CN113807976A - Social network information propagation implementation method and system based on threshold value self-adaptive change - Google Patents
Social network information propagation implementation method and system based on threshold value self-adaptive change Download PDFInfo
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Abstract
The method for realizing network information propagation based on threshold value self-adaptive change comprises the following steps: constructing a random network; distributing initial thresholds to all nodes in the network, setting the initial states of all nodes in the network to be S states, and randomly selecting one node as a propagation source; randomly selecting one strategy from three threshold value self-adaptive change strategies; information is transmitted according to a threshold model mechanism, and information cascade transmission of two transmission modes, namely 'time difference' and 'no time difference' is carried out from one node; and calculating the occupation of the number of the non-S-state nodes in the network in the total number of the nodes after the propagation is finished as a final cascade range. The system of the present invention comprises: the device comprises a random network construction module, an initialization module, a threshold value self-adaptive change module, an information cascade propagation module and a cascade range calculation module. The invention provides a method for realizing the self-adaptive change of the network information threshold value, which combines a network information threshold value self-adaptive change propagation model and three threshold value self-adaptive change strategies, and can prevent the information propagation in the network.
Description
Technical Field
The invention relates to the field of network information transmission, in particular to a social network information transmission implementation method based on threshold value self-adaptive change and a system for implementing the method.
Background
Modeling information diffusion processes (such as fashion, innovation, viral causes, viewpoints, rumors and the like) is of great significance for analyzing the propagation patterns of information on the social network and further controlling the propagation processes of the information. To date, scientists have proposed a variety of models to describe this process, with the most classical including an independent cascade propagation model and a threshold model.
Threshold models were first proposed by the socialist Granovetter to describe collective behavior and have been widely used to describe a series of binary decision phenomena in economics and sociology. The main idea of this model is that the individuals in the network exhibit cluster behavior, i.e. they make decisions based on the behavior of the neighbors. In 2002, Watts thoroughly studied the physical characteristics of the threshold model and demonstrated the conditions when global concatenation of information would be triggered on a random network. Since then, the model has attracted a wide range of attention from network scientists. Mainly focuses on the following aspects: 1. the impact of the underlying network structure. Centola et al first studied the case of small-world networks, indicating that random edges in small-world networks can hinder the propagation behavior of complexity. Galstyan researches the influence of the weakly coupled community network structure, and the result shows that the information propagation speed can present a double-layer phenomenon. Payne et al consider the effects of a degree-associative network, and they find that positive associations between nodes (i.e., a node with a large degree is connected to a node with a large degree) can enlarge the cascading window. Yagan et al examined the situation of multiple networks and showed that the propagation weights of different types of edges (the bias towards propagating a certain product or information) would have a great impact on the propagation dynamics. 2. Influence of initial seed size. Gleeson et al developed a mean field method to study the effect of various seeds on information cascade propagation. 3. The effect of different threshold distributions. Karampournotis et al studied the Gaussian distribution of node thresholds, and found that the proportion of people in the final active state changes non-monotonically with the increase of the standard deviation (the parameter of the Gaussian distribution function, which represents the degree of dispersion).
While so much research has been done on threshold models, most current research assumes that node thresholds remain unchanged throughout the propagation. However, with the rapid development of computer science and big data field in recent years, people find out that a real social system in some online empirical data shows some key factors ignored by a traditional threshold model (and an extension model thereof). For example, when a person takes a new product or watches a new movie, the product is evaluated, some negative feedback will affect other users to some extent, resulting in a change in their "threshold". The analysis of the yelp data set shows that the bad comments received by a restaurant have certain influence on the subsequent business amount of the restaurant. This demonstrates to some extent that the individual adoption threshold varies adaptively with the feedback of neighboring nodes.
Disclosure of Invention
In order to overcome the defect that the node threshold is kept to be a constant value (namely, the node threshold does not change along with time) in the existing threshold model research, the invention provides a social network information propagation implementation method based on threshold self-adaptive change, and provides a threshold self-adaptive change propagation model combined with a time difference mechanism and different threshold self-adaptive change strategies.
The technical conception of the invention is as follows: according to the invention, through simulation experiments, the influence of the negative comments in the commodity marketing or message transmission process on information transmission is theoretically explained, and researchers are helped to better understand the influence caused by the negative comments in the marketing process.
The technical scheme adopted by the invention for solving the technical problems is as follows:
the network information propagation model based on the threshold value adaptive change comprises four node states: non-contact status S, contacted non-comment status E, contacted and comment status IPContacted and published negative comment status IN(ii) a The method for realizing the network information propagation based on the threshold value self-adaptive change comprises the following steps:
s1: constructing a network G ═ (V, E), where V ═ V { (V }1,v2,...,vNThe is the network node set, N is the total number of nodes,
connecting edge sets for the network;
s2: initialization: to the networkAll nodes in the system are assigned the same initial threshold value
Randomly selecting a node as an information propagation source;
s3: three threshold adaptive change strategies are considered: (1) s-state node threshold following neighbor INThe number of state nodes increases linearly; (2) s-state node threshold following neighbor INThe number of state nodes grows exponentially; (3) s-state node threshold following neighbor INThe number of state nodes is logarithmically increased;
s4: the network information cascade transmission process considers two transmission modes, namely 'existence time difference' and 'nonexistence time difference', and is different in that whether the S-state node is changed into I immediately with probability pPA state node; the node transmits information according to a threshold model mechanism, changes the node state of S-state nodes meeting a threshold condition according to the existence of time difference and the probability p until new non-S-state nodes do not appear in the network, and finishes the transmission;
s5: and calculating the final cascade range of information transmission, carrying out multiple experiments on each group of experiment indexes, and calculating the ratio of the number of non-S-state nodes in the network in the total number of the nodes after the transmission is finished.
Further, the step S1 specifically includes:
considering a random network with an average degree z, the number of nodes is 1000, and the number of connected edges satisfies
Further, the step S2 specifically includes:
each node v in the networkiAll have node thresholds, recorded as
For all nodes in the network, the same initial threshold is given
Setting initial states of all nodes of networkIn S state, a node is randomly selected to be in I statePState.
Further, the step S3 specifically includes:
the invention designs three threshold value self-adaptive change strategies, and assumes S-state node viIn the neighborhood of (1)NNumber of nodes of state nneg: first, a linear threshold adaptive change strategy, node viIs at a threshold value of
Increase on the basis of
The second is an exponential change strategy, node viIs at a threshold value of
Increase on the basis of
The third is a logarithmic change strategy, node viIs at a threshold value of
Increase on the basis of
Wherein the variable delta is a node threshold speed increase index, and the value of the variable delta is set to 0.01; the node threshold ranges from 0, 1]。
Further, the step S4 specifically includes:
in the information transmission process of step S4, there are two cases of "existence of time difference" and "nonexistence of time difference"; "time difference" means, S-state node viConversion to I with probability p immediately after satisfaction of a threshold decision formulaPThe state node is firstly converted into the E state and then converted into the I state with probability p after a period of time delta tPState;
the propagation process of "absence of time difference" is specifically as follows:
s4.1: randomly selecting a networkOne node in the system is used as a source for making comments, a positive comment is made, and the state of the positive comment is set to be IPState;
s4.2: at the beginning of each step of information dissemination, for the S-state node v in the current networkiAccording to I in its neighboursNNumber of state nodes nnegAnd randomly selecting a threshold value self-adaptive change strategy to update the node threshold value
S4.3: randomly selecting an S-state node viSuppose that the number of positive comments made in the neighbor node is nposThe following threshold decision formula is adopted:
where k is node viThe number of neighbors of (2); for S-state node v satisfying threshold decision formula (1)iGenerating a random number r to be compared with a probability value p, and when the random number r is less than or equal to p, the node viState transition ofPState otherwise will become INState;
s4.4: continuously repeating the steps S4.1-S4.3 until no new non-S-state node appears in the network;
the propagation process of "existence of time difference" is specifically as follows:
t4.1: setting all node clocks to be 0; randomly selecting a node in the network as a source for making a comment, making a positive comment, and setting the state of the positive comment as IPState;
t4.2: at the beginning of each step of information propagation, 1 is added to the clock of each E-state node for updating, and for the E-state node reaching a certain time difference, the E-state node is converted into I by the probability pPState node, converted to I with probability 1-pNA state node; and for the S-state node v in the present networkiAccording to I in its neighboursNNumber of state nodes nnegUpdating its node threshold
T4.3: randomly selecting an S-state node viAssume I in its neighbor nodePThe number of state nodes is npos，INThe number of state nodes is nnegAnd the number of E-state nodes is nmidThe following threshold decision formula is adopted:
for S-state node v satisfying threshold decision formula (2)iThe node state is immediately changed into the E state, and a node clock is set to be 1;
t4.4: and continuously repeating the steps T4.1-T4.4 until no new non-S-state nodes appear in the network.
Further, the step S5 specifically includes:
changing network mean value z and S-state node transition to IPAnd (3) carrying out experiments for 10 times by N times, calculating the ratio of the number of non-S-state nodes in the network after the propagation is finished in the total number of the network nodes, taking the ratio as the final information propagation cascade range, and calculating the average value of multiple experiments.
The system for realizing the social network information propagation realization method based on the threshold value self-adaption change comprises the following steps: the device comprises a random network construction module, an initialization module, a threshold value self-adaptive change module, an information cascade propagation module and a cascade range calculation module;
the random network construction module constructs a random network comprising N nodes, wherein the nodes are randomly connected with one another, and the network average degree is recorded as z;
the initialization module gives all nodes in the network the same initial threshold value
Each node v in the networkiAll have node thresholds, recorded as
Setting the initial state of all nodes in the network to S state, and randomly selecting one node to make it in I statePState;
the threshold value self-adaptive change module comprises two types of threshold value self-adaptive change strategies: the first type is a linear threshold adaptive change strategy, S-state node viAccording to the value of I in its neighboursNNumber of state nodes nnegLinear growth; the second type is a nonlinear threshold adaptive change strategy, which comprises two nonlinear strategies, one is an S-state node viAccording to the value of I in its neighboursNNumber of state nodes nnegExponentially increased, and the other is an S-state node viAccording to the value of I in its neighboursNNumber of state nodes nnegThe growth is logarithmic;
the information cascade propagation module comprises two propagation modes, namely 'existence time difference' and 'nonexistence time difference', and is different from the mode that whether an S-state node is changed into I immediately with probability pPA state node; randomly selecting a node as an information transmission source, performing information transmission according to a threshold model mechanism, and changing the node state of S-state nodes meeting a threshold condition according to the fact whether time difference exists or not and the probability p until new non-S-state nodes do not appear in the network, wherein the transmission is finished;
the cascade range calculation module carries out 10 × N experiments on each group of experiment indexes z and p and different threshold adaptive change strategies, calculates the ratio of the number of non-S-state nodes in the network after the propagation is finished in the total number of the network nodes, and uses the ratio as the final information propagation cascade range, and calculates the average value of multiple experiments;
the random network construction module, the initialization module, the threshold value self-adaptive change module, the information cascade propagation module and the cascade range calculation module are connected in sequence.
The invention has the beneficial effects that:
(1) the invention provides a social network information propagation realization method based on threshold value self-adaptive change, which visually displays and explains the propagation process of positive and negative comments on the network and provides new insight for researchers to better understand the information propagation process in the network;
(2) meanwhile, a time difference mechanism is added, different threshold self-adaptive strategies are designed, the influence of negative comments on information transmission in a real marketing process or an information transmission process is simulated, and good help is provided for further research on collective human behaviors.
Drawings
FIG. 1 is a flow chart of the algorithm of the present invention;
FIG. 2 is a schematic diagram of a random network according to an embodiment of the present invention;
FIG. 3 is a time-varying trend graph of the cascading range of network information when the time difference does not exist;
FIG. 4 is a time-varying trend graph of the cascading range of the network information when the time difference exists;
fig. 5 is a schematic diagram of the system architecture of the present invention.
Detailed Description
The following detailed description of embodiments of the invention is provided in connection with the accompanying drawings.
Referring to fig. 1 to 4, a social network information propagation implementation method and system based on threshold adaptive change includes the following steps:
the invention uses an artificial random network for modeling analysis, the total number N of nodes is 1000, the value range of the network average degree z is 0-8, and the nodes are converted into IPThe value range of the probability p of the state node is 0-1, and the experiment times of each group of indexes are 10 x N times;
s1: a random network G ═ (V, E) is constructed as shown in fig. 2, where V ═ V1,v2,...,vNThe is a random network node set, N is the total number of nodes,
connecting edges randomly between nodes for a random network connecting edge set, specifically comprising:
considering a random network with an average degree z, the number of nodes is 1000, and the number of connected edges satisfies
S2: assigning the same initial threshold to all nodes in a network
Randomly selecting a node as an information propagation source; the method specifically comprises the following steps:
each node v in the networkiAll have node thresholds, recorded as
For all nodes in the network, the same initial threshold is given
Setting the initial state of all nodes in the network to S state, and randomly selecting one node to make it in I statePState.
S3: three threshold adaptive change strategies are considered: (1) s-state node threshold following neighbor INThe number of state nodes increases linearly; (2) s-state node threshold following neighbor INThe number of state nodes grows exponentially; (3) s-state node threshold following neighbor INThe number of state nodes is logarithmically increased; the method specifically comprises the following steps:
the invention designs three threshold value self-adaptive change strategies, and assumes S-state node viIn the neighborhood of (1)NNumber of nodes of state nneg: first, a linear threshold adaptive change strategy, node viIs at a threshold value of
Increase on the basis of
The second is an exponential change strategy, node viIs at a threshold value of
Increase on the basis of
The third is a logarithmic change strategy, node viIs at a threshold value of
Increase on the basis of
Wherein the variable delta is a node threshold speed increase index, and the value of the variable delta is set to 0.01; the node threshold ranges from 0, 1]。
S4: the network information cascade transmission process considers two transmission modes, namely 'existence time difference' and 'nonexistence time difference', and is different in that whether the S-state node is changed into I immediately with probability pPA state node; the node transmits information according to a threshold model mechanism, changes the node state of S-state nodes meeting a threshold condition according to the existence of time difference and the probability p until new non-S-state nodes do not appear in the network, and finishes the transmission; the method specifically comprises the following steps:
the method for realizing social network information propagation based on threshold value self-adaptive change is characterized by comprising the following steps: in the information transmission process of step S4, there are two cases of "existence of time difference" and "nonexistence of time difference"; "time difference" means, S-state node viConversion to I with probability p immediately after satisfaction of a threshold decision formulaPThe state node is converted into E state firstly and then converted into I state with probability p after a period of timePState;
the propagation process of "absence of time difference" is specifically as follows:
s4.1: randomly selecting a node in the network as a source for making a comment, making a positive comment, and setting the state of the positive comment as IPState;
s4.2: at the beginning of each step of information dissemination, for the S-state node v in the current networkiAccording to I in its neighboursNNumber of state nodes nnegAnd randomly selecting a threshold value self-adaptive change strategy to update the node threshold value
S4.3: randomly selecting an S-state node viSuppose that the number of positive comments made in the neighbor node is nposThe following threshold decision formula is adopted:
where k is node viThe number of neighbors of (2); for S-state node v satisfying threshold decision formula (1)iGenerating a random number r to be compared with a probability value p, and when the random number r is less than or equal to p, the node viState transition ofPState otherwise will become INState;
s4.4: continuously repeating the steps S4.1-S4.3 until no new non-S-state node appears in the network;
the propagation process of "existence of time difference" is specifically as follows:
t4.1: setting all node clocks to be 0; randomly selecting a node in the network as a source for making a comment, making a positive comment, and setting the state of the positive comment as IPState;
t4.2: at the beginning of each step of information propagation, 1 is added to the clock of each E-state node for updating, and for the E-state node reaching a certain time difference, the E-state node is converted into I by the probability pPState node, converted to I with probability 1-pNA state node; and for the S-state node v in the present networkiAccording to I in its neighboursNNumber of state nodes nnegUpdating its node threshold
T4.3: randomly selecting an S-state node viAssume I in its neighbor nodePThe number of state nodes is npos，INThe number of state nodes is nnegAnd the number of E-state nodes is nmidThe following threshold decision formula is adopted:
for S-state node v satisfying threshold decision formula (2)iThe node state is immediately changed into the E state, and a node clock is set to be 1;
t4.4: continuously repeating the steps T4.1-T4.4 until no new non-S-state nodes appear in the network;
s5: calculating the final cascade range of information propagation, carrying out multiple experiments on each group of experiment indexes, and calculating the proportion of the number of non-S-state nodes in the network in the total number of the nodes after the propagation is finished, wherein the method specifically comprises the following steps:
changing network mean value z and S-state node transition to IPAnd (3) carrying out experiments for 10 times by N times, calculating the ratio of the number of non-S-state nodes in the network after the propagation is finished in the total number of the network nodes, taking the ratio as the final information propagation cascade range, and calculating the average value of multiple experiments.
The invention also includes a social network information propagation implementation system based on threshold value adaptive change, as shown in fig. 5, including: the random network construction module, the initialization module, the threshold value self-adaptive change module, the information cascade propagation module and the cascade range calculation module are sequentially connected;
the random network construction module constructs a random network comprising N nodes, wherein the nodes are randomly connected with one another, and the network average degree is recorded as z; the method specifically comprises the following steps:
considering a random network with an average degree z, the number of nodes is 1000, and the number of connected edges satisfies
The initialization module gives all nodes in the network the same initial threshold value
Each node v in the networkiIs recorded as
Setting the initial state of all nodes in the network to S state, and randomly selecting one node to make it in I statePState; the method specifically comprises the following steps:
each node v in the networkiAll have node thresholds, recorded as
For all nodes in the network, the same initial threshold is given
Setting the initial state of all nodes in the network to S state, and randomly selecting one node to be in I statePThe states act as a source of propagation.
The threshold value self-adaptive change module comprises two types of threshold value self-adaptive change strategies: the first type is a linear threshold adaptive change strategy, S-state node viAccording to the value of I in its neighboursNNumber of state nodes nnegLinear growth; the second type is a nonlinear threshold adaptive change strategy, which comprises two nonlinear strategies, one is an S-state node viAccording to the value of I in its neighboursNNumber of state nodes nnegExponentially increased, and the other is an S-state node viAccording to the value of I in its neighboursNNumber of state nodes nnegThe growth is logarithmic; when in use, three threshold adaptive change strategies are randomly selected, specifically comprising:
the invention designs three threshold value self-adaptive change strategies, and assumes S-state node viIn the neighborhood of (1)NNumber of nodes of state nneg: first, a linear threshold adaptive change strategy, node viIs at a threshold value of
Increase on the basis of
The second is an exponential change strategy, node viIs at a threshold value of
Increase on the basis of
The third is a logarithmic change strategy, node viIs at a threshold value of
Increase on the basis of
Wherein the variable delta is a node threshold speed increase index, and the value of the variable delta is set to 0.01; the node threshold ranges from 0, 1](ii) a Three threshold adaptive change strategies are randomly selected.
The information cascade propagation module comprises two propagation modes, namely 'existence time difference' and 'nonexistence time difference', and is different from the mode that whether an S-state node is changed into I immediately with probability pPA state node; randomly selecting a node as an information transmission source, performing information transmission according to a threshold model mechanism, and changing the node state of S-state nodes meeting a threshold condition according to the fact whether time difference exists or not and the probability p until new non-S-state nodes do not appear in the network, wherein the transmission is finished; the method specifically comprises the following steps:
the propagation process of "absence of time difference" is specifically as follows:
s4.1: randomly selecting a node in the network as a source for making a comment, making a positive comment, and setting the state of the positive comment as IPState;
s4.2: at the beginning of each step of information dissemination, for the S-state node v in the current networkiAccording to I in its neighboursNNumber of state nodes nnegAnd randomly selecting a threshold value self-adaptive change strategy to update the node threshold value
S4.3: randomly selecting an S-state node viSuppose that the number of positive comments made in the neighbor node is nposAccording to a threshold value determination formula:
where k is node viThe number of neighbors of (2); for S-state node v satisfying threshold decision formula (1)iGenerating a random number r to be compared with a probability value p, and when the random number r is less than p, the node viState transition ofPState otherwise will become INState;
s4.4: continuously repeating the steps S4.1-S4.3 until no new non-S-state node appears in the network;
the propagation process of "existence of time difference" is specifically as follows:
t4.1: setting all node clocks to be 0; randomly selecting a node in the network as a source for making a comment, making a positive comment, and setting the state of the positive comment as IPState;
t4.2: at the beginning of each step of information propagation, 1 is added to the clock of each E-state node for updating, and for the E-state nodes meeting the condition of time difference, the E-state nodes are converted into I by probability pPState node, converted to I with probability 1-pNA state node; and for the S-state node v in the present networkiAccording to I in its neighboursNNumber of state nodes nnegAnd randomly selecting a threshold value self-adaptive change strategy to update the node threshold value
T4.3: randomly selecting an S-state node viAssume I in its neighbor nodePThe number of state nodes is npos，INThe number of state nodes is nnegAnd the number of E-state nodes is nmidAccording to a threshold value determination formula:
for S-state node v satisfying threshold decision formula (2)iNode state immediately transitions to EState, and set the node clock to 1;
t4.4: and continuously repeating the steps T4.1-T4.4 until no new non-S-state nodes appear in the network.
The cascade range calculation module performs 10 × N experiments on each group of experiment indexes z and p and different threshold adaptive change strategies, calculates the ratio of the number of non-S-state nodes in the network after the propagation is finished in the total number of network nodes, and uses the ratio as a final information propagation cascade range, and calculates the average value of multiple experiments, specifically comprising:
changing network mean value z and S-state node transition to IPAnd (3) selecting different threshold self-adaptive change strategies according to the probability p of the state nodes, carrying out 10N-10-1000-10000 times in each group of experiments, calculating the ratio of the number of non-S state nodes in the network to the total number of network nodes after the propagation under each group of indexes is finished, taking the ratio as the final information propagation cascade range, and calculating the average value of multiple experiments.
The random network construction module, the threshold initialization module, the threshold self-adaptive change module, the information cascade propagation module and the cascade range calculation module are connected in sequence.
The embodiments described in this specification are merely illustrative of implementations of the inventive concept and the scope of the present invention should not be considered limited to the specific forms set forth in the embodiments but rather by the equivalents thereof as may occur to those skilled in the art upon consideration of the present inventive concept.
Claims (8)
1. The social network information propagation implementation method based on threshold value self-adaptive change is characterized by comprising the following steps: the social network information propagation model based on threshold value self-adaptive change comprises four node states: non-contact status S, contacted non-comment status E, contacted and comment status IPContacted and published negative comment status IN(ii) a The method comprises the following steps:
s1: constructing a network G ═ (V, E), where V ═ V { (V }1,v2,...,vNThe is the network node set, N is the total number of nodes,
connecting edge sets for the network;
s2: initialization: assigning the same initial threshold to all nodes in a network
Randomly selecting a node as an information propagation source;
s3: three threshold adaptive change strategies are considered: (1) s-state node threshold following neighbor INThe number of state nodes increases linearly; (2) s-state node threshold following neighbor INThe number of state nodes grows exponentially; (3) s-state node threshold following neighbor INThe number of state nodes is logarithmically increased;
s4: the network information cascade transmission process considers two transmission modes, namely 'existence time difference' and 'nonexistence time difference', and is different in that whether the S-state node is changed into I immediately with probability pPA state node; the node transmits information according to a threshold model mechanism, changes the node state of S-state nodes meeting a threshold condition according to the existence of time difference and the probability p until new non-S-state nodes do not appear in the network, and finishes the transmission;
s5: and calculating the final cascade range of information transmission, carrying out multiple experiments on each group of experiment indexes, and calculating the ratio of the number of non-S-state nodes in the network in the total number of the nodes after the transmission is finished.
2. The method for implementing social network information propagation based on threshold adaptive change as claimed in claim 1, wherein: the step S1 specifically includes:
considering a random network with an average degree z, the number of nodes is 1000, and the number of connected edges satisfies
3. The method for implementing social network information propagation based on threshold adaptive change as claimed in claim 1, wherein: the step S2 specifically includes:
each node v in the networkiAll have node thresholds, recorded as
For all nodes in the network, the same initial threshold is given
Setting the initial state of all nodes in the network to S state, and randomly selecting one node to make it in I statePState.
4. The method for implementing social network information propagation based on threshold adaptive change as claimed in claim 1, wherein: the step S3 specifically includes:
three kinds of threshold value self-adaptive change strategies are designed, and S-state node viIn the neighborhood of (1)NNumber of nodes of state nneg: first, a linear threshold adaptive change strategy, node viIs at a threshold value of
Increase on the basis of
The second is an exponential change strategy, node viIs at a threshold value of
Increase on the basis of
The third is a logarithmic change strategy, node viIs at a threshold value of
Increase on the basis of
Wherein the variable delta is a node threshold speed increase index, and the value of the variable delta is set to 0.01; the node threshold ranges from 0, 1]。
5. The method for implementing social network information propagation based on threshold adaptive change as claimed in claim 1, wherein: in the information transmission process of step S4, there are two cases of "existence of time difference" and "nonexistence of time difference"; "time difference" means, S-state node viConversion to I with probability p immediately after satisfaction of a threshold decision formulaPThe state node is firstly converted into the E state and then converted into the I state with probability p after a period of time delta tPState; the propagation process of "absence of time difference" is specifically as follows:
s4.1: randomly selecting a node in the network as a source for making a comment, making a positive comment, and setting the state of the positive comment as IPState;
s4.2: at the beginning of each step of information dissemination, for the S-state node v in the current networkiAccording to I in its neighboursNNumber of state nodes nnegAnd randomly selecting a threshold value self-adaptive change strategy to update the node threshold value
S4.3: randomly selecting an S-state node viThe number of positive comments made in the neighbor nodes is nposThe following threshold decision formula is adopted:
where k is node viThe number of neighbors of (2); for S-state node v satisfying threshold decision formula (1)iGenerating a random number r to be compared with a probability value p, and when the random number r is less than or equal to p, the node viState transition ofPState otherwise will become INState;
s4.4: and continuously repeating the steps S4.1-S4.3 until no new non-S-state nodes appear in the network.
6. The method for implementing social network information propagation based on threshold adaptive change as claimed in claim 1, wherein: in the information propagation process of step S4, the propagation process of "having a time difference" is specifically as follows:
t4.1: setting all node clocks to be 0; randomly selecting a node in the network as a source for making a comment, making a positive comment, and setting the state of the positive comment as IPState;
t4.2: at the beginning of each step of information propagation, 1 is added to the clock of each E-state node for updating, and for the E-state node reaching a certain time difference, the E-state node is converted into I by the probability pPState node, converted to I with probability 1-pNA state node; and for the S-state node v in the present networkiAccording to I in its neighboursNNumber of state nodes nnegUpdating its node threshold
T4.3: randomly selecting an S-state node viIn its neighbor node IPThe number of state nodes is npos，INThe number of state nodes is nnegAnd the number of E-state nodes is nmidThe following threshold decision formula is adopted:
for S-state node v satisfying threshold decision formula (2)iThe node state is immediately changed into the E state, and a node clock is set to be 1;
t4.4: and continuously repeating the steps T4.1-T4.4 until no new non-S-state nodes appear in the network.
7. The method for implementing social network information propagation based on threshold adaptive change as claimed in claim 1, wherein: the step S5 is specifically as follows:
changing network mean value z and S-state node transition to IPAnd (3) carrying out experiments for 10 times by N times, calculating the ratio of the number of non-S-state nodes in the network after the propagation is finished in the total number of the network nodes, taking the ratio as the final information propagation cascade range, and calculating the average value of multiple experiments.
8. The system for implementing the social network information propagation implementation method based on threshold value adaptive change of claim 1 comprises: the device comprises a random network construction module, an initialization module, a threshold value self-adaptive change module, an information cascade propagation module and a cascade range calculation module;
the random network construction module constructs a random network comprising N nodes, wherein the nodes are randomly connected with one another, and the network average degree is recorded as z;
the initialization module gives all nodes in the network the same initial threshold value
Each node v in the networkiAll have node thresholds, recorded as
Setting the initial state of all nodes in the network to S state, and randomly selecting one node to make it in I statePState;
the threshold value self-adaptive change module comprises two types of threshold value self-adaptive change strategies: the first type is a linear threshold adaptive change strategy, S-state node viAccording to the value of I in its neighboursNNumber of state nodes nnegLinear growth; the second type is a nonlinear threshold adaptive change strategy, which comprises two nonlinear strategies, one is an S-state node viAccording to the value of I in its neighboursNNumber of state nodes nnegExponentially increased, and the other is an S-state node viAccording to the value of I in its neighboursNNumber of state nodes nnegThe growth is logarithmic; when used, three kinds of threshold values are selfRandomly selecting a change strategy;
the information cascade propagation module comprises two propagation modes, namely 'existence time difference' and 'nonexistence time difference', and is different from the mode that whether an S-state node is changed into I immediately with probability pPA state node; randomly selecting a node as an information transmission source, performing information transmission according to a threshold model mechanism, and changing the node state of S-state nodes meeting a threshold condition according to the fact whether time difference exists or not and the probability p until new non-S-state nodes do not appear in the network, wherein the transmission is finished;
the cascade range calculation module carries out 10 × N experiments on each group of experiment indexes z and p and different threshold adaptive change strategies, calculates the ratio of the number of non-S-state nodes in the network after the propagation is finished in the total number of the network nodes, and uses the ratio as the final information propagation cascade range, and calculates the average value of multiple experiments;
the random network construction module, the initialization module, the threshold value self-adaptive change module, the information cascade propagation module and the cascade range calculation module are connected in sequence.
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