CN116468569A - Group-based model construction method for false information propagation - Google Patents
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Abstract
A model construction method for false information propagation based on groups comprises constructing a network; secondly, randomly selecting a certain proportion of intelligent nodes in the network; thirdly, distributing different information filtering capacities for the common node and the selected intelligent node; fourth, setting transition probabilities between node states; fifthly, propagating based on a cascade information propagation method; finally, an information propagation range index is calculated. The invention simulates the behaviors of intelligent groups in the group in the propagation model, sets intelligent nodes in the model for observing the influence of node behaviors on false information propagation, and explores the role of the language in the group information propagation.
Description
Technical Field
The invention relates to the field of network false information propagation, in particular to a social network false information propagation model based on groups.
Background
With the advent of web2.0, information can be rapidly and widely disseminated due to the popularity of various social media platforms (e.g., online social networks, microblogs, and WeChat). Millions of people discuss and share their topics on these social media, ranging from public transactions to personal life, which greatly facilitates people's communication and dissemination of information. The information propagation process is known, the propagation characteristics of social media are revealed, and important theory and application values are achieved. It is therefore also one of the most popular topics. The transmission of information can lead to large-scale infection, and the real information is beneficial to people and can be widely transmitted; and the propagation of some false information may lead to serious social effects, the propagation of which should be impaired. Due to the high degree of freedom in social networks, it is more difficult to control the propagation of information. Therefore, research on the characteristics and prevention and control aspects of false information propagation is very important.
Chinese patent publication No. CN111797328 (a method for inhibiting rumor transmission in social networks) discloses a method for inhibiting rumor transmission in social networks. According to the method, the network is subjected to (k, eta) kernel decomposition, the number of immune persons selected from each (k, eta) kernel is calculated based on the number of infectious persons of the (k, eta) kernel and the kernel value, then the influence of rumors of infection sets when uninfected individuals are taken as immune persons is calculated according to a rumor transmission model, and the individual with the smallest influence of rumor transmission is selected to be added into the immune person set, so that a final immune person set is obtained. Individuals in the immune repertoire are immunized to suppress rumor transmission. The method can quickly and effectively find immune individuals in the social network for the large-scale social network to achieve the purpose of inhibiting rumor transmission. Publication number CN119669958 (a rumor propagation model building method) provides a rumor propagation model that considers both individual liveness and the mechanism of rumors. By improving the SIR model, a multi-factor model SIWISR-M model is proposed. The rumor-avoiding mechanism provided by the invention can effectively reduce the peak value of the density of rumors and the duration of rumors. Chinese patent publication number CN111274496 (a method for constructing a rumor propagation model taking groups into account on heterogeneous networks) discloses an SEIR rumor propagation model based on the group characteristics of today's mobile social networks, which randomly sets some groups in the network, and the propagation mechanism includes group propagation and node propagation, and explores the role of groups in propagation in heterogeneous networks.
However, in the above-mentioned existing rumor propagation model or rumor suppression method, the effect of the existence of the intelligent node on information propagation is not studied, and the influence of the comment made in the group on the information propagation process is not considered, so the model is not perfect.
The presence of groups in a social network can enable information to be rapidly and efficiently disseminated in a broadcast manner, such as micro-clusters, QQ clusters and microblogs, and the presence of the groups can also promote individuals to carry out closed discussions on the information, and the discussions can have fermentation or inhibition effects on the information dissemination.
Disclosure of Invention
In order to overcome the defect of false information prevention and control of the existing social network, the invention provides a false information propagation model implementation method based on the group, which is favorable for prevention, control and treatment of false information based on an independent cascade model and combined with the characteristic that people in the social network can widely participate in the group to discuss. The technical scheme adopted for solving the technical problems is as follows:
a group-based model building method for spurious information propagation, the method comprising the steps of:
s1, constructing a network: the network set is G= (V, E), and the node set and the edge set are V= { V respectively 1 ,v 2 ,…,v n Sum ofThe total number of nodes N;
s2, randomly selecting intelligent nodes of the part, and marking the set as V smart ；
S3, distributing the information filtering capability of the nodes: for set V smart The probability of receiving false information is 0, and the probability of receiving false information for a common node is P n ；
S4, setting the transition probability among the node states;
s5, a cascade information propagation-based method comprises the following steps: there are three different states for each node in the model: 0 (susceptibility), 1 (adoption), 2 (immunization), and information transmission is carried out from the adopted node, the node transmits the information to a group taking the node as a center (the group is constructed by any one node and a first-order neighbor thereof), the information is transmitted layer by layer, and when all nodes in the network are traversed completely, the information is stopped;
s6, calculating information propagation range indexes: and calculating the average value of false information propagation ranges under different proportion intelligent nodes and different silencing probabilities to obtain a final propagation result.
The scale of the network described in the stated step S1 is n=5000, the average degree k=10, and the initial states of all nodes are set as the susceptibility (state=0).
The stated step S2 specifically includes: randomly selecting a part of nodes in the network as intelligent nodes (the intelligent nodes in the group do not send out comments with silencing probability lambda or comment with 1-lambda, issue own questions on false information, influence the decision of individuals in the group) and adding the intelligent nodes into a set V smart The rest nodes are common nodes.
The stated step S3 specifically includes:
s3.1 initial probability P of intelligent node accepting false information S ＝0；
S3.2, the initial probability that the common node receives false information is a random number P in a range of intervals n ∈(0,0.5)。
The step S4 specifically includes:
s4.1, the probability P will be after receiving the message by the susceptible person (state=0) n λ k Converting to a taker (state=1), wherein k represents the number of intelligent nodes in the group, and the greater the silencing probability lambda of the intelligent nodes, the greater the probability of the susceptible person converting to the taker in the case that the number of intelligent nodes in the group is k; meanwhile, under the condition of the silencing probability of given intelligent nodes, the probability that the susceptible person is converted into the adoption person is smaller as the intelligent nodes k in the group are more;
s4.2, the users in the group are also influenced by the comments of the intelligent nodes, thereby the information is processedDoubt about the authenticity of each of the employers (state=1) with probability 1- λ k Converted to immunity (state=2) with probability λ k Remain unchanged (state=1);
the probability formula is set as follows:
P 0→1 ＝P n λ k (1)
P 0→2 ＝1-P n λ k (2)
P 1→1 ＝λ k (3)
P 1→2 ＝1-λ k (4)
wherein P is 0→1 Probability of the susceptible node being converted into the adopted node, P 0→2 Is the probability of a susceptible node being converted into an immune node, P 1→1 Is the probability of adopting the node to maintain the self state, P 1→2 The probability of a node being converted to an immune node is employed.
The stated step S5 specifically includes:
s5.1, randomly selecting a node as an initial adopted node, and starting information propagation from the adopted node, wherein the node forwards information to a group (the group is composed of a current node and a first-order neighbor thereof) centering on the node;
s5.2, the adopter-centered group is denoted as V i The method comprises the steps of carrying out a first treatment on the surface of the Node set is adopted in the network and is marked as V adopted The method comprises the steps of carrying out a first treatment on the surface of the The node set immunized in the network is denoted as V immune The method comprises the steps of carrying out a first treatment on the surface of the The node set of each step of newly adopted information in the network is marked as V new_adopted The method comprises the steps of carrying out a first treatment on the surface of the The set of temporary storage nodes is denoted as V temp The method comprises the steps of carrying out a first treatment on the surface of the When a certain node i forwards information, every node in the group can see the information, and the node i can send the information with probability P i Forwarding the information, the process is as follows:
s5.3, randomly selecting a node i from a common node set, taking the node i as an adopted node (state=1) as a source of information transmission, forwarding false information, and adding the node i to V adopted And V new_adopted In (1), V new_adopted The node in (a) is added to V temp In (a) and (b);
s5.4 from V temp Any node i, and group centered on i is denoted as V i Calculating the number of intelligent nodes in the group, denoted as k, for any one belonging to V i According to the network information propagation model, calculating the probability of the nodes receiving the information, and when no intelligent node exists in the group or no comment is issued, propagating according to a general independent cascade model;
s5.5, traversing nodes in the group, and generating a random number random of 0-1 for each susceptible node i; if P i If not less than random, forwarding the node i, and adding the node i to V new_adopted Node turns into adoption node (state=1); if P i If less than random, node i does not forward, node i enters V immune Node conversion to immune node (state=2);
s5.6, for the infected nodes in the group, generating a random number random of 0-1, if P i ＝λ k The nodes i keep the state unchanged if the nodes are not less than random; if P i ＝λ k If less than random, node i does not forward, node i enters V immune And set V adopted Node i, node state transitions to immune node (state=2);
s5.7, mix V temp Node in (1) is emptied and V is set new_adopted The node in (a) is added to V adopted And V temp In which steps S5.4-S5.7 are repeated continuously, when V new_adopted When no node exists, stopping the algorithm, and indicating that the information transmission is finished at the moment; v (V) adopted Representing a final adopted node set;
s5.8, continuously repeating the steps S5.3-S5.7 until the iteration number reaches 1000, and recording the total propagation number;
s5.9, changing the proportion r of the intelligent nodes, repeating the steps S3-S6 of the silencing probability of the intelligent nodes, and recording the propagation times under each condition.
The stated step S6 specifically includes:
s6.1, recording the final number of the adopters as N', wherein the transmission range of the false information is as follows:
s6.2, obtaining different intelligent node proportions r and propagation conditions of different networks under different intelligent node silencing probabilities lambda.
The beneficial effects of the invention are as follows:
a method for constructing a false information propagation model based on a group is provided, and the role of intelligent nodes in the group is explained. The model can help researchers better understand the relation between the statement of the intelligent node in the group and the existence of the group and the false information propagation range, and can provide thought for suppressing the false information propagation.
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The accompanying drawings are included to provide a visual representation of the invention and are incorporated in and constitute a part of this specification, illustrate the invention and together with the embodiments of the invention, serve to explain the invention.
FIG. 1 is an algorithm flow chart of a method for constructing a group-based false information propagation model according to the present invention;
FIG. 2 is a graph showing the variation of false information propagation range with the probability of silencing of intelligent nodes at different p values in a random network;
FIG. 3 is a graph of the range of spurious information propagation over time at different lambda values in a random network;
FIG. 4 is a graph showing the variation of probability p of false information propagation range with common node acceptance information at different lambda and r values in a random network;
FIG. 5 is a graph showing the variation of the information propagation range with the probability of silencing of intelligent nodes at different p values in a scaleless network;
FIG. 6 is a graph showing the variation of the information propagation range with the probability of silencing of intelligent nodes at different p values in a real network.
Detailed Description
The following describes the embodiments of the present invention in further detail with reference to the drawings.
Example 1
Referring to fig. 1 to 6, the invention selects three different structure networks, a random network, a scaleless network and a real network as experimental networks. The network is simple in structure, but representatively can help researchers to better understand the role of intelligent nodes and understand false information propagation based on groups.
In this embodiment, a method for constructing a model for false message propagation based on a group includes the following specific steps:
a group-based model building method for spurious information propagation, the method comprising the steps of:
s1, constructing a network: the network set is G= (V, E), and the node set and the edge set are V= { V respectively 1 ,v 2 ,…,v n Sum ofThe total number N of nodes comprises the following specific steps:
s1.1, constructing a random network, wherein the scale of the network is n=5000, the average degree k=10, and the initial states of all nodes are set as susceptible persons (state=0).
S2, randomly selecting intelligent nodes with a certain proportion, and marking the set as V smart The method comprises the following specific steps of:
s2.1, randomly selecting a part of nodes in the network as intelligent nodes (the intelligent nodes in the group do not send out comments by silencing probability lambda or comment by 1-lambda, issue own questions of false information, influence individual decisions in the group) and adding the nodes into a set V smart The rest nodes are common nodes.
S3, distributing the information filtering capability of the nodes: for set V smart The probability of receiving false information is 0, and the probability of receiving false information for a common node is P n The method comprises the following specific steps:
s3.1 initial probability P of intelligent node accepting false information S ＝0；
S3.2, the initial probability that the common node receives false information is a random number P in a range of intervals n ∈(0,0.5)。
S4, setting the transition probability among node states, wherein the method comprises the following specific steps of:
s4.1, the probability P will be after receiving the message by the susceptible person (state=0) n λ k Converting to a taker (state=1), wherein k represents the number of intelligent nodes in the group, and the greater the silencing probability lambda of the intelligent nodes, the greater the probability of the susceptible person converting to the taker in the case that the number of intelligent nodes in the group is k; meanwhile, under the condition of the silencing probability of given intelligent nodes, the probability that the susceptible person is converted into the adoption person is smaller as the intelligent nodes k in the group are more;
the owners in S4.2 group are also affected by the comments of the intelligent nodes, so that doubt is made about the authenticity of the information, and each owner (state=1) has a probability of 1-lambda k Converted to immunity (state=2) with probability λ k Remain unchanged (state=1);
the probability formula is set as follows:
P o→1 ＝P n λ k (1)
P o→2 ＝1-P n λ k (2)
P 1→1 ＝λ k (3)
P 1→2 ＝1-λ k (4)
wherein P is 0→1 Probability of the susceptible node being converted into the adopted node, P 0→2 Is the probability of a susceptible node being converted into an immune node, P 1→1 Is the probability of adopting the node to maintain the self state, P 1→2 The probability of a node being converted to an immune node is employed.
S5, a cascade information propagation-based method comprises the following steps: there are three different states for each node in the model: 0 (susceptibility), 1 (adoption), 2 (immunization), and information transmission is carried out from the adoption node, the node transmits the information to a group taking the node as a center (the group is constructed by any one node and a first-order neighbor thereof), the information is transmitted layer by layer, and when all nodes in a network are traversed completely, the information is stopped, and the specific steps are as follows:
s5.1, randomly selecting a node as an initial adopted node, and starting information propagation from the adopted node, wherein the node forwards information to a group (the group is composed of a current node and a first-order neighbor thereof) centering on the node;
s5.2, the adopter-centered group is denoted as V i The method comprises the steps of carrying out a first treatment on the surface of the Node set is adopted in the network and is marked as V adopted The method comprises the steps of carrying out a first treatment on the surface of the The node set immunized in the network is denoted as V immune The method comprises the steps of carrying out a first treatment on the surface of the The node set of each step of newly adopted information in the network is marked as V new _ adopted The method comprises the steps of carrying out a first treatment on the surface of the The set of temporary storage nodes is denoted as V temp The method comprises the steps of carrying out a first treatment on the surface of the When a certain node i forwards information, every node in the group can see the information, and the node i can send the information with probability P i Forwarding the information, the process is as follows:
s5.3, randomly selecting a node i from a common node set, taking the node i as an adopted node (state=1) as a source of information transmission, forwarding false information, and adding the node i to V adopted And V new_adopted In (1), V new_adopted The node in (a) is added to V temp In (a) and (b);
s5.4 from V temp Any node i, and group centered on i is denoted as V i Calculating the number of intelligent nodes in the group, denoted as k, for any one belonging to V i According to the network information propagation model, calculating the probability of the nodes receiving the information, and when no intelligent node exists in the group or no comment is issued, propagating according to a general independent cascade model;
s5.5, traversing nodes in the group, and generating a random number random of 0-1 for each susceptible node i; if P i If not less than random, forwarding the node i, and adding the node i to V new_adopted Node turns into adoption node (state=1); if P i If less than random, node i does not forward, node i enters V immune Node conversion to immune node (state=2);
s5.6, for the infected nodes in the group, generating a random number random of 0-1, if P i ＝λ k The nodes i keep the state unchanged if the nodes are not less than random; if P i ＝λ k If less than random, node i does not forward, node i enters V immune And set V adopted Node i, node state transitions to immune node (state=2);
s5.7, mix V temp Node in (1) is emptied and V is set new_adopted The node in (a) is added to V adopted And V twmp In which steps S5.4-S5.7 are repeated continuously, when V new_adopted When no node exists, stopping the algorithm, and indicating that the information transmission is finished at the moment; v (V) adopted Representing a final adopted node set;
s5.8, continuously repeating the steps S5.3-S5.7 until the iteration number reaches 1000, and recording the total propagation number;
s5.9, changing the proportion r of the intelligent nodes, repeating the steps S3-S6 of the silencing probability of the intelligent nodes, and recording the propagation times under each condition.
S6, calculating information propagation range indexes: calculating the average value of false information propagation ranges under different proportion intelligent nodes and different silencing probabilities to obtain a final propagation result, wherein the method comprises the following specific steps:
s6.1, recording the final number of the adopters as N', wherein the transmission range of the false information is as follows:
s6.2, obtaining different intelligent node proportions r and propagation conditions of different networks under different intelligent node silencing probabilities lambda.
FIG. 2 is a graph showing the variation of false information propagation range with the probability of silencing of intelligent nodes at different p values in a random network. Fig. 3 is a graph of the spread range of spurious information over time at different lambda values in a random network. Fig. 4 is a graph showing the variation of the probability p of false information propagation range with the acceptance of information by a common node in a random network with different lambda and r values. Fig. 5 and 6 are graphs showing the variation of the information propagation range with the probability of silencing of the intelligent node in the scaleless network and the real network, respectively, with different p values. As can be seen from fig. 2, when the probability of silencing of the intelligent node is greater than a certain threshold, false information can be propagated. Comparing fig. 2,5, and 6, it can be seen that spurious information is more difficult to propagate out in a scaleless network, and a larger threshold exists. Fig. 3 shows that with increasing time steps the spread of spurious information expands rapidly, and the network then remains stationary. As λ increases, the range of spurious information propagation gradually decreases. FIG. 4 shows that the greater the number of intelligent nodes in the network, the more difficult the spurious information is to propagate, and that spurious information can only propagate if it is greater than a certain threshold.
As described above, for the embodiments of the present invention on random networks and scale-free networks as well as real networks, we propose a method for constructing a group-based false information propagation model, when intelligent nodes exist in a social network, the language of the intelligent nodes may affect the nodes in the group. Based on the method, through combining with simulation experiments, the model can help researchers to better know the effect of the intelligent nodes in group propagation and play the role of suppressing false information propagation by the intelligent nodes.
Example 2
The embodiment provides a false information propagation inhibition method of a group-based false information propagation model construction method, which comprises the following steps:
steps S1 to S6 of this embodiment are the same as those of embodiment 1, and further include the steps of:
s7, a specific application method. The method comprises the following specific steps:
s7.1, selecting the nodes with influence in the social network as intelligent nodes, such as selecting professors of well-known universities, experts and the like, wherein the people have relative authorities and learning, and the language of the people has more public confidence and the attribute of the intelligent nodes.
S7.2, people with influence make a speaking and publishing. When spurious information is seen in the network, they can issue views about the spurious information on a circle of friends, a micro-community or micro-blogging platform, etc., to influence the decisions of surrounding masses.
S7.3, elimination of false information. When the true language with respect to the false information starts to spread in the network, those masses who would believe the false information start to no longer information the false information and do not disseminate the information. Over time, false information will not reach cascading phenomena in the network, i.e. only small-scale propagation. Thereby achieving the purpose of suppressing the propagation of information.
The above embodiments are only examples of the present invention, and are not intended to limit the present invention, and any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (7)
1. A model construction method for false information propagation based on groups comprises the following steps:
s1, constructing a network: the network set is G= (V, E), and the node set and the edge set are V= { V respectively 1 ,v 2 ,…,v n Sum ofThe total number of nodes N;
s2, randomly selecting intelligent nodes of the part, and marking the set as V smart ；
S3, distributing the information filtering capability of the nodes: for set V smart The probability of receiving false information is 0, and the probability of receiving false information for a common node is P n ；
S4, setting the transition probability among the node states;
s5, a cascade information propagation-based method comprises the following steps: there are three different states for each node in the model: 0 (susceptibility), 1 (adoption), 2 (immunization), and information transmission is carried out from the adopted node, the node transmits the information to a group taking the node as a center (the group is constructed by any one node and a first-order neighbor thereof), the information is transmitted layer by layer, and when all nodes in the network are traversed completely, the information is stopped;
s6, calculating information propagation range indexes: and calculating the average value of false information propagation ranges under different proportion intelligent nodes and different silencing probabilities to obtain a final propagation result.
2. The method of claim 1, wherein the network in step S1 has a size of n=5000, an average degree of k=10, and an initial state of all nodes is set to be a susceptible (state=0).
3. The method of claim 1, wherein in step S2, part of the intelligent nodes are selected randomly, the intelligent nodes in the group do not send comments with silencing probability λ, or comment with 1- λ, and issue own questions about the false information to influence the decisions of individuals in the group, namely: randomly selecting a part of nodes in the network as intelligent nodes and adding the intelligent nodes into the set V smart The rest nodes are common nodes.
4. The method for constructing a model for false information propagation according to claim 1, wherein in the step S3, the capability of filtering information of the node is allocated, and the specific steps are as follows:
s3.1 initial probability P of intelligent node accepting false information S ＝0；
S3.2, the initial probability that the common node receives false information is a random number P in a range of intervals n ∈(0，0.5)。
5. The method for constructing a model for false information propagation based on group as claimed in claim 1, wherein in said step S4, the probability of transition between node states is set as follows:
s4.1, the probability P will be after receiving the message by the susceptible person (state=0) n λ k Converting to a taker (state=1), wherein k represents the number of intelligent nodes in the group, and the greater the silencing probability lambda of the intelligent nodes, the greater the probability of the susceptible person converting to the taker in the case that the number of intelligent nodes in the group is k; at the same time give intelligenceUnder the condition of the silencing probability of the intelligent nodes, the more intelligent nodes k in the group, the smaller the probability that the susceptible person is converted into the adoption person;
the owners in S4.2 group are also affected by the comments of the intelligent nodes, so that doubt is made about the authenticity of the information, and each owner (state=1) has a probability of 1-lambda k Converted to immunity (state=2) with probability λ k Remain unchanged (state=1);
the probability formula is set as follows:
P 0→1 ＝P n λ k (1)
P 0→2 ＝1-P n λ k (2)
P 1→1 ＝λ k (3)
P 1→2 ＝1-λ k (4)
wherein P is 0→1 Probability of the susceptible node being converted into the adopted node, P 0→2 Is the probability of a susceptible node being converted into an immune node, P 1→1 Is the probability of adopting the node to maintain the self state, P 1→2 The probability of a node being converted to an immune node is employed.
6. The method for constructing a model for false information propagation based on a group as claimed in claim 1, wherein in the step S5, the method for learning based on cascade information propagation comprises the following specific steps:
s5.1, randomly selecting a node as an initial adopted node, and starting information propagation from the adopted node, wherein the node forwards information to a group (the group is composed of a current node and a first-order neighbor thereof) centering on the node;
s5.2, the adopter-centered group is denoted as V i The method comprises the steps of carrying out a first treatment on the surface of the Node set is adopted in the network and is marked as V adopted The method comprises the steps of carrying out a first treatment on the surface of the The node set immunized in the network is denoted as V immune The method comprises the steps of carrying out a first treatment on the surface of the The node set of each step of newly adopted information in the network is marked as V new_adopted The method comprises the steps of carrying out a first treatment on the surface of the The set of temporary storage nodes is denoted as V temp The method comprises the steps of carrying out a first treatment on the surface of the After a certain node i forwards the information, each node in the groupThe points can see the information while node i will see the probability P i Forwarding the information, the process is as follows:
s5.3, randomly selecting a node i from a common node set, taking the node i as an adopted node (state=1) as a source of information transmission, forwarding false information, and adding the node i to V adopted And V new_adopted In (1), V new_adopted The node in (a) is added to V temp In (a) and (b);
s5.4 from V temp Any node i, and group centered on i is denoted as V i Calculating the number of intelligent nodes in the group, denoted as k, for any one belonging to V i According to the network information propagation model, calculating the probability of the nodes receiving the information, and when no intelligent node exists in the group or no comment is issued, propagating according to a general independent cascade model;
s5.5, traversing nodes in the group, and generating a random number random of 0-1 for each susceptible node i; if P i If not less than random, forwarding the node i, and adding the node i to V new_adopted Node turns into adoption node (state=1); if P i <random, node i does not forward, node i enters V immune Node conversion to immune node (state=2);
s5.6, for the infected nodes in the group, generating a random number random of 0-1, if P i ＝λ k The nodes i keep the state unchanged if the nodes are not less than random; if P i ＝λ k <random, node i does not forward, node i enters V immune And set V adopted Node i, node state transitions to immune node (state=2);
s5.7, mix V temp Node in (1) is emptied and V is set new_adopted The node in (a) is added to V adopted And V temp In which steps S5.4-S5.7 are repeated continuously, when V new_adopted When no node exists, stopping the algorithm, and indicating that the information transmission is finished at the moment; v (V) adopted Representing a final adopted node set;
s5.8, continuously repeating the steps S5.3-S5.7 until the iteration number reaches 1000, and recording the total propagation number;
s5.9, changing the proportion r of the intelligent nodes, repeating the steps S3-S6 of the silencing probability of the intelligent nodes, and recording the propagation times under each condition.
7. The method for constructing a model for false information propagation based on group as claimed in claim 1, wherein in the step S6, an information propagation range index is calculated, specifically comprising the steps of:
s6.1, recording the final number of the adopters as N', wherein the transmission range of the false information is as follows:
s6.2, obtaining different intelligent node proportions r and propagation conditions of different networks under different intelligent node silencing probabilities lambda.
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