CN109191318B - Target searching method and device for effectively contributing to incentive information propagation - Google Patents

Target searching method and device for effectively contributing to incentive information propagation Download PDF

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CN109191318B
CN109191318B CN201810828138.9A CN201810828138A CN109191318B CN 109191318 B CN109191318 B CN 109191318B CN 201810828138 A CN201810828138 A CN 201810828138A CN 109191318 B CN109191318 B CN 109191318B
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王睿
王盈
常飞
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University of Science and Technology Beijing USTB
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Abstract

The invention provides a target searching method and device for effectively contributing to incentive information propagation, which can improve the information propagation efficiency and accuracy. The method comprises the following steps: judging whether the propagation contribution value of the node is greater than or equal to a preset propagation threshold value or not; if yes, the node is an effective contribution node, and awards are given to the information source node of the effective contribution node so as to promote the information in the network to be spread to the more valuable node. The invention relates to the field of information propagation and construction in social networks.

Description

Target searching method and device for effectively contributing to incentive information propagation
Technical Field
The invention relates to the field of information propagation and construction in social networks, in particular to a target searching method and device for effectively contributing to incentive information propagation.
Background
In recent years, social networks have evolved into an unprecedented information dissemination platform. Interactions between self-networks (where self-networks are short for self (ego) -centric social networks) can allow for widespread dissemination of information, which can provide a convenient way to solve specific problems, such as finding missing people, but solutions require a large number of participants to disseminate information, and the directionality of dissemination is also important in approaching the target.
In the prior art, a node volunteer transmission mode is generally adopted to search a target node, and the information transmission efficiency and the accuracy are low.
Disclosure of Invention
The invention aims to provide a target searching method and a target searching device for effectively contributing to incentive information propagation, and aims to solve the problems of low information propagation efficiency and low accuracy caused by searching a target node by using a node volunteer propagation mode in the prior art.
To solve the above technical problem, an embodiment of the present invention provides a target finding method for effectively contributing to incentive information propagation, including:
judging whether the propagation contribution value of the node is greater than or equal to a preset propagation threshold value or not;
if yes, the node is an effective contribution node, and awards are given to the information source node of the effective contribution node so as to promote the information in the network to be spread to the more valuable node.
Further, before determining whether the propagation contribution value of the node is greater than or equal to a preset propagation threshold, the method further includes:
for the same message, judging whether a node can receive the transmission from different central nodes;
if not, taking the only central node which transmits the message to the certain node as the only information source node of the certain node;
and if so, setting the certain node as a common node, enabling the central nodes in each self network to compete the common node one by one, and selecting the central node with the largest effort cost in the competition process as the unique information source node of the competing common node.
Further, the competition process employs a full auction mechanism.
Further, the unique information source node v of the common nodewinExpressed as:
Figure BDA0001742953060000021
Figure BDA0001742953060000022
wherein n represents the number of central nodes actually participating in the competition,
Figure BDA0001742953060000023
indicating a competing central node viThe cost of the best effort to compete is won,
Figure BDA0001742953060000024
for participating in the central node v of competitioniThe degree of effort actually expended to obtain the reward, izfor participating in the central node v of competitioniObtaining the lowest effort degree required by the reward, wherein lambda represents the estimated value of the total number of the central nodes participating in the competition, e represents a natural constant, F (z) is a distribution function of the effort degree z, and F-1(z) represents the reciprocal of F (z), F' (z) represents the first derivative of F (z), and the central node viDegree of effort z paid to obtain a rewardiAnd a prize wi(M) toHas a relation function of zi＝wi(M)，
Figure BDA0001742953060000025
Is wiThe inverse function of (M).
Further, the determining whether the propagation contribution value of the node is greater than or equal to a preset propagation threshold value includes:
determining the value of the node to the message propagation;
determining the effective transmission rate of the node;
taking the product of the value brought by the node to the message propagation and the propagation effective rate of the node as the propagation contribution value of the node;
and judging whether the propagation contribution value of the node is greater than or equal to a preset propagation threshold value.
Further, let viBeing any node in the self network G, node viValue to message dissemination
Figure BDA0001742953060000026
Comprises the following steps:
Figure BDA0001742953060000027
Figure BDA0001742953060000028
Figure BDA0001742953060000031
wherein, ω isIAnd ωsWeights, I, representing node influence and attribute similarity, respectivelyiRepresenting a node viThe influence of (a) on the magnetic field,
Figure BDA0001742953060000032
representing a node viAnd a target node vtarSimilarity of attributes, kiIs a nodeviOut of degree of (k)jRepresents the degree of departure of the jth node in the ego-network G, | G | represents the total number of nodes in the ego-network G, and att (-) represents the set of attributes of the nodes.
Further, node viEffective rate of propagation ofiComprises the following steps:
qi＝p(θi,ρi|Si＝s)＝p(θi|Si＝s)·p(ρi|Si＝s)
wherein, p (theta)i|SiS) is the probability of successful propagation, p (ρ)i|SiS) probability of propagating will, θiRepresenting a node viPropagation probability of piRepresenting a node viS denotes the node viFrom central node to node viWhether the propagation of (2) was successful, SiIs a random variable with a value s.
Further, let viInformation source node for effectively contributing node in self network, viIs given by the reward function M (v)i) Comprises the following steps:
Figure BDA0001742953060000033
wherein KL is divergence and is used for representing a central node viThe difference degree between the initial contribution and the final contribution is the propagation degree, and corresponding rewards are given to the propagation of the initial contribution and the final contribution; k is the reward paid by the unit difference degree;
Figure BDA0001742953060000037
is a central node viInitial contribution when not participating in propagation;
Figure BDA0001742953060000034
Figure BDA0001742953060000038
representing a central node viWhen the propagation is stopped, the central node viAll the neighbor nodes v propagated tojIs provided withEffective propagation contribution
Figure BDA0001742953060000036
The sum, t, represents the number of neighbor nodes with significant propagation contributions.
An embodiment of the present invention further provides a target searching apparatus for effectively contributing to incentive information propagation, including: an effective contribution incentive module, the effective contribution incentive module comprising: a judgment submodule and an excitation submodule;
the judging submodule is used for judging whether the propagation contribution value of the node is greater than or equal to a preset propagation threshold value or not;
and the excitation submodule is used for determining the node as an effective contribution node if the propagation contribution value of the node is greater than or equal to a preset propagation threshold value, and giving a reward to the information source node of the effective contribution node so as to promote the message in the network to propagate to the more valuable node.
Further, the system further comprises: a node competition module;
the node competition module is used for judging whether a node can receive the transmission from different central nodes or not for the same message; if not, taking the only central node which transmits the message to the certain node as the only information source node of the certain node; and if so, setting the certain node as a common node, enabling the central nodes in each self network to compete the common node one by one, and selecting the central node with the largest effort cost in the competition process as the unique information source node of the competing common node.
The technical scheme of the invention has the following beneficial effects:
in the scheme, whether the propagation contribution value of the node is greater than or equal to a preset propagation threshold value is judged; if so, the node is an effective contribution node, and awards the information source node of the effective contribution node to promote the information in the network to be transmitted to the more valuable node, so that the effective contribution node is utilized to introduce directionality in the information transmission process, the blind transmission of the information can be avoided, the target node can be found more quickly, and meanwhile, an excitation mechanism is adopted in the transmission process, and the participation degree and the transmission enthusiasm of the node are increased compared with a node volunteer transmission mode, so that the information transmission efficiency and accuracy are improved.
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Fig. 1 is a schematic flowchart of a target finding method for effectively contributing to incentive information propagation according to an embodiment of the present invention;
FIG. 2 is a logic diagram of a target finding method for efficient contribution incentive information propagation according to an embodiment of the present invention;
fig. 3 is a schematic diagram of a node contention module according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of an efficient contribution excitation module provided by an embodiment of the present invention;
FIG. 5 is a schematic diagram of a platform payment trend provided by an embodiment of the present invention;
fig. 6 is a schematic structural diagram of a target search apparatus for effectively contributing to incentive information propagation according to an embodiment of the present invention.
Detailed Description
In order to make the technical problems, technical solutions and advantages of the present invention more apparent, the following detailed description is given with reference to the accompanying drawings and specific embodiments.
The invention provides a target searching method and device for effectively contributing to incentive information transmission, aiming at the problems of low information transmission efficiency and low accuracy caused by searching a target node by utilizing a node volunteer transmission mode in the prior art.
Example one
As shown in fig. 1, the target finding method for effectively contributing to incentive information propagation provided by the embodiment of the present invention includes:
s101, judging whether the propagation contribution value of the node is larger than or equal to a preset propagation threshold value or not;
and S102, if yes, the node is an effective contribution node, and awards are given to the information source node of the effective contribution node so as to promote the information in the network to be spread to the more valuable node.
The target searching method for effectively contributing to the propagation of the excitation information judges whether the propagation contribution value of the node is greater than or equal to a preset propagation threshold value; if so, the node is an effective contribution node, and awards the information source node of the effective contribution node to promote the information in the network to be transmitted to the more valuable node, so that the effective contribution node is utilized to introduce directionality in the information transmission process, the blind transmission of the information can be avoided, the target node can be found more quickly, and meanwhile, an excitation mechanism is adopted in the transmission process, and the participation degree and the transmission enthusiasm of the node are increased compared with a node volunteer transmission mode, so that the information transmission efficiency and accuracy are improved.
The target searching method for effectively contributing to incentive information propagation, which is disclosed by the embodiment of the invention, can be used for searching target nodes by other complex networks, such as a traffic network, a crime network and the like, and has the advantages of wide application range and strong universality.
In a specific implementation of the foregoing target finding method for effectively contributing to incentive information propagation, further before determining whether a propagation contribution value of a node is greater than or equal to a preset propagation threshold, the method further includes:
for the same message, judging whether a node can receive the transmission from different central nodes;
if not, taking the only central node which transmits the message to the certain node as the only information source node of the certain node;
and if so, setting the certain node as a common node, enabling the central nodes in each self network to compete the common node one by one, and selecting the central node with the largest effort cost in the competition process as the unique information source node of the competing common node.
In the embodiment, a node competition model can be established, and the established node competition model is used for determining the unique information source node of the common node in a competition common node mode, so that repeated and inefficient propagation of messages is avoided; the method comprises the following specific steps:
step 1-1), determining a common node set by using the established node competition model:
for the same message, judging whether a certain node can receive the transmission from different central nodes, if not, taking the only central node transmitting the message to the certain node as the only information source node of the certain node; if yes, the certain node is set as a common node, and a set formed by the common nodes is called a common node set.
Step 1-2), determining an information source node:
the central nodes in each self-network compete each common node in the common node set one by one, a full auction mechanism is adopted in the competition process, the central nodes participating in the competition are participants, the central node which has the largest effort cost in the competition process is selected as a winner, and the central node becomes the unique information source node of the common node of the competition.
The award given to the winner per competition is M, participant viThe corresponding effort for obtaining the reward is ziAnd z isi∈[0,1]The function of the relationship between the degree of effort and the reward is zi＝wi(M), where w is a differentiable increasing function. The number n of the central nodes actually participating in the competition is unknown to each participant, and n follows poisson distribution, so that the probability that each central node participating in the competition can win the competition is as follows:
Figure BDA0001742953060000061
wherein λ is the total number of the central nodes participating in the competition, F (z)i) To an effort degree ziThe distribution function of (2).
In this embodiment, assuming that each participant is individual in competition and the cost of each participant is also the optimal cost of participating in competition, determining the optimal cost of each participant to win competition according to the probability that each central node participating in competition can win competition as follows:
Figure BDA0001742953060000062
wherein the content of the first and second substances,
Figure BDA0001742953060000063
indicating a competing central node viThe cost of the best effort to compete is won,
Figure BDA0001742953060000064
for participating in the central node v of competitioniThe degree of effort actually expended to obtain the reward, izfor participating in the central node v of competitioniObtaining the lowest effort degree required by the reward, wherein lambda represents the estimated value of the total number of the central nodes participating in the competition, e represents a natural constant, F (z) is a distribution function of the effort degree z, and F-1(z) represents the reciprocal of F (z), F' (z) represents the first derivative of F (z), and the central node viDegree of effort z paid to obtain a rewardiAnd a prize wiThe relation function between (M) is zi＝wi(M)，
Figure BDA0001742953060000065
Is wiThe inverse function of (M).
In this embodiment, the number of the central nodes actually participating in the competition is n, and the unique information source node of the common node is represented as:
Figure BDA0001742953060000071
in an embodiment of the foregoing method for searching for a target by effectively contributing to incentive information propagation, the determining whether a propagation contribution value of a node is greater than or equal to a preset propagation threshold includes:
determining the value of the node to the message propagation;
determining the effective transmission rate of the node;
taking the product of the value brought by the node to the message propagation and the propagation effective rate of the node as the propagation contribution value of the node;
and judging whether the propagation contribution value of the node is greater than or equal to a preset propagation threshold value.
In this embodiment, after determining the information source node for each common node, the message is propagated in the network, and in order to improve the efficiency and accuracy of message propagation, an excitation mechanism is introduced in the propagation process, which may specifically include:
step 2-1), a node value model is established, and the value of the node for message propagation can be measured through the established node value model.
In this embodiment, the evaluation of the value of a node is mainly considered from two aspects, namely, the influence and the attribute similarity of the node.
In this embodiment, v is set for an own network GiFor any node in the network, node viInfluence of (I)iCan be expressed as:
Figure BDA0001742953060000072
wherein k isiIs a node viOut of degree of (k)jRepresents the degree of departure of the jth node in the self network G, and | G | represents the total number of nodes in the network G.
On the other hand, the attribute similarity mainly reflects the similarity degree of the attributes of the current node and the target node, and common attributes comprise a series of easily-obtained attributes related to human information, such as a place of birth, a location, work, gender and the like. Node viAnd a target node vtarSimilarity of attributes
Figure BDA0001742953060000073
Can be expressed as:
Figure BDA0001742953060000074
wherein att (-) represents a set of attributes for a node.
According to the idea of multi-weight distributionNode viValue to message dissemination
Figure BDA0001742953060000075
Comprises the following steps:
Figure BDA0001742953060000076
wherein, ω isIAnd ωsWeights, ω, representing node influence and attribute similarity, respectivelyI,ωsNot less than 0 and omegaI+ωs＝1。
Step 2-2), determining the effective transmission rate of the node.
In the process of the message transmission in the network, not only the transmission probability of the message successfully transmitted to the node is considered, but also whether the transmitted node intentionally continues to transmit is considered, namely the transmission efficiency of the node, the node viEffective rate of propagation ofiCan be expressed as:
qi＝p(θi,ρi|Si＝s)＝p(θi|Si＝s)·p(ρi|Si＝s)
wherein, p (theta)i|SiS) is the probability of successful propagation, p (ρ)i|SiS) probability of propagation willingness, both obeying to a Beta distribution, θi∈[0,1]Representing a node viS ∈ {0,1} represents node viFrom central node to node viWhether the propagation of (2) was successful, SiIs a random variable of value s, pi∈[0,1]Indicating the node's willingness to propagate.
Step 2-3), constructing an effective contribution excitation model.
Node viHas a propagation contribution value of
Figure BDA0001742953060000081
Setting a propagation threshold
Figure BDA0001742953060000082
Thereby to the nodeThe contribution made is screened to be greater than the propagation threshold
Figure BDA0001742953060000083
The contribution value of (c) is considered as an effective contribution, namely: only when node viIs greater than or equal to a propagation threshold value
Figure BDA0001742953060000084
Then it is considered to be a valid propagation, now called node viIn order to contribute to the node efficiently,
Figure BDA0001742953060000085
contribute to effective propagation
Figure BDA0001742953060000086
In this embodiment, information source nodes that can make effective contributing nodes are awarded rewards to promote the propagation of messages to more valuable nodes.
In this example, viCan represent any node in the self network, viOr it may be embodied as an information source node that effectively contributes nodes in a self-network, if viInformation source node for effectively contributing node in self network, viIs given by the reward function M (v)i) Comprises the following steps:
Figure BDA0001742953060000087
wherein KL is divergence and is used for representing a central node viThe difference degree between the initial contribution and the final contribution is the propagation degree, and corresponding rewards are given to the propagation of the initial contribution and the final contribution; k is the reward paid by the unit difference degree;
Figure BDA0001742953060000088
is a central node viInitial contribution when not participating in propagation;
Figure BDA0001742953060000089
Figure BDA00017429530600000810
representing a central node viWhen the propagation is stopped, the central node viAll the neighbor nodes v propagated tojEffective propagation of
Figure BDA00017429530600000811
The sum, t, represents the number of neighbor nodes with significant propagation contributions.
For better understanding of the target finding method for effectively contributing to incentive information propagation according to the embodiment of the present invention, the method is described in detail with reference to fig. 2 and 3:
as shown in fig. 2 and 3, for the same message, in the node competition module, it is first determined whether a node can receive propagation from different central nodes, and if not, the only central node that propagates the message to the certain node is used as the only information source node of the certain node; if so, the node is set as a common node and added to the common node set.
After the common node set is determined, the central nodes in the self networks compete with the nodes in the common node set in sequence until each common node is competed once. Each central node participating in competition pays corresponding effort degree according to expected obtained reward
Figure BDA0001742953060000091
The cost of effort is calculated as follows:
Figure BDA0001742953060000092
for the central node v participating in competitioniIf the winning of the competition is equal to 0.8, the prize M is set
Figure BDA0001742953060000093
λ＝3，ziFor uniform distribution, i.e. distribution function F (z)i)＝ziThe number of competitors scaled to the Poisson distribution minimum effort z i0, the degree of effort actually put into practice is
Figure BDA0001742953060000094
Then node viThe cost of the effort is
Figure BDA0001742953060000095
Sequentially calculating the effort cost of the nodes participating in competition according to an optimal effort cost formula, and selecting the node with the highest effort cost as the information source node vwinI.e. by
Figure BDA0001742953060000096
After the information source node of each node is determined, the effective contribution excitation module calculates the value of the node by using the node value model according to the historical data. If node viOut of degree of kiAnd | G | represents the total number of nodes in the self network G, and the influence of the nodes is calculated by the following formula:
Figure BDA0001742953060000097
calculate node v using the formulaiAnd a target node vtarThe attribute similarity is:
Figure BDA0001742953060000098
as shown in fig. 4, the node influence and the attribute similarity are comprehensively considered, and the weights of the node influence and the attribute similarity are set to ωIAnd ωsAnd 0 is not more than omegaI,ωs≤1，ωI+ωsCalculating node v according to the idea of multiple weight assignment, 1iThe values of the method are as follows:
Figure BDA0001742953060000099
and evaluating the value of each node by using the node value model, determining the value of each node, and determining the contribution of the nodes in the transmission by calculating the transmission effective rate of the nodes.
In the network, the node receives the message and then propagates the message with a certain probability, and the probability can be expressed as the propagation intention of the node. Using the parameter thetai∈[0,1]Representing a node viIs a propagation probability of, parameter thetaiObeying a Bernoulli distribution, while its prior probability distribution obeys a Beta distribution, i.e.
Figure BDA0001742953060000101
Figure BDA0001742953060000102
For the number of times the initial propagation was successful,
Figure BDA0001742953060000103
the number of initial propagation failures. The parameter s ∈ {0,1} represents the node viFrom central node to node viWhether the propagation of (2) was successful, SiIs a random variable with a value s; using the parameter pi∈[0,1]Expressing the propagation intention of the node, still obeying the Bernoulli distribution, the same parameter rhoiObey a Beta distribution, i.e.
Figure BDA0001742953060000104
Figure BDA0001742953060000105
For the number of times initially deemed to be strong at the willingness to propagate,
Figure BDA0001742953060000106
the number of times the initial propagation willingness is weak. After the propagation is carried out for r times,
Figure BDA0001742953060000107
Figure BDA0001742953060000108
representing the number of successful propagation times after the central node propagates r times to the neighbor nodes,
Figure BDA0001742953060000109
indicating the number of propagation failures, and the same can be obtained
Figure BDA00017429530600001010
According to thetaiAnd rhoiConditional independence of computing nodes, propagation efficiency q of computing nodesiThe following were used:
Figure BDA00017429530600001011
the propagation contribution is expressed in terms of the node value and the propagation efficiency of the node, so node viOf the propagation contribution
Figure BDA00017429530600001012
Comprises the following steps:
Figure BDA00017429530600001013
a node also has a difference in the amount of contribution to message propagation due to its own value and the difference in propagation efficiency. In order to efficiently find the target node, determine the propagation direction and save the propagation cost, a node with large propagation contribution needs to be found for propagation. As shown in FIG. 4, in order to distinguish the difference of the contribution amounts and select the nodes with large contribution amounts, a propagation threshold value is introduced into the model
Figure BDA00017429530600001014
Assuming a propagation threshold
Figure BDA00017429530600001015
The specific threshold value is set by the platform according to the actual situation, and only when the node v is connectediIs not less than the threshold, then it is considered to haveEffect propagation, this time called
Figure BDA00017429530600001016
For propagating the contribution amount effectively, i.e. for propagating the effective contribution amount
Figure BDA00017429530600001017
Comprises the following steps:
Figure BDA00017429530600001018
in each self-network, propagation from a central node to its neighbor nodes exists, and the propagated neighbor nodes are regarded as child nodes, which will generate a series of propagation contributions according to propagation threshold values
Figure BDA00017429530600001019
Screening is carried out, quantum nodes with effective propagation contribution can be selected, the sum of the effective propagation contribution of all the child nodes to which the central node propagates is used as an evaluation standard, the KL divergence is used for representing the difference degree of the initial contribution and the final contribution of the central node as the propagation degree, and corresponding rewards are given to the propagation of the central node.
Figure BDA00017429530600001020
Is a node viNode v, the initial contribution when not participating in the propagationiThe sum of the effective propagation contributions of the child nodes is
Figure BDA0001742953060000111
Setting the payment paid by unit difference degree as k which is larger than the cost b paid by unit propagation degree, and aiming at the node viThe propagation rewards of (1):
Figure BDA0001742953060000112
in this embodiment, information source nodes capable of making effective contribution nodes are awarded, and information such as a propagation path and a propagation hop count of a target node is determined based on an award result.
As shown in fig. 5, according to the nature of the reward function, because
Figure BDA0001742953060000113
So when
Figure BDA0001742953060000116
When gradually increasing, node viThe rewards earned also increase progressively, which encourages nodes to strive to find neighbors with significant propagation contributions in order to obtain higher rewards. And also
Figure BDA0001742953060000115
Then for the platform node viWill be a convex function. Therefore, the efficiency and the accuracy of information transmission in the network can be improved through node competition and the establishment of an effective contribution incentive mechanism, and the optimal path of the target node can be found more quickly, so that the transmission cost is reduced.
In summary, according to the target searching method for effectively contributing to incentive information propagation, the only information source node of the common node is determined by the constructed node competition model and the full auction mechanism idea, so that repeated and inefficient propagation of messages is avoided; constructing an effective contribution excitation model, and judging whether the propagation contribution value of the node is greater than or equal to a preset propagation threshold value; if so, the node is an effective contribution node, and awards the information source node of the effective contribution node to promote the information in the network to be transmitted to the more valuable node, so that the effective contribution node is utilized to introduce directionality in the information transmission process, the blind transmission of the information can be avoided, the target node can be found more quickly, and meanwhile, an excitation mechanism is adopted in the transmission process, and the participation degree and the transmission enthusiasm of the node are increased compared with a node volunteer transmission mode, so that the information transmission efficiency and accuracy are improved.
Example two
The present invention further provides a specific embodiment of a target finding apparatus for effectively contributing to incentive information dissemination, and since the target finding apparatus for effectively contributing to incentive information dissemination provided by the present invention corresponds to the specific embodiment of the target finding method for effectively contributing incentive information dissemination, and the target finding apparatus for effectively contributing incentive information dissemination can achieve the object of the present invention by executing the flow steps in the specific embodiment of the method, the explanation in the specific embodiment of the target finding method for effectively contributing incentive information dissemination is also applicable to the specific embodiment of the target finding apparatus for effectively contributing incentive information dissemination provided by the present invention, and will not be described again in detail in the following specific embodiment of the present invention.
As shown in fig. 6, an embodiment of the present invention further provides a target search apparatus for effectively contributing to incentive information propagation, including: an effective contribution incentive module, the effective contribution incentive module comprising: a judgment submodule and an excitation submodule;
the judging submodule 11 is configured to judge whether the propagation contribution value of the node is greater than or equal to a preset propagation threshold value;
and the excitation submodule 12 is configured to, if the propagation contribution value of a node is greater than or equal to a preset propagation threshold value, determine that the node is an effective contribution node, and award an information source node of the effective contribution node so as to promote the message in the network to propagate to a node with a higher value.
The target searching device for effectively contributing to the propagation of the excitation information judges whether the propagation contribution value of the node is greater than or equal to a preset propagation threshold value or not; if so, the node is an effective contribution node, and awards the information source node of the effective contribution node to promote the information in the network to be transmitted to the more valuable node, so that the effective contribution node is utilized to introduce directionality in the information transmission process, the blind transmission of the information can be avoided, the target node can be found more quickly, and meanwhile, an excitation mechanism is adopted in the transmission process, and the participation degree and the transmission enthusiasm of the node are increased compared with a node volunteer transmission mode, so that the information transmission efficiency and accuracy are improved.
In the foregoing specific embodiment of the target-finding device that effectively contributes to incentive information dissemination, the system further comprises: a node competition module;
the node competition module is used for judging whether a node can receive the transmission from different central nodes or not for the same message; if not, taking the only central node which transmits the message to the certain node as the only information source node of the certain node; and if so, setting the certain node as a common node, enabling the central nodes in each self network to compete the common node one by one, and selecting the central node with the largest effort cost in the competition process as the unique information source node of the competing common node.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions.
While the foregoing is directed to the preferred embodiment of the present invention, it will be understood by those skilled in the art that various changes and modifications may be made without departing from the spirit and scope of the invention as defined in the appended claims.

Claims (8)

1. A method for target finding to efficiently contribute to incentivized information dissemination, comprising:
judging whether the propagation contribution value of the node is greater than or equal to a preset propagation threshold value or not;
if so, the node is an effective contribution node, and awards the information source node of the effective contribution node so as to promote the information in the network to be transmitted to the more valuable node;
before determining whether the propagation contribution value of the node is greater than or equal to a preset propagation threshold value, the method further includes:
for the same message, judging whether a node can receive the transmission from different central nodes;
if not, taking the only central node which transmits the message to the certain node as the only information source node of the certain node;
and if so, setting the certain node as a common node, enabling the central nodes in each self network to compete the common node one by one, and selecting the central node with the largest effort cost in the competition process as the unique information source node of the competing common node.
2. The method of claim 1, wherein the competition process employs a full auction mechanism.
3. The method of claim 1, wherein the unique information source node vwin of the common node is expressed as:
Figure FDA0003225669060000011
Figure FDA0003225669060000012
wherein n represents the number of central nodes actually participating in the competition,
Figure FDA0003225669060000013
indicating a competing central node viThe cost of the best effort to compete is won,
Figure FDA0003225669060000014
for participating in the central node v of competitioniThe degree of effort actually expended to obtain the reward, izfor participating in the central node v of competitioniThe lowest effort degree required for obtaining the reward, lambda represents the estimated value of the total number of the central nodes participating in the competition, e represents a natural constant, and F (z) is the score of the effort degree zCloth function, F-1(z) represents the reciprocal of F (z), F' (z) represents the first derivative of F (z), and the central node viDegree of effort z paid to obtain a rewardiAnd a prize wiThe relation function between (M) is zi＝wi(M)，
Figure FDA0003225669060000015
Is wiThe inverse function of (M).
4. The method of claim 1, wherein the determining whether the propagation contribution value of the node is greater than or equal to a preset propagation threshold value comprises:
determining the value of the node to the message propagation;
determining the effective transmission rate of the node;
taking the product of the value brought by the node to the message propagation and the propagation effective rate of the node as the propagation contribution value of the node;
and judging whether the propagation contribution value of the node is greater than or equal to a preset propagation threshold value.
5. The method of claim 4, wherein v is setiBeing any node in the self network G, node viValue to message dissemination
Figure FDA0003225669060000021
Comprises the following steps:
Figure FDA0003225669060000022
Figure FDA0003225669060000023
Figure FDA0003225669060000024
wherein, ω isIAnd ωsWeights, I, representing node influence and attribute similarity, respectivelyiRepresenting a node viThe influence of (a) on the magnetic field,
Figure FDA0003225669060000025
representing a node viAnd a target node vtarSimilarity of attributes, kiIs a node viOut of degree of (k)jRepresents the degree of departure of the jth node in the ego-network G, | G | represents the total number of nodes in the ego-network G, and att (-) represents the set of attributes of the nodes.
6. The method of claim 5, wherein node v is a node that efficiently contributes to incentive information disseminationiEffective rate of propagation ofiComprises the following steps:
qi＝p(θi，ρi|Si＝s)＝p(θi|Si＝s)·p(ρi|Si＝s)
wherein, p (theta)i|SiS) is the probability of successful propagation, p (ρ)i|SiS) probability of propagating will, θiRepresenting a node viPropagation probability of piRepresenting a node viS denotes the node viFrom central node to node viWhether the propagation of (2) was successful, SiIs a random variable with a value s.
7. The method of claim 5, wherein v is setiInformation source node for effectively contributing node in self network, viIs given by the reward function M (v)i) Comprises the following steps:
Figure FDA0003225669060000026
wherein KL is divergence and is used for representing a central node viThe difference degree between the initial contribution and the final contribution is the propagation degree, and corresponding rewards are given to the propagation of the initial contribution and the final contribution; k is the reward paid by the unit difference degree;
Figure FDA0003225669060000031
is a central node viInitial contribution when not participating in propagation;
Figure FDA0003225669060000032
Figure FDA0003225669060000033
representing a central node viWhen the propagation is stopped, the central node viAll the neighbor nodes v propagated tojEffective propagation of
Figure FDA0003225669060000034
The sum, t, represents the number of neighbor nodes with significant propagation contributions.
8. A target-seeking apparatus for efficiently contributing to the propagation of motivational information, comprising: an effective contribution incentive module, the effective contribution incentive module comprising: a judgment submodule and an excitation submodule;
the judging submodule is used for judging whether the propagation contribution value of the node is greater than or equal to a preset propagation threshold value or not;
the incentive submodule is used for determining the node as an effective contribution node if the propagation contribution value of the node is greater than or equal to a preset propagation threshold value, and awarding information source nodes of the effective contribution node so as to promote the message in the network to propagate to more valuable nodes;
wherein the apparatus further comprises: a node competition module;
the node competition module is used for judging whether a node can receive the transmission from different central nodes or not for the same message; if not, taking the only central node which transmits the message to the certain node as the only information source node of the certain node; and if so, setting the certain node as a common node, enabling the central nodes in each self network to compete the common node one by one, and selecting the central node with the largest effort cost in the competition process as the unique information source node of the competing common node.
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