based on the total weight W (i, j) of all interactions actively initiated by a user i to a user j in the SNS, constructing a weighted directed social network diagram serving as a data model of the social network;
if the social network data mining operation further includes a friend interaction index metric, the method further includes:
defining a secondary interactive user set of the user i; the users with direct interaction with the user i form a set S, and all other users with direct interaction with any user in the set S form a secondary interaction user set of the user i; wherein, user j is located in the set S, there is bidirectional direct interaction between node i corresponding to user i and node j corresponding to user j, and the total weight between user i and user j is W (i, j) +w (j, i); the user p is located in the secondary interaction user set of the user i, bidirectional direct interaction exists between a node p corresponding to the user p and a node j corresponding to the user j, and the total weight between the user p and the user j is W (j, p) +W (p, j); the user m is neither located in the set S nor in the secondary interaction user set of the user i, bidirectional direct interaction exists between a node m corresponding to the user m and a node p corresponding to the user p, and the total weight between the user m and the user p is W (p, m) +W (m, p);
Defining an attenuation factor rho epsilon (0, 1), and then friend interaction index Inter (i, p) between the user i and the user p is as follows:
Inter(i，p)＝(W(i，j)+W(j，i))*1+(W(j，p)+W(p，j))*ρ；
and, friend interaction index Inter (i, m) between user i and user m is:
Inter(i，m)＝(W(i，j)+W(j，i))*1+(W(j，p)+W(p，j))*ρ+(W(p，m)+W(m，p))*ρ 2 ；
and each time one level of interaction is added, the attenuation factor rho is multiplied once.
2. The method of claim 1, wherein if the social network data mining operation includes social network visual presentation, the performing the social network data mining operation based on the data model comprises:
representing the difference of the weights of the edges in the data model by the difference of the lengths, the thicknesses or the colors of the edges; wherein the shorter the length of the edge, the greater the weight of the edge; the thicker the edge, the greater the weight representing the edge; the darker the color of the edge, the greater the weight representing the edge;
and, representing the liveness or popularity of the user with the size or color of the nodes being different; wherein a larger node indicates a more active or popular user; the darker the node indicates the more active or popular the user.
3. The method of social networking data mining in accordance with claim 2, wherein if the social networking data mining operation further includes hotspot persona discovery, the method further comprises:
Mining hot spot figures in the SNS network according to the entering degree and the exiting degree of each node;
wherein, the ingress degree indecree (i) of the node i: defining the sum of all the weights of the edges pointing to the node i;
wherein, the outbound degree outdepth (i) of the node i: defining as the sum of the weights of all edges issued from node i;
the higher the outbound degree Outdegree (i) of the node i is, the more active the user i corresponding to the node i is in the social relationship; the higher the incoming degree Inregre (i) of the node i is, the higher the popularity of the user i corresponding to the node i is; the first few users with highest incorporations index (i) in the SNS network act as hotspot characters in the SNS network.
4. The method of claim 1, wherein if the social network data mining operation further includes community discovery and friend recommendation based on friend interaction index, the method comprises:
acquiring users with friend interaction indexes which exceed a specified threshold beta with the user i, and forming a user community of the user i; the user community of the user i represents a user community with the strongest interaction relationship with the user i and the highest social affinity degree, namely a community taking the user i as a center;
And recommending other users which are not established as friends with the user i to the user i as friends if the other users which are not established as friends with the user i exist in the community which takes the user i as a center.
5. The method of social networking data mining recited in claim 4, wherein if the social networking data mining operation further includes personalized information recommendation, the method comprises:
and recommending the target object which is recently accessed in the website by the user in the community to the user i in the community taking the user i as the center.
6. An SNS-based social networking data mining system, comprising:
the model building unit is used for building a data model of the social network based on the interaction relationship between any two users in the SNS;
the data mining unit is used for performing social network data mining operation based on the data model;
the social network data mining operation comprises, but is not limited to, social network visual display, hot character discovery, friend interaction index measurement, community discovery based on friend interaction index, friend recommendation and personalized information recommendation;
Each user in the SNS corresponds to a node, when the user i actively transmits one-time online interaction to the user j, the node i corresponding to the user i points to the edge of the node j corresponding to the user j, the weight W (i, j) of the edge is at least related to three factors of interaction type, interaction frequency and interaction timeliness, and the weight W (i, j) of the edge represents the social intimacy degree between the two users of the user i and the user j;
the model building unit is specifically used for:
defining that a user i actively sends weights V (i, j) corresponding to different interaction types to a user j in an SNS network as follows:
V(i，j)∈[1，2，3，4，5]
wherein the value of V (i, j) is an integer between 1 and 5, and the larger the value of V (i, j) is, the higher the social affinity between the user i and the user j is;
the factor TF (t) defining the interaction time is as follows:
wherein t: representing the month of occurrence of the kth interaction; t (T) 1 Representing the month; t (T) 0 Representing the time start month; the numerator and the denominator are added with 1 respectively to prevent the situation that the numerator or the denominator is equal to 0; the later the interaction time, the stronger the interaction timeliness, and the larger the factor TF (t) of the interaction timeliness;
defining the weight w for user i to actively initiate the kth interaction to user j k (i, j, t) is as follows:
w k (i，j，t)＝V(i，j)×TF(t)
Define the total weight W (i, j) that user i actively initiates all interactions to user j as follows:
k: the method comprises the steps that a user i actively initiates K interactions to a user j, namely the interaction frequency is higher than or equal to K;
based on the total weight W (i, j) of all interactions actively initiated by a user i to a user j in the SNS, constructing a weighted directed social network diagram serving as a data model of the social network;
if the social network data mining operation further includes a friend interaction index measurement unit, the data mining unit further includes a friend interaction index measurement unit, where the friend interaction index measurement unit is specifically configured to:
defining a secondary interactive user set of the user i; the users with direct interaction with the user i form a set S, and all other users with direct interaction with any user in the set S form a secondary interaction user set of the user i; wherein, user j is located in the set S, there is bidirectional direct interaction between node i corresponding to user i and node j corresponding to user j, and the total weight between user i and user j is W (i, j) +w (j, i); the user p is located in the secondary interaction user set of the user i, bidirectional direct interaction exists between a node p corresponding to the user p and a node j corresponding to the user j, and the total weight between the user p and the user j is W (j, p) +W (p, j); the user m is neither located in the set S nor in the secondary interaction user set of the user i, bidirectional direct interaction exists between a node m corresponding to the user m and a node p corresponding to the user p, and the total weight between the user m and the user p is W (p, m) +W (m, p);
Defining an attenuation factor rho epsilon (0, 1), and then friend interaction index Inter (i, p) between the user i and the user p is as follows:
Inter(i，p)＝(W(i，j)+W(j，i))*1+(W(j，p)+W(p，j))*ρ
and, friend interaction index Inter (i, m) between user i and user m is:
Inter(i，m)＝(W(i，j)+W(j，i))*1+(W(j，p)+W(p，j))*ρ+(W(p，m)+W(m，p))*ρ 2 ；
and each time one level of interaction is added, the attenuation factor rho is multiplied once.
7. The social network data mining system of claim 6, wherein if the social network data mining operation includes a social network visual presentation, the data mining unit includes a social network visual presentation unit, the social network visual presentation unit being specifically configured to:
representing the difference of the weights of the edges in the data model by the difference of the lengths, the thicknesses or the colors of the edges; wherein the shorter the length of the edge, the greater the weight of the edge; the thicker the edge, the greater the weight representing the edge; the darker the color of the edge, the greater the weight representing the edge;
and, representing the liveness or popularity of the user with the size or color of the nodes being different; wherein a larger node indicates a more active or popular user; the darker the node indicates the more active or popular the user.
8. The social network data mining system recited in claim 7, wherein if the social network data mining operation further includes a hotspot persona discovery, the data mining unit further includes a hotspot persona discovery unit, the hotspot persona discovery unit is specifically configured to:
Mining hot spot figures in the SNS network according to the entering degree and the exiting degree of each node;
wherein, the ingress degree indecree (i) of the node i: defining the sum of all the weights of the edges pointing to the node i;
wherein, the outbound degree outdepth (i) of the node i: defining as the sum of the weights of all edges issued from node i;
the higher the outbound degree Outdegree (i) of the node i is, the more active the user i corresponding to the node i is in the social relationship; the higher the incoming degree Inregre (i) of the node i is, the higher the popularity of the user i corresponding to the node i is; the first few users with highest incorporations index (i) in the SNS network act as hotspot characters in the SNS network.
9. The social network data mining system of claim 6, wherein if the social network data mining operation further includes community discovery and friend recommendation based on a friend interaction index, the data mining unit further includes a community discovery unit and a friend recommendation unit, wherein:
the community discovery unit is specifically configured to obtain a user whose friend interaction index Inter with the user i exceeds a specified threshold β, so as to form a user community of the user i; the user community of the user i represents a user community with the strongest interaction relationship with the user i and the highest social affinity degree, namely a community taking the user i as a center;
The friend recommending unit is specifically configured to recommend other users that are not established as friends with the user i to the user i as friends if the other users exist in the community with the user i as centers.
10. The social networking data mining system of claim 9, wherein if the social networking data mining operation further comprises a personalized information recommendation, the data mining unit further comprises a personalized information recommendation unit, the personalized information recommendation unit being specifically configured to:
and recommending the target object which is recently accessed in the website by the user in the community to the user i in the community taking the user i as the center.
11. A service device, characterized in that the service device comprises the SNS-based social network data mining system according to any one of claims 6 to 10.
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