CN111881615A - Model and method for information cooperative propagation of individual sensitivity in multilayer network - Google Patents
Model and method for information cooperative propagation of individual sensitivity in multilayer network Download PDFInfo
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Abstract
The invention discloses a model and a method for information cooperative propagation of individual sensitivity in a multilayer network, which overcome a series of realistic problems of the relation degree between individuals and the channel quantity of the individual for obtaining information sources in the real network in the prior art, and comprises a multilayer complex network modeling module, a numerical simulation module and a real network verification module which are connected in sequence; the multilayer complex network modeling module is used for constructing a network model for modeling; the numerical simulation module is used for carrying out simulation on the model constructed by the multilayer complex network modeling module; and the real network verification module simulates the process of realizing information propagation in the real network from the model. The invention adopts the process of researching information transmission from the angle of a complex network, controls adverse effects caused by malignant transmission to a certain extent through the improvement and optimization of the model, and can better provide a relatively safe social network environment for users.
Description
Technical Field
The invention relates to the technical field of complex network propagation dynamics, in particular to a model and a method for information cooperative propagation in a multilayer network based on individual sensitivity of dynamic characteristics of information propagation in the multilayer network.
Background
With the development of the information age, information communication modes are gradually diversified, information communication between infrastructure networks in the real world is wider, and different social factors influence the information propagation range. In the field of complex networks, by using the ideas of subjects such as statistical physics, some theoretical methods have been proposed by scholars, and extensive theory such as mean field theory and the like is applied, so that the propagation behavior of epidemic diseases on the complex network can be researched, and in order to kill malignant propagation in a cradle and spread benign propagation as fast as possible, the scholars have proposed an SI model, an SIR model and an SIs model.
In the early days, the research results of the propagation dynamics on a single network were significant both domestically and abroad. However, in the research of spreading dynamics on a single network, many factors affecting the spreading, such as multi-channel of the spreading path, multi-path spreading of information caused by different interaction platforms (Facebook, Twitter, etc.) and interaction modes (short messages, telephone, etc.) in the social network, etc., are inevitably ignored.
Disclosure of Invention
The invention provides an individual sensitivity model and an individual sensitivity method for information cooperative propagation in a multilayer network, aiming at overcoming the problems caused by neglecting a plurality of influence propagation factors in the prior art.
In order to achieve the purpose, the invention adopts the following technical scheme:
an individual sensitivity information cooperation propagation model in a multilayer network comprises a multilayer complex network modeling module, a numerical simulation module and a real network verification module which are sequentially connected; the multilayer complex network modeling module is used for constructing a network model for modeling; the numerical simulation module is used for carrying out simulation on the model constructed by the multilayer complex network modeling module; the real network verification module simulates the process of realizing information propagation in a real network from a model, and is applied to the actual network according to the propagation dynamics principle, so that the propagation range of information in the real network and the prevention, control and intervention of the malignant information flooding in the network can be better known.
Preferably, the multi-layer complex network modeling module performs modeling based on an SI model and an SIR model, and propagation dynamics principles are involved in the process.
Preferably, when the SI model network is in the initial state, an individual has two states, namely a susceptible state and an adopted state, and information is propagated in the multi-layer network, where the propagation process includes the following steps:
a1: assuming that a certain number of individuals are infected in the initial state in the network, namely the initial infection probability is unchanged, and the number of the information channels acquired by the individuals is certain, so that the information can be transmitted in the network, and the degree of relation between the individuals in the network is defined, namely the individual sensitivity is theta;
a2: a user in the first network is informed of a message, and the probability of the user being infected is P1＝(1/3)θ；
A3: a user in the layer two network is informed of a message, and the probability of the user being infected is P2＝(2/3)θ；
A4: when the user is informed of the same information in all three networks, the user can certainly trust the information, and the probability is 1; the kinetic processes when susceptible individuals are infected are:
S+I→2I
wherein S is susceptible state, and I is adopted state.
Preferably, the SIR model has three states in the initial state of the network, namely a susceptible state, an adopted state and an immune state, the infected individual can recover with a probability γ in the multilayer network, and the dynamic process of the infected individual recovering the state of paired information immunity is as follows:
I→R
wherein I is the adopted state, R is the immune state, and the susceptible state is represented by S.
Preferably, the numerical simulation module comprises social influence factors, wherein the social influence factors comprise individual sensitivity, the channel number of individual perception information, the number of infected individuals in an initial state and a certain recovery probability of the infected individuals.
Preferably, the analysis process of the numerical simulation module for information collaborative propagation in the multi-layer network includes the following steps:
b1: initializing initial infection probability omega and individual sensitivity theta, and under the condition that nodes in the network are not recovered to be immune nodes, obtaining a function curve of the ratio of the infected nodes in the network and the function curve of the infected nodes in the network when information propagation reaches a steady state by adjusting the value of the channel number of individual perception information;
b2: initializing initial infection probability omega, channel quantity of individual perception information and recovery probability gamma, setting the step length of the individual sensitivity theta to be 0.02 by adjusting the individual sensitivity theta, and obtaining a function curve of the node quantity and theta and a function curve of the ratio of immune nodes to theta in three states in a network when information propagation reaches a steady state;
b3: initializing initial infection probability omega, channel quantity of individual perception information and individual sensitivity theta, setting the step length of gamma to be 0.002 by adjusting recovery probability gamma under the condition that partial infection nodes in the network are recovered to immune nodes, and obtaining function curves of the node quantity and gamma in three states in the network and function curves of the occupation ratio of the immune nodes and gamma when information transmission reaches a steady state;
b4: counting the variances of B1, B2 and B3 through 100 iterations to obtain a function curve;
b5: setting a plurality of different network layer numbers, and repeating B1-B4;
b6: the ER network in the model is replaced by the BA network, and B1-B5 is repeated.
A method for cooperatively propagating information in a multilayer network by individual sensitivity adopts an information cooperative propagation model in the multilayer network by the individual sensitivity, and comprises the following steps:
s1: generating a three-layer network with an initial infection probability omega;
s2: initializing the number of individual perception information channels and the recovery probability gamma to be 0, and carrying out iterative updating according to the sensitivity degree theta epsilon (0, 10) of an individual to information;
s3: judging the termination of the iteration condition, updating the number of the infected nodes in the three-layer network, and counting the probability of the infected nodes as PIAnd a variance;
s4: adjusting parameters of the model, reinitializing the number of individual perception information channels and the sensitivity degree theta of an individual to information, and performing iterative updating by recovering the probability gamma which belongs to (0, 1);
s5: judging the termination of the iteration condition, updating the number of susceptible nodes, the number of infected nodes and the number of immune nodes in a three-layer network, and counting the probability of the immune nodes as PRAnd a variance;
s6: the model of the invention is verified in an actual network system.
Therefore, the invention has the following beneficial effects:
1. the method simulates the process of realizing information propagation in the real network from a model, and is applied to the real network according to the propagation dynamics principle, so that the propagation range of information in the real network and the prevention, control and intervention of the malignant information flooding in the network can be better known;
2. the method utilizes the characteristics and the propagation dynamics of the real network, more truly and effectively reflects the influence of the relationship among different individuals on the cooperative propagation of the information in the multilayer network, technically simulates the process and the control of the cooperative propagation of the information, and solves a series of problems existing in reality, such as the degree of the relationship among the individuals in the real network, the number of channels for the individuals to obtain information sources and the like;
3. the method has the advantages that the information transmission process is researched from the perspective of a complex network, and the prevention, control and intervention effects are achieved by improving and optimizing the model and combining the characteristics of the real network and the transmission dynamics theory on the information flooding of the real network; the method and the device can effectively analyze how to promote the wide spread of benign information and the inhibition of bad information in the network, further control the bad results caused by the malignant spread to a certain extent for the application in the actual life, and can better provide a relatively safe social network environment for the user.
Drawings
FIG. 1 is a flow chart of the operation of the present invention.
Fig. 2 is a diagram showing simulation results of the SIR model of the present invention.
FIG. 3 is a graph showing the results of a simulation of the immune status of the present invention.
Fig. 4 is a block diagram of the architecture of the present invention.
In the figure: 1-multilayer complex network modeling module 2-numerical simulation module 3-real network verification module
Detailed Description
The invention is further described with reference to the following detailed description and accompanying drawings.
The embodiment provides an individual sensitivity information collaborative propagation model in a multilayer network, as shown in fig. 1-3, comprising a multilayer complex network modeling module 1, a numerical simulation module 2 and a real network verification module 3 which are connected in sequence; the multilayer complex network modeling module is used for constructing a network model for modeling; the numerical simulation module is used for carrying out simulation on the model constructed by the multilayer complex network modeling module; the real network verification module simulates the process of realizing information propagation in a real network from a model, and is applied to the real network according to the propagation dynamics principle, so that the propagation range of information in the real network and the prevention, control and intervention of the malignant information flooding in the network are better known; the multi-layer complex network modeling module carries out modeling based on an SI model and an SIR model, a propagation dynamics principle is involved in the process, an individual has two states in the initial state of the SI model network, namely a susceptible state and an adopted state, information is propagated in the multi-layer network, and the propagation process comprises the following steps:
a1: assuming that a certain number of individuals are infected in the initial state in the network, namely the initial infection probability is unchanged, and the number of the information channels acquired by the individuals is certain, so that the information can be transmitted in the network, and the degree of relation between the individuals in the network is defined, namely the individual sensitivity is theta;
a2: a user in the first network is informed of a message, and the probability of the user being infected is P1＝(1/3)θ；
A3: a user in the layer two network is informed of a message, and the probability of the user being infected is P2＝(2/3)θ；
A4: when the user is informed of the same information in all three networks, the user can certainly trust the information, and the probability is 1; the kinetic processes when susceptible individuals are infected are:
S+I→2I
wherein S is susceptible state, and I is adopted state.
Wherein, the individual has three kinds of states when the network initial state of SIR model, is the state of being liable to, adopts state and immunity state respectively, and infected individual can resume with probability gamma in multilayer network, and the kinetic process that infected individual resumes the state of information immunity in pairs is:
I→R
wherein I is the adopted state, R is the immune state, and the susceptible state is represented by S.
The numerical simulation module comprises social influence factors, wherein the social influence factors comprise individual sensitivity, channel quantity of individual perception information, quantity of infected individuals in an initial state, and certain recovery probability of the infected individuals.
The analysis process of the numerical simulation module for the information collaborative propagation in the multilayer network comprises the following steps: b1: initializing initial infection probability omega and individual sensitivity theta, and under the condition that nodes in the network are not recovered to be immune nodes, obtaining a function curve of the ratio of the infected nodes in the network and the function curve of the infected nodes in the network when information propagation reaches a steady state by adjusting the value of the channel number of individual perception information;
b2: initializing initial infection probability omega, channel quantity of individual perception information and recovery probability gamma, setting the step length of the individual sensitivity theta to be 0.02 by adjusting the individual sensitivity theta, and obtaining a function curve of the node quantity and theta and a function curve of the ratio of immune nodes to theta in three states in a network when information propagation reaches a steady state;
b3: initializing initial infection probability omega, channel quantity of individual perception information and individual sensitivity theta, setting the step length of gamma to be 0.002 by adjusting recovery probability gamma under the condition that partial infection nodes in the network are recovered to immune nodes, and obtaining function curves of the node quantity and gamma in three states in the network and function curves of the occupation ratio of the immune nodes and gamma when information transmission reaches a steady state;
b4: counting the variances of B1, B2 and B3 through 100 iterations to obtain a function curve;
b5: setting a plurality of different network layer numbers, and repeating B1-B4;
b6: replacing the ER network in the model with a BA network, and repeating B1-B5;
in this set of experiments, a phenomenon was obtained in which the ratio of immune nodes does not increase monotonically with increasing probability of recovery, does not coincide exactly with the monotonically increasing theoretical prediction, but rather exhibits a non-monotonic behavior.
The embodiment also correspondingly provides an information collaborative propagation method of the individual sensitivity in the multilayer network, and the data of the embodiment is derived from the world track tournament in 2013 and the data of the world track tournament in 2013 by adopting an information collaborative propagation model of the individual sensitivity in the multilayer network: one user is regarded as a node, various types of social relations among the users are regarded as an edge, and the data of the championship game is abstracted into an undirected network; in the network, users respectively propagate dynamics propagation mechanisms of information in a multilayer network in three layers of networks, namely an RT network, an MT network and an RE network according to a model algorithm provided by the present invention, and compare the dynamics propagation mechanisms with the model, wherein, regarding mainly various types of social relations existing among the users obtained on Twitter, the existing actual network nodes have 88804 edges and 210250 edges, and the method specifically comprises the following steps:
s1: generating a three-layer network with an initial infection probability omega;
s2: initializing the number of individual perception information channels and the recovery probability gamma to be 0, and carrying out iterative updating according to the sensitivity degree theta epsilon (0, 10) of an individual to information;
s3: judging the termination of the iteration condition, updating the number of the infected nodes in the three-layer network, and counting the probability of the infected nodes as PIAnd a variance;
s4: adjusting parameters of the model, reinitializing the number of individual perception information channels and the sensitivity degree theta of an individual to information, and performing iterative updating by recovering the probability gamma which belongs to (0, 1);
s5: judging the termination of the iteration condition, updating the number of susceptible nodes, the number of infected nodes and the number of immune nodes in a three-layer network, and counting the probability of the immune nodes as PRAnd a variance;
s6: the model of the invention is verified in an actual network system.
The method has the advantages that the information transmission process is researched from the perspective of a complex network, and the prevention, control and intervention effects are achieved by combining the characteristics of a real network and the transmission dynamics theory to the information inundation of the real network through the improvement and optimization of the model; the method and the device can effectively analyze how to promote the wide spread of benign information and the inhibition of bad information in the network, further control the bad results caused by the malignant spread to a certain extent for the application in the actual life, and can better provide a relatively safe social network environment for the user.
The above embodiments are described in detail for the purpose of further illustrating the present invention and should not be construed as limiting the scope of the present invention, and the skilled engineer can make insubstantial modifications and variations of the present invention based on the above disclosure.
Claims (7)
1. An individual sensitivity information cooperation propagation model in a multilayer network is characterized by comprising a multilayer complex network modeling module, a numerical simulation module and a real network verification module which are sequentially connected; the multilayer complex network modeling module is used for constructing a network model for modeling; the numerical simulation module is used for carrying out simulation on the model constructed by the multilayer complex network modeling module; the real network verification module simulates the process of realizing information propagation in the real network from a model, and is applied to the actual network according to the propagation dynamics principle, so that the propagation range of information in the real network and the prevention, control and intervention of the malignant information flooding in the network can be better known.
2. The model of claim 1, wherein the multi-layer complex network modeling module models based on an SI model and an SIR model, and the propagation dynamics are involved in the process.
3. The model of claim 2, wherein the SI model network has an initial state in which an individual has two states, namely a susceptible state and an adopted state, and information is propagated in the multi-layer network, and the propagation process includes the following steps:
a1: assuming that a certain number of individuals are infected in the initial state in the network, namely the initial infection probability is unchanged, and the number of the individual acquisition information channels is certain, so that information can be transmitted in the network, and the degree of relation between the individuals in the network is defined, namely the individual sensitivity is theta;
a2: a user in the first network is informed of a message, and the probability of the user being infected is P1＝(1/3)θ；
A3: a user in the layer two network is informed of a message, and the probability of the user being infected is P2＝(2/3)θ；
A4: when the user is informed of the same information in all three networks, the user can certainly trust the information, and the probability is 1; the kinetic processes when susceptible individuals are infected are:
S+I→2I
wherein S is susceptible state, and I is adopted state.
4. The model of claim 2, wherein the SIR model is used in the initial state of the network to provide three states, namely a susceptible state, an adopted state and an immune state, and the infected individual in the multi-layer network recovers with probability γ, and the dynamic process of the infected individual recovering to the information immune state is as follows:
I→R
wherein I is the adopted state, R is the immune state, and the susceptible state is represented by S.
5. The model of claim 1, wherein the numerical simulation module comprises social influence factors, and the social influence factors comprise individual sensitivity, channel number of individual perception information, number of infected individuals in an initial state, and recovery probability of infected individuals.
6. The model of claim 5, wherein the analysis process of the numerical simulation module for information cooperative propagation in the multi-layer network comprises the following steps:
b1: initializing initial infection probability omega and individual sensitivity theta, and under the condition that nodes in the network are not recovered to be immune nodes, obtaining a function curve of the ratio of the infected nodes in the network and the function curve of the infected nodes in the network when information propagation reaches a steady state by adjusting the value of the channel number of individual perception information;
b2: initializing initial infection probability omega, channel quantity of individual perception information and recovery probability gamma, setting the step length of the individual sensitivity theta to be 0.02 by adjusting the individual sensitivity theta, and obtaining the function curves of the node quantity and function curves of the node quantity under three states in the network and the function curves of the ratio of immune nodes to theta when information propagation reaches a steady state;
b3: initializing initial infection probability omega, channel quantity of individual perception information and individual sensitivity theta, setting the step length of gamma to be 0.002 by adjusting recovery probability gamma under the condition that partial infected nodes in the network are recovered to immune nodes, and obtaining function curves of the node quantity and gamma in three states in the network and the function curves of the ratio of the immune nodes to gamma when information propagation reaches a steady state;
b4: counting the variances of B1, B2 and B3 through 100 iterations to obtain a function curve;
b5: setting a plurality of different network layer numbers, and repeating B1-B4;
b6: the ER network in the model is replaced by the BA network, and B1-B5 is repeated.
7. An individual sensitivity information collaborative propagation method in a multilayer network, which adopts an individual sensitivity information collaborative propagation model in any one of claims 1-6, and is characterized by comprising the following steps:
s1: generating a three-layer network with an initial infection probability omega;
s2: initializing the number of individual perception information channels and the recovery probability gamma to be 0, and carrying out iterative updating according to the sensitivity degree theta epsilon (0, 10) of an individual to information;
s3: judging the termination of the iteration condition, updating the number of the infected nodes in the three-layer network, and counting the probability of the infected nodes as PIAnd a variance;
s4: adjusting parameters of the model, and reinitializing the number of individual perception information channels and the sensitivity degree theta of an individual to information so as to recover the probability gamma, which belongs to (0, 1), and performing iterative updating;
s5: judging the termination of the iteration condition, updating the number of susceptible nodes, the number of infected nodes and the number of immune nodes in a three-layer network, and counting the probability of the immune nodes as PRAnd a variance;
s6: the model of the invention is verified in an actual network system.
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Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108230170A (en) * | 2017-12-20 | 2018-06-29 | 重庆邮电大学 | Towards the multi information and multidimensional network Information Propagation Model and method of social networks |
| US10178120B1 (en) * | 2015-07-23 | 2019-01-08 | Hrl Laboratories, Llc | Method for determining contagion dynamics on a multilayer network |
| CN109903853A (en) * | 2019-01-08 | 2019-06-18 | 南京邮电大学 | Double-layer network propagation model construction method based on individual susceptibility Yu mass media's influence power |
-
2020
- 2020-06-18 CN CN202010561399.6A patent/CN111881615A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US10178120B1 (en) * | 2015-07-23 | 2019-01-08 | Hrl Laboratories, Llc | Method for determining contagion dynamics on a multilayer network |
| CN108230170A (en) * | 2017-12-20 | 2018-06-29 | 重庆邮电大学 | Towards the multi information and multidimensional network Information Propagation Model and method of social networks |
| CN109903853A (en) * | 2019-01-08 | 2019-06-18 | 南京邮电大学 | Double-layer network propagation model construction method based on individual susceptibility Yu mass media's influence power |
Non-Patent Citations (2)
| Title |
|---|
| 李娟等: "基于复杂网络的信息传播研究", 信息通信, no. 09 * |
| 韩忠明等: "基于内容的热点话题传播模型", 智能系统学报, no. 03, pages 1 - 2 * |
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