CN104966240A - Edge betweenness based social network rumor control method and system - Google Patents

Abstract

The invention relates to an edge betweenness based social network rumor control method and system to solve the problem of how to effectively control spreading of a large amount of rumors in a social network on the premise of ensuring network communication as far as possible. The method comprises the steps of: S1, obtaining a concerned user list of N users in the social network and creating an adjacent matrix of the social network; S2, calculating the betweenness value of each edge in an edge set E of the adjacent matrix; and S3, cutting off k edges with maximum betweenness values in the edge set E and finishing social network rumor control. As the edge betweenness value represents importance of edges in the network, the more important the edges in the network is, the higher the edge betweenness value is and the higher the possibility of rumor spreading along the edges is, the rumors can be effectively controlled by cutting off the edges. In addition, as cutoff is carried out based on edge layers, compared to cutoff based on node layers in the prior art, false cutoff is avoided and network connectivity is ensured as far as possible.

Description

Based on social networks rumour control method and the system of limit betweenness

Technical field

The present invention relates to the rumour control technology field of social networks, be specifically related to a kind of social networks rumour control method based on limit betweenness and a kind of social networks rumour control system based on limit betweenness.

Background technology

Rumour, as the typical social phenomenon of one, emerges in an endless stream in each stage of social development, and constantly become the focal issue that people pay close attention to, particularly in various accident, the impact of rumour can not be underestimated.In recent years, the social network-i i-platform such as microblogging, Renren Network, micro-letter become the brand-new instrument of people's communication and message propagation, also make gossip propagation possess the dual-use function of the interpersonal communication of " point-to-point " and the mass media of " point-to-area ".

Lack supervision due to the convenience of Information issued in social networks and the information content and filter, a large amount of unreal information, rumour etc. are wantonly propagated on social networks, pollute social network environment, the stable of society and nation's security are caused and has a strong impact on.Such as, after U.S. Sang Di (Sandy) hurricane occurs, push away on spy (Twitter) and occurred succouring unfavorable, corpse network rumour all over the fields about government in a large number, cause the fear of the masses, adverse effect is caused to social stability.In addition, be also flooded with numerous network rumour in the social network sites that China is the most popular, such as, military vehicle is gone to the capital, food transmission of bleeding is viral, causes panic.Again such as, earthquake rumor, makes millions of people street corner, Shanxi take refuge; Maggot tangerine rumour, makes national oranges and tangerines seriously unsalable etc.According to 2013 " Chinese new media development report " display that the Chinese Academy of Social Sciences issues on June 25th, 2013, from 100 microblogging focus public sentiment cases in year January in January, 2012 to 2013, the ratio of deceptive information is more than 1/3.

Nature, society and the triple factor of technology make a concerted effort promote under; starting a rumour spreads the rumour just becomes a kind of public opinion campaign of normalization increasingly; all crises of natural and social aspect accumulation and risk; the new platform of medium taking social networks as representative is applied amplification; produce one and another rumour shock wave; negative effect is created to the development of the state of affairs, is easy to the irrational mood and the behavior that cause group, is unfavorable for the process of Public Crisis Events.If developed as one pleases, must cause violent " butterfly effect ", very likely cause unstable, social uneasy, the civil disturbances of the popular feeling.Therefore in the urgent need to the effective rumour control method of one, gossip propagation path can be held in time, exactly, and rumour is control effectively.

The rumour control method generally taked at present mainly contains Random Control Method, Target Control Method.Wherein, STOCHASTIC CONTROL needs to control node most in network, and the target control basis that needs to carry out multianalysis to Global Information in network controls part important node.These two kinds of control methods visible are all node aspects, the mistake on part limit can be caused to cut off, to such an extent as to have a strong impact on the connectedness of network.

Summary of the invention

Technical matters to be solved by this invention is under the prerequisite guaranteeing network connectivty as far as possible, how effectively control disseminating in a large number of rumour in social networks.

For this purpose, the present invention proposes a kind of social networks rumour control method based on limit betweenness, the method comprises:

The concern user list of N number of user in S1, acquisition social networks, the adjacency matrix G={V of described social networks is built according to the concern user list of described N number of user, E}, wherein N is pre-set user quantity, V is the user's collection be made up of described N number of user, E be by two users any in described N number of user between the limit collection that forms of limit;

S2, calculate described adjacency matrix limit collection E in the betweenness value on each bar limit;

S3, cut off the maximum k bar limit of described Bian Ji E intermediary numerical value, complete and control the rumour of social networks, wherein k is the preset value cutting off limit number.

Further, the limit that described limit is concentrated represents with following formula:

$e_{i,j}=\left\{\begin{array}{cc}0& j\∉f\left(i\right)\\ 1& j\∈f\left(i\right)\end{array}\right.$

Wherein, the concern user list that 1≤i≤N, 1≤j≤N, f (i) is user i, ^e _i,jfor the limit between user i and user j.

Further, in the limit collection E of described adjacency matrix, the betweenness value on each bar limit is calculated by following formula:

$BC\left(e_{i,j}\right)=\left\{\begin{array}{cc}0& e_{i,j}=0\\ \underset{\begin{array}{c}u,w\∈V\\ u\≠w\end{array}}{\Σ}\frac{{\mathrm{\σ}}_{uw}\left(e_{i,j}\right)}{{\mathrm{\σ}}_{uw}}& e_{i,j}=1\end{array}\right.$

Wherein, BC (e _i,j) be the betweenness value on the limit between user i and user j, 1≤u≤N, 1≤w≤N, σ _uwfor the shortest path total quantity between user u and user w, σ _uw(e _i,j) in all shortest paths between user u and user w through limit ^e _i,jnumber of paths.

Further, described S3 specifically comprises:

Betweenness value according to each bar limit sorts to each bar limit;

The maximum k bar limit of described Bian Ji E intermediary numerical value is cut off according to ranking results.

Present invention also offers a kind of social networks rumour control system based on limit betweenness, this system comprises:

Adjacency matrix builds module, for obtaining the concern user list of N number of user in social networks, the adjacency matrix G={V of described social networks is built according to the concern user list of described N number of user, E}, wherein N is pre-set user quantity, V is the user's collection be made up of described N number of user, E be by two users any in described N number of user between the limit collection that forms of limit;

Jie's Numerical Simulation Module, for calculate described adjacency matrix limit collection E in the betweenness value on each bar limit;

Limit cuts off module, for cutting off the maximum k bar limit of described Bian Ji E intermediary numerical value, completing and controlling the rumour of social networks, and wherein k is the preset value cutting off limit number.

Further, the limit that described limit is concentrated represents with following formula:

$e_{i,j}=\left\{\begin{array}{cc}0& j\∉f\left(i\right)\\ 1& j\∈f\left(i\right)\end{array}\right.$

Wherein, the concern user list that 1≤i≤N, 1≤j≤N, f (i) is user i, ^e _i,jfor the limit between user i and user j.

Further, described Jie's Numerical Simulation Module calculates the betweenness value on each bar limit in the limit collection E of described adjacency matrix by following formula:

$BC\left(e_{i,j}\right)=\left\{\begin{array}{cc}0& e_{i,j}=0\\ \underset{\begin{array}{c}u,w\∈V\\ u\≠w\end{array}}{\Σ}\frac{{\mathrm{\σ}}_{uw}\left(e_{i,j}\right)}{{\mathrm{\σ}}_{uw}}& e_{i,j}=1\end{array}\right.$

Wherein, BC (e _i,j) be the betweenness value on the limit between user i and user j, 1≤u≤N, 1≤w≤N, σ _uwfor the shortest path total quantity between user u and user w, σ _uw(e _i,j) in all shortest paths between user u and user w through limit ^e _i,jnumber of paths.

Further, described limit cut-out module comprises:

Rank submodule, sorts to each bar limit for the betweenness value according to each bar limit;

Cut off submodule, for cutting off the maximum k bar limit of described Bian Ji E intermediary numerical value according to ranking results.

Jie's numerical values recited on limit characterizes limit importance in a network, and the betweenness value on limit is larger, and limit is more important in a network, the possibility that rumour is propagated along this limit is larger, therefore the present invention calculates the betweenness value on limit, and cuts off this limit according to its betweenness value, can realize the effective control to rumour.Again because the present invention is the cut-out carried out based on boundary layer face, relative in prior art based on the cut-out of node aspect, avoid as far as possible and cut off by mistake, therefore ensure that the connectedness of network as much as possible.

Accompanying drawing explanation

Can understanding the features and advantages of the present invention clearly by reference to accompanying drawing, accompanying drawing is schematic and should not be construed as and carry out any restriction to the present invention, in the accompanying drawings:

Fig. 1 shows the schematic flow sheet of the social networks rumour control method that the present invention is based on limit betweenness;

Fig. 2 shows the structured flowchart of the social networks rumour control system that the present invention is based on limit betweenness.

Embodiment

Below in conjunction with accompanying drawing, embodiments of the present invention is described in detail.

The invention provides a kind of social networks rumour control method based on limit betweenness, as shown in Figure 1, the method comprises:

The concern user list of N number of user in S1, acquisition social networks, the adjacency matrix G={V of described social networks is built according to the concern user list of described N number of user, E}, wherein N is pre-set user quantity, V is the user's collection be made up of described N number of user, E be by two users any in described N number of user between the limit collection that forms of limit;

S2, calculate described adjacency matrix limit collection E in the betweenness value on each bar limit;

S3, cut off the maximum k bar limit of described Bian Ji E intermediary numerical value, complete and control the rumour of social networks, wherein k is the preset value cutting off limit number.

Described social networks can be microblogging, Renren Network, micro-letter etc.

The concern user list of certain user is the list of the user that this user pays close attention to.The concern user list of user i can represent with f (i), if another user j ∈ f (i), then represents that user i pays close attention to user j.If then represent that user i does not pay close attention to user j.

The adjacency matrix built comprises user's collection and limit collection, and to represent the concern relation in N number of user between any two users, so-called concern relation limit represents.

The betweenness on certain limit and limit betweenness are defined as the ratio accounting for all shortest path quantity in all shortest paths through the quantity of the shortest path on this limit, and visible edge betweenness indicates the possibility of information flow through this limit.Therefore limit betweenness can be utilized to determine the most probable path of gossip propagation, and limit betweenness is larger, illustrates that this limit is more important in a network, larger to the propagation effect of rumour in the entire network.Therefore initially cut off this limit in the propagation of rumour, the object controlling gossip propagation can be reached.Again owing to the invention provides the limit cutting-off method based on boundary layer face, the mistake avoiding the part limit that the limit cutting-off method due to node aspect causes is cut off, so the present invention is relative to based on the Random Control Method of node aspect and Target Control Method, decrease as much as possible and cut off by mistake, thus the network connectivty guaranteed as far as possible.

Concrete can be represented by the formula:

$e_{i,j}=\left\{\begin{array}{cc}0& j\∉f\left(i\right)\\ 1& j\∈f\left(i\right)\end{array}\right.$

Wherein, i, j are the user that described user concentrates, the concern user list that 1≤i≤N, 1≤j≤N, f (i) is user i, ^e _i,jfor the limit between user i and user j.If user i does not pay close attention to user j, then the limit between user i and user j is 0; If user i has paid close attention to user j, then the limit between user i and user j has been 1.

Further, in the limit collection E of described adjacency matrix, the betweenness value on each bar limit is calculated by following formula:

$BC\left(e_{i,j}\right)=\left\{\begin{array}{cc}0& e_{i,j}=0\\ \underset{\begin{array}{c}u,w\∈V\\ u\≠w\end{array}}{\Σ}\frac{{\mathrm{\σ}}_{uw}\left(e_{i,j}\right)}{{\mathrm{\σ}}_{uw}}& e_{i,j}=1\end{array}\right.$

Wherein, BC (e _i,j) be the betweenness value on the limit between user i and user j, u, w are the user that described user concentrates, 1≤u≤N, 1≤w≤N, σ _uwfor the shortest path sum between user u and user w, σ _uw(e _i,j) in all shortest paths between user u and user w through limit ^e _i,jnumber of path.

Further, described S3 specifically can realize in the following ways:

Betweenness value according to each bar limit sorts to each bar limit;

The maximum k bar limit of described Bian Ji E intermediary numerical value is cut off according to ranking results.

Maximum k bar limit is worth, so the betweenness value on the limit of each bar cut-out is greater than the betweenness value on all limits do not cut off in the collection E of limit owing to having cut off betweenness.

By sorting to each bar limit, if to little sortord, rank is more forward from large by its betweenness value, limit is more important in a network, cuts off the limit of the most forward predetermined number of rank.If by its betweenness value sortord from small to large, more rearward, limit is more important in a network for rank, cut off the limit of rank predetermined number the most rearward.Therefore, by the mode of importance rank, the work of choosing on the limit for cutting off is become and simply, easily realizes.

Present invention also offers a kind of social networks rumour control system 100 based on limit betweenness, as shown in Figure 2, this system comprises:

Adjacency matrix builds module 101, for obtaining the concern user list of N number of user in social networks, the adjacency matrix G={V of described social networks is built according to the concern user list of described N number of user, E}, wherein N is pre-set user quantity, V is the user's collection be made up of described N number of user, E be by two users any in described N number of user between the limit collection that forms of limit;

Jie's Numerical Simulation Module 102, for calculate described adjacency matrix limit collection E in the betweenness value on each bar limit;

Limit cuts off module 103, for cutting off the maximum k bar limit of described Bian Ji E intermediary numerical value, completing and controlling the rumour of social networks, and wherein k is the preset value cutting off limit number.

Further, the limit that described limit is concentrated can represent with following formula:

$e_{i,j}=\left\{\begin{array}{cc}0& j\∉f\left(i\right)\\ 1& j\∈f\left(i\right)\end{array}\right.$

Wherein, i, j are the user that described user concentrates, the concern user list that 1≤i≤N, 1≤j≤N, f (i) is user i, ^e _i,jfor the limit between user i and user j.

Further, described Jie's Numerical Simulation Module can calculate the betweenness value on each bar limit in the limit collection E of described adjacency matrix by following formula:

$BC\left(e_{i,j}\right)=\left\{\begin{array}{cc}0& e_{i,j}=0\\ \underset{\begin{array}{c}u,w\∈V\\ u\≠w\end{array}}{\Σ}\frac{{\mathrm{\σ}}_{uw}\left(e_{i,j}\right)}{{\mathrm{\σ}}_{uw}}& e_{i,j}=1\end{array}\right.$

Wherein, BC (e _i,j) be the betweenness value on the limit between user i and user j, u, w are the user that described user concentrates, 1≤u≤N, 1≤w≤N, σ _uwfor the shortest path sum between user u and user w, σ _uw(e _i,j) in all shortest paths between user u and user w through limit ^e _i,jnumber of path.

Further, described limit cut-out module can comprise:

Rank submodule, sorts to each bar limit for the betweenness value according to each bar limit;

Cut off submodule, for cutting off the maximum k bar limit of described Bian Ji E intermediary numerical value according to ranking results.

Due to the function structure module that above-mentioned rumour control system is above-mentioned rumour control method, what it was relevant illustrate please refer to appropriate section in rumour control method with beneficial effect, does not repeat them here.

In sum, the present invention is based on social networks rumour control method and the system of limit betweenness, according to based on the concern relation in social networks between user, build adjacency matrix.Then according to the most probable path of the betweenness value determination gossip propagation on limit, and carry out rumour control according to this result, initially just the most probable travel path of rumour can be grasped at gossip propagation by the method, rumour is controlled to be accurate to limit, namely the relation between user, thus ensure that the promptness that rumour controls and accuracy, effectively reduce the pressure from public opinion because rumour control causes.Meanwhile, relative to the cut-out based on node aspect, decrease and cut rate by mistake, ensure that the connectedness of network as far as possible.

Although describe embodiments of the present invention by reference to the accompanying drawings, but those skilled in the art can make various modifications and variations without departing from the spirit and scope of the present invention, such amendment and modification all fall into by within claims limited range.

Claims (8)

1., based on a social networks rumour control method for limit betweenness, it is characterized in that, comprising:

The concern user list of N number of user in S1, acquisition social networks, the adjacency matrix G={V of described social networks is built according to the concern user list of described N number of user, E}, wherein N is pre-set user quantity, V is the user's collection be made up of described N number of user, E be by two users any in described N number of user between the limit collection that forms of limit;

S2, calculate described adjacency matrix limit collection E in the betweenness value on each bar limit;

S3, cut off the maximum k bar limit of described Bian Ji E intermediary numerical value, complete and control the rumour of social networks, wherein k is the preset value cutting off limit number.

2. rumour control method according to claim 1, is characterized in that, the limit that described limit is concentrated represents with following formula:

$e_{i,j}=\left\{\begin{array}{cc}0& j\∉f\left(i\right)\\ 1& j\∈f\left(i\right)\end{array}\right.$

Wherein, the concern user list that 1≤i≤N, 1≤j≤N, f (i) is user i, e _i,jfor the limit between user i and user j.

3. rumour control method according to claim 2, is characterized in that, in the limit collection E of described adjacency matrix, the betweenness value on each bar limit is calculated by following formula:

$BC\left(e_{i,j}\right)=\left\{\begin{array}{cc}0& e_{i,j}=0\\ \underset{\begin{array}{c}u,w\∈V\\ u\≠w\end{array}}{\Σ}\frac{{\mathrm{\σ}}_{uw}\left(e_{i,j}\right)}{{\mathrm{\σ}}_{uw}}& e_{i,j}=1\end{array}\right.$

Wherein, BC (e _i,j) be the betweenness value on the limit between user i and user j, 1≤u≤N, 1≤w≤N, σ _uwfor the shortest path total quantity between user u and user w, σ _uw(e _i,j) in all shortest paths between user u and user w through limit e _i,jnumber of paths.

4. rumour control method according to claim 1, is characterized in that, described S3 specifically comprises:

Betweenness value according to each bar limit sorts to each bar limit;

The maximum k bar limit of described Bian Ji E intermediary numerical value is cut off according to ranking results.

5., based on a social networks rumour control system for limit betweenness, it is characterized in that, comprising:

Adjacency matrix builds module, for obtaining the concern user list of N number of user in social networks, the adjacency matrix G={V of described social networks is built according to the concern user list of described N number of user, E}, wherein N is pre-set user quantity, V is the user's collection be made up of described N number of user, E be by two users any in described N number of user between the limit collection that forms of limit;

Jie's Numerical Simulation Module, for calculate described adjacency matrix limit collection E in the betweenness value on each bar limit;

Limit cuts off module, for cutting off the maximum k bar limit of described Bian Ji E intermediary numerical value, completing and controlling the rumour of social networks, and wherein k is the preset value cutting off limit number.

6. rumour control system according to claim 5, is characterized in that, the limit that described limit is concentrated represents with following formula:

$e_{i,j}=\left\{\begin{array}{cc}0& j\∉f\left(i\right)\\ 1& j\∈f\left(i\right)\end{array}\right.$

Wherein, the concern user list that 1≤i≤N, 1≤j≤N, f (i) is user i, e _i,jfor the limit between user i and user j.

7. rumour control system according to claim 6, is characterized in that, described Jie's Numerical Simulation Module calculates the betweenness value on each bar limit in the limit collection E of described adjacency matrix by following formula:

$BC\left(e_{i,j}\right)=\left\{\begin{array}{cc}0& e_{i,j}=0\\ \underset{\begin{array}{c}u,w\∈V\\ u\≠w\end{array}}{\Σ}\frac{{\mathrm{\σ}}_{uw}\left(e_{i,j}\right)}{{\mathrm{\σ}}_{uw}}& e_{i,j}=1\end{array}\right.$

Wherein, BC (e _i,j) be the betweenness value on the limit between user i and user j, 1≤u≤N, 1≤w≤N, σ _uwfor the shortest path total quantity between user u and user w, σ _uw(e _i,j) in all shortest paths between user u and user w through limit e _i,jnumber of paths.

8. rumour control system according to claim 5, is characterized in that, described limit cuts off module and comprises:

Rank submodule, sorts to each bar limit for the betweenness value according to each bar limit;

Cut off submodule, for cutting off the maximum k bar limit of described Bian Ji E intermediary numerical value according to ranking results.