CN110113765A - A kind of information source location algorithm based on cellular Automation Model - Google Patents
A kind of information source location algorithm based on cellular Automation Model Download PDFInfo
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Abstract
The invention discloses a kind of information source location algorithm based on cellular Automation Model.The present invention constructs the complex network nodal analysis method (Random-Susceptible-Infected-Recovered based on Cellular Automaton Theory first, R-SIR), then the probability occurred using the model calculate node to the forward-path number of other forward node and the forward-path, a kind of information source lookup method based on node accessibility measurement is proposed, so that the information in location information communication network issues source.Theoretical analysis shows that on the one hand on the other hand the algorithm can overcome the problem of step number estimation inaccuracy in easily access property measurement to avoid maximal possibility estimation bring time complexity problem.The results show, the algorithm are all higher to the information source identification rate and recognition correct rate of multinode network, achieve the purpose that information source is searched, are of great significance to the secret protection of personal big data.
Description
Technical field
The present invention relates to information source field of locating technology in social networks, more particularly, to the information of cellular Automation Model
Source location algorithm.
Background technique
The link prediction research of network refers to by studying known network structure information, to predict in network
There has been no a possibility that linking even is generated between two nodes of when company of determining whether (or can not).Another is used
Very wide link prediction method is the method based on maximal possibility estimation, and Clauset, Moore and Newman have found after study,
The connection of nodes be able to reflect in network hierarchical structure, maximal possibility estimation can be very good processing hierarchical structure
Apparent network, but this method calculating process is complicated, and time complexity is high, leads to not handle catenet.
It is also fewer to Network Information Sources lookup using cellular Automation Model in current research work, it is substantially benefit
Rumour or viral transmission are carried out with the model.It is dynamic to complex network gossip propagation that Song Yurong and Jiang Guoping are based on cellular automata
Mechanical behavior has carried out a series of researchs, but its modeling artificial network scale is smaller, is unable to fully reflect true multiple
Transmission dynamics behavior in miscellaneous network.Wang Yaqi and Jiang Guoping have studied propagation delay to complex network viral transmission process
It influences.Li Zhao etc. proposes a kind of new propagation model based on cellular automata, has studied the popularity degree and cellular of virus
Relationship between neighbours' size.In addition, effective tool of the cellular automata as research complication system, can not only predict virus
Propagation trend, additionally it is possible to performance propagate in probability event, therefore be increasingly valued by people.
It is used for reference herein by other methods, by Cellular Automaton Theory, establishes the number for meeting current complex network feature
It according to propagation model, discusses respectively to four kinds of common complex network models, finds the state and conversion of Node Contraction in Complex Networks
Relationship is calculated positioning diversity entropy by the reachable path of node for data forwarding packet, is positioned using node accessibility metric algorithm
Data source achievees the purpose that complex network information source is searched, and ensures that data dissemination is safe and reliable.
Summary of the invention
The purpose of the present invention is to provide the information source location algorithms based on cellular Automation Model.
It is as follows that the present invention solves the step of technical solution used by its technical problem:
1) proposition of the complex network data dissemination model based on cellular automata
According to the four of cellular automata model elements, it is as follows to establish R-SIR model:
Cellular space C: establishing the one-dimensional cellular space comprising N number of cellular, and a cellular in space indicates complicated letter
Cease a node in grid.
Finite state collection Q:R-SIR model considers forwarding (propagation) state of node, receives forwarding (not propagating) state
With rejection state.It enables Q={ (0,0), (1,0), (0,1), (1,1) }, (0,0) indicates disarmed state；(1,0) forwarding is indicated
State；(0,1) reception state is indicated.Vector Si (t) (Si (t) ∈ Q) expression of the state variable of node i moment t, vector Si
It (t) include two components: Si (t)=(Six (t), Siy (t)), wherein component Six (t) is used to indicate whether node forwards, point
Amount Siy (t) is used to indicate whether node receives, then has:
Wherein, the expression node i of state 1 is refusal in the state of moment t, and state 2 indicates that state of the node i in moment t is
Forwarding, the expression node i of state 3 are to receive in the state of moment t, and state 4 is not present.
In cellular neighborhood V:R-SIR model with network moment t adjacency matrix A (t) Lai Dingyi moment t cellular it
Between neighborhood.The vector of the i-th row in adjacency matrix A (t) indicates node i in the neighbours Vi (t) of moment t, i.e. Vi(t)
={ aij(t)|aij(t) ∈ A (t), j=1,2 ..., N }.If aij(t)=1, existing between expression node i and j in moment t can
Propagate the connection of security risk.
State transition rules f: in R-SIR model, state transition rules are equally divided into two parts.First part is
At initial stage (i.e. moment t=0), the selection node random with Probability p in a network, the node being then selected from each
One neighbor node of middle random selection is forwarded.The state transition rules of first part are as follows:
Judge whether to be selected with Probability p firstly for each node i, be indicated with hi: hi=1 indicates node i quilt
It chooses, hi=0 indicates that node i is not selected.The rule for defining hi is as follows:
Wherein random number of the r between (0,1) judges whether node i is selected for being compared with Probability p.
If hi=1, indicate that node i is selected, the random selection one in the neighbours of node i is next needed to turn
Hair.By the definition of cellular neighborhood V in model it is found that vector Vi(0)={ aij(0)|aij(0) (0) ∈ A, j=1,2 ..., N } table
Show that node i is carved and the adjacency state of other nodes at the beginning.Set vi (0) indicates all neighbours' of initial time node i
Set, vi(0)=j | aij(0)=1, aij(0)∈A(0)}.If vi(0)=θ, then skip node i, continue to other nodes into
The judgement whether row is chosen.If vi(0) ≠ θ then randomly selects an element jr from vi (0) and is forwarded, makes sjr(0)=
(0,1)。
If hi=0, indicates that node i is not selected, then skip node i, continue other nodes are made whether to choose
Judgement.Need to modify the initial adjacency matrix A (0) of network G simultaneously, if si (0)=(0,1), then ai j (0)=aji (0)=0,
(j=1,2, N).
Evolutionary phase (moment t > 0) of the second part of state transition rules for propagation, in the evolutionary phase, Mei Geshi
Between be spaced in forward node forward data to its neighbours with probability α, while in each unit time forward node also with probability β
Receive upstream node data；If forward node switchs to receiving node in subsequent time, which is becoming the same of receiving node
When with probability δ become refusal upstream node data.The state dress of second part changes regular as follows:
When the state of node i moment t is reception state, i.e. when si (t)=(0,1), si (t+1)=(0,1) indicates node
Once becoming receiving node, it is constant to be maintained for reception state.(3) upper horizontal line indicates inversion operation in formula；Gx, gy are state conversion
Discriminant function is defined as follows:
(5) in view of two kinds of situations that node i is respectively disarmed state and forwarding state in moment t in formula.When node i exists
When moment t is disarmed state, si (t)=0, then the first item in (5) formula works, i.e.,
G=1- (1- α)mi(t)-r (7)
(7) formula is used to judge to be in the node i of disarmed state in moment t, and whether state changes after a discrete time
Become.A refusal node is changed into other shape probability of states and increases with the increase of receiving node quantity adjacent thereto in model
Add.Becoming received probability in subsequent time in the node i that moment t is in disarmed state is 1- (1- α)mi(t), wherein mi (t) table
Show the quantity in receiving node moment t adjacent with node i:
And if si (t)=(0,1), then there are aij (t)=aji (t)=0, j=1,2 ..., N.
(6) formula is used to judge whether be converted in moment t as forwarding state and in the node i that moment t+1 reverts to refusal
Reception state, wherein random number of the r between (0,1), is compared for refusing δ with reception.(6) in formula when six (t) be 1,
When six (t+1) is 0 and (δ-r) > 0, gy > 0, expression node i is forwarding state in moment t, reverts to and refuses in moment t+1
Absolutely；Other situations gy, which is respectively less than, is equal to 0, indicates that node i is not restored to refuse.
It is indicated to refuse node ratio shared in all nodes in moment t network with S (t), I (t) is indicated in moment t
Forward node ratio shared in all nodes in network, R (t) indicate that receiving node is in all nodes in moment t network
In shared ratio, then there is following result in model:
+ R (t)=1 S (t)+I (t). (11)
2) proposition of the information source location algorithm based on R-SIR model node accessibility measurement
In order to avoid maximal possibility estimation bring time complexity problem, step number in easily access property measurement is overcome to estimate not
Accurate problem proposes the information source location algorithm measured based on node accessibility, and steps are as follows:
R-SIR model is established, until network enters stable state, determines forward node collection；
Selected forward node i is current accessed node；
Find out all reachable paths between the node that current accessed node is forwarded to every other N-1；
Find out the general of all reachable paths appearance between the node that current accessed node is forwarded to every other N-1
Rate；
The accessibility of current accessed node is calculated according to formula (12)；
A node is randomly selected as current accessed node from the remaining forward node concentration having not visited, and jumps to step
Rapid 3；
If all forward node were visited, exit；
Finally, the maximum node of the accessibility of node is the information source for needing to find；
Detailed description of the invention
Fig. 1 describes scales-free network Node distribution schematic diagram
Fig. 2 describes reachable path number and path probability of occurrence relationship
Fig. 3, which describes path number, influences node accessibility
Fig. 4 describes path number and information source identification rate relationship
Fig. 5 describes number of network node and information source identification accuracy relationship
Fig. 6 describes accessibility metric algorithm and the comparison of distance center algorithm
Specific embodiment
Below with reference to example, the present invention is further illustrated:
1) experimental situation describes
By the model use into four network topologies currently studied extensively: ER (Erdos-Renyi) network (node
Counting is 400) Geographic network (number of nodes 200), BA (Barabasi-Albert) network (number of nodes 250),
The network LFR benchmark (number of nodes 1000).
2) experimental data describes
Influence for observation path number to node accessibility is utilized respectively different paths when calculating accessibility
Number observes the variation tendency of node accessibility.In an experiment, if path number is respectively 1,2,3,4,5,6；The ginseng of network
Number is as follows respectively: the average degree of ER network is that the average degree of 6, Geographic network is, the average degree of 4, BA networks is 6, LFR
The average degree of network is 10；In addition, the node size of four networks is 3000.
It is provided that ER network node number is 800 for observed information identifing source rate network parameter, information source
Number is 10；Geographic network node number is 1200, and information source number is 12；BA network node number is 3300, information
Source number is 8；LFR benchmark network node number is 4000, and information source number is 12.
3) experimental diagrams describe
Fig. 1 describes scales-free network Node distribution schematic diagram；
Fig. 2 describes reachable path number and path probability of occurrence relationship；
Fig. 3, which describes path number, influences node accessibility；
Fig. 4 describes path number and information source identification rate relationship；
Fig. 5 describes number of network node and information source identification accuracy relationship；
Fig. 6 describes accessibility metric algorithm and the comparison of distance center algorithm；
4) analysis of experimental results
It is most of with the relationship between the path probability of occurrence that Fig. 2 illustrates reachable path number between two nodes
All only possess 1 or 2 paths numbers between node, and can occur with very big probability；Between only a small number of nodes
Possess 6 paths numbers, and the probability that the path occurs is very low.；
From figure 3, it can be seen that if when only carrying out the accessibility of calculate node with a paths number, though diversity entropy
It is right very big, but result is often inaccuracy, and increasing with the path number for participating in the calculating of diversity entropy, diversity
Entropy gradually tends to be steady, and it is just relatively reliable to obtain node accessibility result；
From fig. 4, it can be seen that the path number for participating in accessibility calculating is more, the discovery rate of information source is higher.This
It is because more path number is considered, the information propagation path of node is just more clear, then more have can for information source node
It can be detected；
From fig. 5, it can be seen that the accuracy rate of information source identification of the inventive algorithm in BA network is not high always, this
It is as caused by the topological property of network；In the artificial synthesized network of LRF, the accuracy rate curve of information source identification is steady, says
It is bright in LRF network with the increase of number of nodes, big change will not occur for the topological structure property of network；And in ER network and
In Geographic network, falling before all occurs in discrimination curve rises last steady phenomenon again, illustrates with number of nodes
Increase, the information that information source identification can refer to also increases, more and more accurate so as to cause recognition result；
Although from fig. 6, it can be seen that two methods to the recognition correct rate of information source 80% or more, based on can
Higher based on distance center method average recognition rate up to path metric ratio, effect is also more preferable.
Claims (1)
1. a kind of information source location algorithm based on cellular Automation Model, it is characterised in that the technical step of the model is as follows:
1) proposition of the complex network data dissemination model based on cellular automata
According to the four of cellular automata model elements, it is as follows to establish R-SIR model:
Cellular space C: establishing the one-dimensional cellular space comprising N number of cellular, and a cellular in space indicates complex information system
A node in system network；
Finite state collection Q:R-SIR model considers forwarding (propagation) state of node, receives forwarding (not propagating) state and refuse
Exhausted reception state；It enables Q={ (0,0), (1,0), (0,1), (1,1) }, (0,0) indicates disarmed state；(1,0) forwarding shape is indicated
State；(0,1) reception state is indicated；Vector Si (t) (Si (t) ∈ Q) expression of the state variable of node i moment t, vector Si (t)
Include two components: Si (t)=(Six (t), Siy (t)), wherein component Six (t) is used to indicate whether node forwards, component
Siy (t) is used to indicate whether node receives, then has:
Wherein, the expression node i of state 1 is refusal in the state of moment t, and the expression node i of state 2 is to forward in the state of moment t,
The expression node i of state 3 is to receive in the state of moment t, and state 4 is not present；
In cellular neighborhood V:R-SIR model with network moment t adjacency matrix A (t) Lai Dingyi between moment t cellular
Neighborhood；The vector of the i-th row in adjacency matrix A (t) indicates node i in the neighbours Vi (t) of moment t, i.e. Vi(t)={ aij
(t)|aij(t) ∈ A (t), j=1,2 ..., N }；If aij(t)=1 it, indicates that peace can be propagated in moment t presence between node i and j
The connection of full blast danger；
State transition rules f: in R-SIR model, state transition rules are equally divided into two parts；First part is first
Stage beginning (i.e. moment t=0), the selection node random with Probability p in a network, then from the node that each is selected with
Machine selects a neighbor node to be forwarded；The state transition rules of first part are as follows:
Judge whether to be selected with Probability p firstly for each node i, be indicated with hi: hi=1 indicates that node i is selected,
Hi=0 indicates that node i is not selected；The rule for defining hi is as follows:
Wherein random number of the r between (0,1) judges whether node i is selected for being compared with Probability p；
If hi=1, indicate that node i is selected, the random selection one in the neighbours of node i is next needed to be forwarded；By
The definition of cellular neighborhood V is it is found that vector V in modeli(0)={ aij(0)|aij(0) (0) ∈ A, j=1,2 ..., N } indicate node
I is carved at the beginning and the adjacency state of other nodes；Set vi (0) indicates the set of all neighbours of initial time node i, vi
(0)=j | aij(0)=1, aij(0)∈A(0)}；If vi(0)=θ, then skip node i, continues to be made whether to select to other nodes
In judgement；If vi(0)=θ then randomly selects an element jr from vi (0) and is forwarded, makes sjr(0)=(0,1)；
If hi=0, indicates that node i is not selected, then skip node i, continue the judgement for being made whether to choose to other nodes；
Need to modify the initial adjacency matrix A (0) of network G simultaneously, if si (0)=(0,1), then ai j (0)=aji (0)=0, (j=
1,2,···,N)；
Evolutionary phase (moment t > 0) of the second part of state transition rules for propagation, in the evolutionary phase, between each time
Forward data to its neighbours with probability α every interior forward node, while forward node is also received with probability β in each unit time
Upstream node data；If forward node switchs to receiving node in subsequent time, the node become receiving node while with
Probability δ becomes refusal upstream node data；The state dress of second part changes regular as follows:
When the state of node i moment t is reception state, i.e. when si (t)=(0,1), si (t+1)=(0,1) indicates node once
As receiving node, it is constant to be maintained for reception state；(3) upper horizontal line indicates inversion operation in formula；Gx, gy are state conversion judgement
Function is defined as follows:
(5) in view of two kinds of situations that node i is respectively disarmed state and forwarding state in moment t in formula；When node i is at the moment
When t is disarmed state, si (t)=0, then the first item in (5) formula works, i.e.,
G=1- (1- α)mi(t)-r (7)
(7) formula is used to judge to be in the node i of disarmed state in moment t, and whether state changes after a discrete time；Mould
A refusal node is changed into other shape probability of states and increases with the increase of receiving node quantity adjacent thereto in type；?
It is 1- (1- α) that the node i that moment t is in disarmed state, which becomes received probability in subsequent time,mi(t), wherein mi (t) is indicated
The quantity of receiving node moment t adjacent with node i:
And if si (t)=(0,1), then there are ai j (t)=aji (t)=0, j=1,2 ..., N；
(6) formula is used to judge whether be converted to reception in moment t as forwarding state and in the node i that moment t+1 reverts to refusal
State, wherein random number of the r between (0,1), is compared for refusing δ with reception；(6) when six (t) is 1, six in formula
(t+1) be 0 and (δ-r) > 0 when, gy > 0, indicate node i moment t be forwarding state, revert to refusal in moment t+1；Its
His situation gy, which is respectively less than, is equal to 0, indicates that node i is not restored to refuse；
It is indicated to refuse node ratio shared in all nodes in moment t network with S (t), I (t) is indicated in moment t network
Middle forward node ratio shared in all nodes, R (t) indicate receiving node institute in all nodes in moment t network
The ratio accounted for, then there is following result in model:
+ R (t)=1 S (t)+I (t)； (11)
2) proposition of the information source location algorithm based on R-SIR model node accessibility measurement
In order to avoid maximal possibility estimation bring time complexity problem, step number estimation inaccuracy in easily access property measurement is overcome
The problem of, propose the information source location algorithm measured based on node accessibility, steps are as follows:
R-SIR model is established, until network enters stable state, determines forward node collection；
Selected forward node i is current accessed node；
Find out all reachable paths between the node that current accessed node is forwarded to every other N-1；
Find out the probability that all reachable paths between the node that current accessed node is forwarded to every other N-1 occur；
The accessibility of current accessed node is calculated according to formula (12)；
A node is randomly selected as current accessed node from the remaining forward node concentration having not visited, and jumps to step 3；
If all forward node were visited, exit；
Finally, the maximum node of the accessibility of node is the information source for needing to find.
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| CN117314879A (en) * | 2023-10-19 | 2023-12-29 | 甘肃路桥飞宇交通设施有限责任公司 | Self-adaptive operation and maintenance judging method and monitoring device for road indication board |
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| CN117314879A (en) * | 2023-10-19 | 2023-12-29 | 甘肃路桥飞宇交通设施有限责任公司 | Self-adaptive operation and maintenance judging method and monitoring device for road indication board |
| CN117314879B (en) * | 2023-10-19 | 2024-05-28 | 甘肃路桥飞宇交通设施有限责任公司 | Self-adaptive operation and maintenance judging method and monitoring device for road indication board |
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