CN108092832A - A kind of social networks Virus Info suppressing method and system - Google Patents

Abstract

The invention discloses a kind of social networks Virus Info suppressing method and system, including：Step (1)：Establish social networks；Step (2)：Determine whether that virus is propagated in social networks；If so, then enter step (3)；If it is not, continue to judge；Step (3)：Improved SIQR models are built, the parameter in model is set according to social networks viral transmission situation；Step (4)：Using the parameter in improved SIQR models, the equalization point of HIV suppression is calculated；Step (5)：According to equalization point, HIV suppression strategy is provided, virus is inhibited according to HIV suppression strategy.During virus outbreak, it can take a variety of effective measures will be within its transmission controe to minimum zone according to influence factor and the relation of threshold value.

Description

Method and system for suppressing social network virus information

technical field

The invention relates to a method and system for suppressing social network virus information.

Background technique

With the rapid development of information technology and network technology, social network (SN) has been rapidly popularized around the world, and people can quickly and conveniently obtain various information. At the same time, bad information such as viruses, rumors, and malicious software in social networks will inevitably appear in social networks. Therefore, information dissemination in social networks has become a research hotspot.

In the research of information dissemination in social networks, the complex network dissemination dynamics model is generally used for reference. Since Kermack and McKendrick proposed the warehouse model in 1927, various infectious disease dissemination models have emerged in an endless stream. Kuperman et al. studied the propagation process of the SIR model in the small-world network; Zhang Yanchao et al. and Xiong Xiong et al. studied the information dissemination using the SIR model in social networks, and compared simulations with related models using simulation experiments; Freeman described the SIR model Applied to specific cases and predicted user behavior.

Contents of the invention

In order to solve the deficiencies in the prior art, the invention provides a social network virus information suppression method and system, which improves the SIQR model (the SIQR model is a virus propagation model, and S, I, Q, R represent four types of nodes respectively : Susceptible node S, spreading node I, isolated node Q and immune node R), using differential model analysis and simulation to improve the balance point of the model, and pointed out the effective measures to control the spread of the virus. The research and control of the law of the spread of such viruses is conducive to the development and improvement of the law of spreading dynamics on social networks.

A social network information suppression method, comprising:

Step (1): Establish a social network;

Step (2): judging whether there is a virus spreading in the social network; if so, then enter step (3); if not, then return to step (2) to continue judging;

Step (3): Build an improved SIQR model, and set the parameters in the model according to the spread of social network viruses;

Step (4): Utilize the parameter in the improved SIQR model, calculate the equilibrium point of virus suppression;

Step (5): According to the balance point, a virus suppression strategy is given, and the virus is suppressed according to the virus suppression strategy.

Further, in the step (1):

Suppose a social network includes four kinds of nodes: susceptible node S, spreading node I, isolated node Q and immune node R;

Among them, the susceptible node S can be infected by the virus, the spreading node I has been infected by the virus, and can spread the virus to neighbor nodes; the isolated node Q is a node that is forced to block the communication ability, does not have the ability to spread the virus, and cannot be infected ; Immune node R cannot be infected by the virus.

Further, in the step (3):

(301): The total number of nodes at time t in the social network remains constant K, namely:

S(t)+I(t)+Q(t)+R(t)=K;

Among them, K represents the total number of nodes, S(t) represents the number of susceptible nodes at time t, I(t) represents the number of spreading nodes at time t, Q(t) represents the number of isolated nodes at time t, and R(t) represents the number of immune nodes at time t. number of nodes;

(302): The newly added nodes are all susceptible nodes S, and the new joining rate of newly added nodes is b; the voluntary exit rate of susceptible node S is d; the probability that susceptible node S is transformed into isolated node Q by invalid isolation is m; each susceptible node S is transformed into a spreading node I with the infection rate λ and contact rate β;

(303): With the transformation of the susceptible node S, the probability that the spreading node I exits voluntarily is d, and the probability that the spreading node is forcibly isolated is α;

(304): The probability that the isolated node Q exits voluntarily is d, the probability that the isolated node Q turns into an immune node R is μ, and the probability that the isolated node cannot turn into an immune node is γ;

(305): After the isolation node Q is transformed into an immune node R, it will not be infected again, and the probability of the immune node R's voluntary exit is d.

Further, the equilibrium point R ₀ of virus suppression in the step (4):

Further, in the step (5),

When R ₀ <1, the virus will disappear; when R ₀ >1, the virus will spread;

When the virus is prevalent, by increasing the invalid isolation rate m and the mandatory isolation rate α, the value of R ₀ can be reduced, so as to suppress the virus; or,

When the virus is prevalent, by reducing the transmission rate λ and the contact rate β, the reduction of the R ₀ value is achieved, thereby achieving the suppression of the virus; or,

When the virus is prevalent, by increasing the invalid isolation rate m and the mandatory isolation rate α, at the same time, reducing the transmission rate λ and the contact rate β, the value of R ₀ can be reduced, so as to suppress the virus.

The virus suppression strategy also includes:

The susceptible node S is invalidly isolated with probability m because of its contact with the propagating node I;

Judging whether the invalidly isolated node is transformed into immune node R, if so, end;

If it is not transformed into an immune node R, it is further judged whether the invalidly isolated node voluntarily exits, and if it voluntarily exits, it ends; if it does not voluntarily exit, it is forced to exit.

The node may be: a server or a mobile terminal.

The quarantined nodes Q include quarantined nodes that have been infected by the virus and quarantined nodes that have not been infected by the virus.

The invalid isolation rate means the ratio of the number of nodes not infected by the virus to the number of all isolated nodes among the isolated nodes;

The ineffective immunity rate refers to the ratio of the number of nodes that fail to convert to the number of all nodes participating in the conversion during the process of transforming an isolated node into an immune node.

A social network information suppression system, comprising: a memory, a processor, and computer instructions stored in the memory and run on the processor. When the computer instructions are run by the processor, the steps described in any one of the above methods are completed.

Compared with prior art, the beneficial effect of the present invention is:

When viruses or rumors break out in social networks, isolation strategies are generally adopted. However, in this strategy, there are often problems of invalid isolation, and nodes always maintain liquidity during this process. For these problems, the present invention has improved SIQR model, has introduced parameters such as new join rate, voluntary withdrawal rate, invalid isolation rate, utilizes complex network mean field theory to have studied the existence and the stability problem of equilibrium point, has revealed virus (rumour) ), the relationship between factors such as propagation rate and mandatory isolation rate, and the reliability is verified through simulation experiments. Experimental results show that virus transmission is determined by a threshold, and the mandatory isolation rate is negatively correlated with this threshold. When a virus breaks out, according to the relationship between the influencing factors and the threshold, a variety of effective measures can be taken to control its spread to a minimum.

Description of drawings

The accompanying drawings constituting a part of the present application are used to provide further understanding of the present application, and the schematic embodiments and descriptions of the present application are used to explain the present application, and do not constitute improper limitations to the present application.

Fig. 1 is a flowchart of the present invention;

Figure 2 is an evolution diagram of the state of an invalid isolated node;

Fig. 3 is the immune process diagram of propagation node;

Fig. 4 is the propagation mechanism diagram of improving SIQR model;

Figure 5 is the change curve of the proportion of susceptible nodes corresponding to the disease-free equilibrium point;

Figure 6 is a disease-free equilibrium point corresponding to the transmission node proportion change curve;

Fig. 7 is the change curve of the proportion of susceptible nodes corresponding to the endemic equilibrium point;

Fig. 8 is the change curve of the ratio of transmission nodes corresponding to the endemic equilibrium point.

Detailed ways

It should be pointed out that the following detailed description is exemplary and intended to provide further explanation to the present application. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

As shown in Figure 1, a social network information suppression method includes:

Step (1): Establish a social network;

Suppose a social network includes four kinds of nodes: susceptible node S, spreading node I, isolated node Q and immune node R;

Among them, the susceptible node S can be infected by the virus, the spreading node I can spread the virus to the neighbor nodes, and the isolation node

Q does not have the ability to spread viruses and cannot be infected, and immune node R cannot be infected by viruses;

Step (2): judging whether there is a virus spreading in the social network; if so, then enter step (3); if not, then return to step (2) to continue judging;

Step (3): Build an improved SIQR model, and set the parameters in the model according to the spread of social network viruses;

(301): The total number of nodes at time t in the social network remains constant K, namely:

S(t)+I(t)+Q(t)+R(t)=K;

Among them, K represents the total number of nodes, S(t) represents the number of susceptible nodes at time t, I(t) represents the number of spreading nodes at time t, Q(t) represents the number of isolated nodes at time t, and R(t) represents the number of immune nodes at time t. number of nodes;

(302): The newly added nodes are all susceptible nodes S, and the new joining rate of newly added nodes is b; the voluntary exit rate of susceptible node S is d; the probability that susceptible node S is transformed into isolated node Q by invalid isolation is m; each susceptible node S is transformed into a spreading node I with the infection rate λ and contact rate β;

(303): With the transformation of the susceptible node S, the probability that the spreading node I exits voluntarily is d, and the probability that the spreading node is forcibly isolated is α;

(304): The probability that the isolated node Q exits voluntarily is d, the probability that the isolated node Q turns into an immune node R is μ, and the probability that the isolated node cannot turn into an immune node and stands out is γ;

(305): After the isolation node Q is transformed into an immune node R, it will not be infected again, and the probability of the immune node R's voluntary exit is d.

Step (4): Utilize the parameters in the improved SIQR model to calculate the equilibrium point R ₀ of virus suppression;

Step (5): According to the balance point, the virus suppression strategy is given;

When R ₀ <1, the virus will disappear; when R ₀ >1, the virus will spread;

When the virus is prevalent, by increasing the invalid isolation rate m and the mandatory isolation rate α, the value of R ₀ can be reduced, so as to suppress the virus; or,

When the virus is prevalent, by reducing the transmission rate λ and the contact rate β, the reduction of the R ₀ value is achieved, thereby achieving the suppression of the virus; or,

When the virus is prevalent, by increasing the invalid isolation rate m and the mandatory isolation rate α, at the same time, reducing the transmission rate λ and the contact rate β, the value of R ₀ can be reduced, so as to suppress the virus.

This diagram embodies the social network information suppression process. First establish a social network, the number of user nodes gradually stabilizes within a certain range, and then continuously detect whether there are viruses (or inappropriate remarks, rumors, etc.) in the social network. The parameters are set, and the equilibrium point of virus suppression is calculated based on the differential model. Finally, the virus suppression strategy is given according to the equilibrium point.

As shown in Figure 2, the graph describes the evolution process of invalid isolated nodes. In social networks, ineffective segregation is inevitable. For example, a vulnerable node is mistakenly thought to need to be isolated because it browsed the content of the spreading node or communicated with the spreading node, and evolved into an isolated node, but in fact such nodes may not be infected with the virus. At this time, if the invalid isolated node evolves into an immune node, it will end; otherwise, the node can choose to exit the network by itself, or be forced to exit because it cannot evolve into an immune node.

As shown in Figure 3, the figure describes the immune process of the spreading node. When the virus is generated and propagated, the susceptible node will evolve into a spreading node due to the transmission rate λ and the contact rate β, and then the spreading node will evolve into an isolated node with the mandatory isolation rate α, and further, the isolated node will evolve into the immune rate μ as Immunization node, the immunization process ends.

The information dissemination model of SN is often established on the basis of the infectious disease model. The SIQR model takes into account factors such as isolation and immunity on the basis of the classic SIS and SIR dissemination models with the help of the basic knowledge of complex network dissemination dynamics. The present invention mainly studies the SIQR model under improved isolation conditions.

The SIQR model is obtained by introducing the isolation strategy on the basis of the SIR model. A new type of node, the isolation node, is added between the propagation node and the immune node in the SIR model. Therefore, there are mainly four types of nodes in the SIQR model: susceptible nodes, spreading nodes, isolated nodes, and immune nodes. The proportion of various nodes in the network keeps changing with time.

When the virus broke out, some nodes infected with the virus were isolated and became isolated nodes and no longer have the ability to spread the virus. Finally, the isolated node is effectively controlled and becomes an immune node or exits the network because the virus cannot be cleared, and the immune node will not be re-infected.

Although the SIQR model considers the isolation conditions, it is not comprehensive enough. On the one hand, new joining and exiting nodes in social networks are always happening, and the joining and exiting of nodes will not stop due to the prevalence of viruses. On the other hand, for a sudden virus outbreak, when isolation measures are taken, there is often a lag, which leads to the emergence of ineffective isolation.

Aiming at the above problems, the present invention improves the SIQR propagation model, considers the invalid isolation situation, and establishes a propagation model with new joining rate and voluntary exit rate. The model assumes that the total number of nodes is constant during the virus epidemic. In addition to the voluntary exit, the nodes in the model will also exit due to the virus cannot be cleared, so in order to keep the total number of nodes unchanged, the new joining rate is higher than the voluntary exit rate.

The newly added nodes in this model are all susceptible nodes. In the model, the susceptible nodes, spreading nodes, isolated nodes, and immune nodes will all have voluntary exit. Its propagation mechanism is shown in the figure below (see Figure 4):

(1) The total number of nodes at any time t in the social network remains a constant K, that is, S(t)+I(t)+Q(t)+R(t)=K.

(2) A node with a new joining rate b is added to the susceptible node, and the probability of the susceptible node exiting voluntarily is d. The probability that a susceptible node is transformed into an isolated node by invalid isolation is m, and some susceptible nodes are transformed into propagation nodes under the influence of the transmission rate λ and the contact rate β.

(3) With the conversion of susceptible nodes, the probability of self-exit of spreading nodes is also d. The probability that a propagating node is effectively isolated is α.

(4) The probability that all isolated nodes exit voluntarily is d. The probability that an isolated node cannot be transformed into an immune node and exit is γ, and the probability of successfully transforming into an immune node is μ.

(5) After the isolated node is transformed into an immune node, it will not be infected again, and the probability of the immune node exiting voluntarily is d.

According to the transformation relationship between various nodes in the propagation mechanism, the differential equation of the improved SIQR model can be written according to the complex network mean field theory (see formula 1).

Among them, K represents the total number of nodes, S(t) represents the number of susceptible nodes at time t, I(t) represents the number of spreading nodes at time t, Q(t) represents the number of isolated nodes at time t, and R(t) represents the number of immune nodes at time t. The number of nodes, λ is the infection rate, β is the contact rate, b is the new join rate, d is the voluntary exit rate, m is the invalid isolation rate, α is the mandatory isolation rate, μ is the immunity rate, and γ is the invalid immunity rate;

This improved model has added factors such as new join rate and voluntary exit rate to maintain the mobility of nodes, so it is very likely that there will be an endemic equilibrium point. According to this balance point, the epidemic and disappearance of the virus can be analyzed. For this, the behavior of susceptible nodes (S) and propagating nodes (I) needs to be studied. Using the first two equations in formula (1), the equilibrium point problem of the improved model can be analyzed.

The plane system formed by the first two equations in formula (1) is:

Among them, (S,I)={(S,I)0≤S≤K,0≤I≤K,S+I≤K}

In order to find the equilibrium point of the plane system (Formula 2), let its right end be 0, so as to obtain two possible solutions X ₁ and X ₂ :

If the two solutions are stable disease-free equilibrium point and endemic equilibrium point respectively, then the relevant influencing factors can be adjusted to control the spread of the virus and minimize its harm to the network.

Let R ₀ be the threshold, let When R ₀ <1, the planar system has only one equilibrium point X ₁ in the area D, so the stability of X ₁ can be judged by the signs of the coefficients p, q of its characteristic equation.

It can be seen from formula (3) and formula (4) that the point X ₁ is locally asymptotically stable. And because there is only one and only equilibrium point X ₁ in the region, it is impossible to have a closed trajectory, and it is impossible for the trajectory of the plane system from the region D to go beyond D. Therefore, the point is globally asymptotically stable in region D.

This means that no matter how many initial susceptible nodes are in the total nodes, the virus will not spread, but will gradually disappear. X ₁ is the disease-free equilibrium point of the model.

When _R0 > 1, the planar system (formula 2) has a positive equilibrium point X ₂ in the region D besides the disease-free equilibrium point X ₁ . At this time, due to Point X ₁ is indeterminate.

It can be seen from formula (5) and formula (6) that the point X ₂ is locally stable. The region D is a forward invariant set of the planar system (Formula 2), and there is no closed trajectory of the planar system in D. Furthermore, point _X2 is globally asymptotically stable in region D.

This means that once there is a transmission node, the virus will spread. In the end, the number of susceptible nodes and spreading nodes will be stabilized as a solution of _X2 respectively to form endemic. Point _X2 is the endemic equilibrium point of the model.

To sum up, when _R0 <1, the virus will gradually disappear; when _R0 >1, the virus will be endemic and endemic. And when _R0 =1, it is the threshold for distinguishing whether the virus disappears or not.

In the improved SIQR model, in order to reflect the dynamics of social network nodes, the joining and exiting of nodes always occur, which is similar to the birth and death of the human world. When the virus breaks out, the new join rate, voluntary exit rate, transmission rate, contact rate, invalid isolation rate, mandatory isolation rate and immunity rate of nodes are set as shown in Table 2.

In order to avoid the chance that the experiment is affected by the number of nodes, this simulation experiment uses the proportion of nodes instead of the actual number of nodes. When the experiment started (t=0), the ratio of the number of susceptible nodes to the total number of nodes was: s(0)=S/N=0.9; the ratio of the number of spreading nodes to the total number of nodes was: i(0)=I/N N=0.1; the simulation time is 9 days.

Table 2 parameter setting table

According to the above theoretical analysis results, _R0 = 1 is the threshold for distinguishing virus disappearance or epidemic. The parameter setting in the improved SIQR model among the present invention is as shown in table 2, and factors such as rate of new addition, withdrawal rate are kept constant, and research enforces the relationship between isolation rate and threshold R ₀ . From the constant values of the constant factors in the experiment, it is easy to calculate that when the mandatory isolation rate is 0.25, R ₀ is the threshold value 1 for the epidemic and disappearance of the virus.

In Figures 5 and 6, the mandatory isolation rates are set to be 0.30, 0.35, 0.40, 0.45, 0.50, and 0.55, respectively, and _R0 <1 is satisfied at this time. Figure 5 shows the change curve of the proportion of susceptible nodes over time, and Figure 6 is the change curve of the propagation node with the increase of time. It can be seen from the figure that with the increase of time, the number of susceptible nodes and the number of spreading nodes have reached a steady state, the ratio of the number of susceptible nodes to the total number of nodes is stable at 1, and the ratio of the number of infected groups to the total population Stable to 0. Eventually, the virus completely disappears, and all nodes in the flat system (Formula 2) are susceptible to infection. At this time, _R0 <1, the equilibrium point is (1,0), which coincides with the calculated value of the disease-free equilibrium point X ₁ in the previous theoretical analysis.

In addition, as shown in Figure 5 and Figure 6, as the mandatory isolation rate increases, the time for nodes to reach the disease-free equilibrium point gradually shortens. That is, the greater the mandatory isolation rate, the shorter the time to reach the disease-free equilibrium point.

In Figures 7 and 8, the mandatory isolation rates are set to be 0.20, 0.15, 0.10, 0.05, and 0.01 respectively, and _R0 > 1 is satisfied at this time. Figure 7 shows the change curve of the proportion of susceptible nodes over time, and Figure 8 shows the spread of The change curve of the node proportion with the increase of time. As shown in Figure 7 and Figure 8, with the increase of time, the proportion of susceptible nodes and the proportion of spreading nodes are both in a stable state, but the proportions are both greater than 0. Therefore, when _R0 > 1, the virus spreads and exists all the time.

As shown in Figures 7 and 8, as the mandatory isolation rate decreases, the time for the node proportion to reach the endemic equilibrium point gradually shortens. When the steady state is reached, the proportion of susceptible nodes decreases with the reduction of the mandatory isolation rate; the proportion of spreading nodes increases with the decrease of the mandatory isolation rate.

Theoretical and experimental results show that there is a threshold R ₀ in the improved SIQR propagation model for sudden virus epidemics. When _R0 <1, the virus will disappear; when _R0 >1, the virus will spread. Since in the improved SIQR propagation model, parameters such as new entry rate and exit rate are uncontrollable factors, therefore, when the virus is prevalent, we can take certain measures to control the values of other influencing factors and minimize the value of R ₀ . For the case of virus disappearance, the smaller R ₀ is, the faster the virus disappears; after the virus is prevalent, reducing the value of R ₀ can also effectively reduce the scope of transmission.

Measure 1: According to the formula of R ₀ , both m and α are negatively correlated with R ₀ . With the increase of invalid isolation rate and forced isolation rate, _R0 decreases gradually. Therefore, when the virus is prevalent, it is necessary to do a good job in the promotion of virus prevention, increase the speed and intensity of node isolation, and increase the mandatory isolation rate, so as to more effectively control the spread of the virus.

Measure 2: According to the formula of R ₀ , both λ and β are proportional to R ₀ . As the transmission rate and contact rate decrease, _R0 decreases gradually. Therefore, we can call on social network nodes to be more vigilant, increase awareness of virus prevention, and reasonably adopt prevention tools and means, so as to achieve the purpose of controlling the spread of the virus.

In social networks, virus-like information spreads quickly and is sudden. Some network models currently proposed do not consider the characteristics of invalid isolation and node mobility. Therefore, the present invention improves the SIQR model, and studies the disease-free equilibrium point and endemic equilibrium point of the model based on the mean field theory of complex networks. Existence and stability problems, and use simulation experiments to verify its correctness. And analyzed the relationship between the threshold of virus demise and epidemic and various influencing factors, and put forward the measures to prevent and control the epidemic of the virus based on this theory, so as to minimize the harm caused by the virus. The research work of the invention has important guiding significance for the prevention and control of viruses in social networks.

The above descriptions are only preferred embodiments of the present application, and are not intended to limit the present application. For those skilled in the art, there may be various modifications and changes in the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of this application shall be included within the protection scope of this application.

Claims (10)

1. A social network information suppression method is characterized by comprising the following steps:

step (1): establishing a social network;

step (2): judging whether a virus is spread in the social network or not; if yes, entering the step (3); if not, returning to the step (2) to continue judging;

and (3): constructing an improved SIQR model, and setting parameters in the model according to the propagation condition of the social network viruses;

and (4): calculating a balance point of virus inhibition by using parameters in the improved SIQR model;

and (5): and (4) according to the balance point, giving a virus inhibition strategy, and inhibiting the virus according to the virus inhibition strategy.

2. The method for suppressing social networking information according to claim 1, wherein in the step (1):

assume a social network, comprising four nodes: the node comprises a susceptible node S, a propagation node I, an isolation node Q and an immune node R;

the infection-susceptible node S can be infected by viruses, the transmission node I is infected by the viruses, and the viruses can be transmitted to the neighbor nodes; the isolated node Q is a node which is forced to block the communication capability, has no virus transmission capability and cannot be infected; the immune node R cannot be infected by a virus.

3. The method for suppressing social networking information according to claim 2, wherein in the step (3):

(301): the total number of nodes at the moment t in the social network is kept as a constant K, namely:

S(t)+I(t)+Q(t)+R(t)＝K；

k represents the total node number, S (t) represents the susceptible node number at the time t, I (t) represents the propagation node number at the time t, Q (t) represents the isolated node number at the time t, and R (t) represents the immune node number at the time t;

(302) the method comprises the following steps that new adding nodes are all susceptible nodes S, the new adding rate of the new adding nodes is b, the autonomous withdrawal rate of the susceptible nodes S is d, the probability that the susceptible nodes S are converted into isolated nodes Q through invalid isolation is m, and each susceptible node S is converted into a transmission node I through an infection rate lambda and a contact rate β;

(303) with the transformation of the susceptible node S, the probability of the autonomous exit of the propagation node I is d, and the probability of forced isolation of the propagation node is α;

(304): the probability that the isolation node Q exits independently is d, the probability that the isolation node Q is converted into the immune node R is mu, and the probability that the isolation node can not be converted into the immune node is gamma;

(305): after the isolated node Q is converted into the immune node R, the immune node R cannot be infected secondarily, and the probability of the immune node R exiting autonomously is d.

4. The method as claimed in claim 3, wherein the balance point R of virus suppression in step (4) is₀：

<mrow> <msub> <mi>R</mi> <mn>0</mn> </msub> <mo>=</mo> <mfrac> <mrow> <mi>&amp;lambda;</mi> <mi>&amp;beta;</mi> <mi>b</mi> <mi>K</mi> </mrow> <mrow> <mo>(</mo> <mi>d</mi> <mo>+</mo> <mi>m</mi> <mo>)</mo> <mo>(</mo> <mi>&amp;alpha;</mi> <mo>+</mo> <mi>d</mi> <mo>)</mo> </mrow> </mfrac> <mo>.</mo> </mrow>

5. The method of claim 4, wherein in step (5),

when R is₀When the virus is less than 1, the virus disappears; when R is₀> 1, the virus will be prevalent;

when the virus is epidemic, R is realized by increasing the ineffective isolation rate m and the forced isolation rate α₀The value is reduced, thereby achieving the inhibition of the virus.

6. The method as claimed in claim 5, wherein the social networking information suppression method,

when the virus is epidemic, R is achieved by reducing the transmission rate lambda and the contact rate β₀The value is reduced, thereby achieving the inhibition of the virus.

7. The method as claimed in claim 5, wherein the social networking information suppression method,

when the virus is epidemic, R is realized by increasing the ineffective isolation rate m and the forced isolation rate α and simultaneously reducing the transmission rate lambda and the contact rate β₀The value is reduced, thereby achieving the inhibition of the virus.

8. The method of claim 1, wherein the virus suppression policy further comprises:

the susceptible node S is inefficiently isolated by the probability m due to the contact with the propagation node I;

judging whether the invalid isolated node is converted into an immune node R or not, and if so, ending;

if the node is not converted into the immune node R, further judging whether the invalid isolated node actively exits or not, and if the invalid isolated node actively exits, ending the process; and if the active exit is not carried out, the forced exit is carried out.

9. The method as claimed in claim 1, wherein the social networking information suppression method,

the isolation nodes Q comprise nodes which are isolated by virus infection and nodes which are not isolated by virus infection;

the invalid isolation rate represents the ratio of the number of nodes which are not infected by the virus in the isolated nodes to the number of all the isolated nodes;

and the invalid immunity rate represents the ratio of the number of the nodes which fail to be converted to the number of all the nodes participating in conversion in the process of converting the isolated nodes into the immune nodes.

10. A social networking information suppression system, comprising: memory, a processor, and computer instructions stored on the memory and executed on the processor, the computer instructions, when executed by the processor, performing the steps of the method of any preceding claim.