CN106228452A - Social network information based on causal inference propagates history sort method - Google Patents
Social network information based on causal inference propagates history sort method Download PDFInfo
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- CN106228452A CN106228452A CN201610552249.2A CN201610552249A CN106228452A CN 106228452 A CN106228452 A CN 106228452A CN 201610552249 A CN201610552249 A CN 201610552249A CN 106228452 A CN106228452 A CN 106228452A
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- G—PHYSICS
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- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/01—Social networking
Abstract
The invention discloses a kind of social network information based on causal inference and propagate history sort method, described method includes: collects the historical record of Information Communication in social networks, is ranked up the node in Information Communication historical data or limit according to its contribution degree in Information Communication.The social network information based on causal inference that the present invention provides propagates history sort method, creative first propose Information Communication history sequencing problem in social networks based on causal inference, by using responsibility and the necessary part of ability the two index selection cause-effect and abundant part, avoid the algorithm of complexity, make the limit propagated in historical record or node are ranked up according to the contribution in Information Communication, the algorithm of the contribution of each participant in calculating social network information propagation is simpler, improve the intuitivism apprehension that each participant is contributed by user.
Description
Technical field
The present invention relates to computer social networks technical field, a kind of social network information based on causal inference
Propagate history sort method.
Background technology
In the modern life, the daily life affecting everyone that online social networks (SNS) is the most deep, substantial amounts of
Social information occurs in social network sites and propagates, and a usual user can obtain phase by the people of different concerns or friend
Same information.In social networks, information is disseminated process and is recorded its route of transmission, Ren Menke by propagating historical record
To explain user's behavior in social networks with propagation historical record, but, these are propagated historical record and are easy to become
Extremely complex, in large scale, thus limit user's intuitivism apprehension to it.
In order to reduce the overload of this information, for skilled professional and technical personnel, in the urgent need to the proposition of innovation
A kind of measure, limit or node to propagating in historical record are ranked up according to the contribution in Information Communication.
Summary of the invention
One to be solved of the embodiment of the present invention technical problem is that: provides a kind of social networks based on causal inference to believe
Breath propagates history sort method, and to solve in complicated social networks, due to the overload of information, user is not easy to manage intuitively
Solving Information Communication history, easier understands the contribution that social network information is propagated by each participant.
An aspect according to embodiments of the present invention, it is provided that a kind of based on causal inference social network information propagate go through
History sort method, it is characterised in that including:
Collect the historical record of Information Communication, the historical record of described Information Communication and node or limit phase in social networks
Closing, in social networks, the propagation of information refers to the information Information Communication from a resource node a to destination node；
Being ranked up according to it limit in historical record of Information Communication in the contribution degree of Information Communication, described sequence is
The cause-effect difference algorithm improved, propagates the importance on limit in history in order to assess, and then weighs this limit at this Information Communication
In importance.
Further, in described collection social networks, the historical record sign of Information Communication is:
The information event of disseminating in social networks is ε, and information source is S, and destination node istiRepresent a propagation trajectories,
It is by liIndividual continuous print limitComposition；
Φ is a propagation historical record disseminating process, and Τ is the set on limit in Φ.
Further, in the historical record of described Information Communication, certain specific limit X contribution successful for Information Communication
Spend and have following two measurement mode:
Limit X lost efficacy, and the probability of information transmission success is: P (Φx'=true)；
Limit X is effective, and the probability of information transmission success is: P (Φx=true)；
Wherein, x and x ' respectively representative edge X in random experiments " effectively " and the situation of " inefficacy ", P (Φx'=true)
With P (Φx=true) it is two cause and effect variablees, represent limit X necessary part during Information Communication and abundant part respectively.
Further, described cause-effect difference algorithm is by P (Φx'=true) and P (Φx=true) two causes and effects become
Amount combines, and considers causal abundant part and necessary part the most simultaneously, and its algorithmic formula is:
DCE (X)=P (Φx=true)-P (Φx'=true).
Further, described cause-effect difference algorithm includes the necessary part during Information Communication and abundant part,
Necessary part during described Information Communication is referred to as responsibility, the fully part referred to as energy during described Information Communication
Power；
Cause effect relation in Information Communication is:
The set making T be the limit in Information Communication history, t is a participation limit in Information Communication history, and t ∈ T, Γ are
The set on a part of limit in Information Communication history,If met:
After removing Γ from T, from information sourceTo destination nodeInformation Communication the most successful；
After removing Γ from T, remove t further and can cause the inefficacy of Information Communication；
Then t is called the reason that information is disseminated, and Γ is the forecast failure collection of t；
T responsibility in Information Communication is:
Γ contains the forecast failure collection of all t；
T ability in Information Communication is:
Wherein τ represents all propagation paths through t, the limit that return value is τ of function st (τ)
Collection；
Described utilization cause-effect difference algorithm to the limit in the historical record of Information Communication according to it in Information Communication
The strategy that is ranked up of contribution degree, the referred to as ordering strategy of " responsibility-ability ".
Further, the algorithmic formula of described " responsibility-ability " ordering strategy is:
Score=α * fn (responsibility)+(1-α) fn (capability)
Wherein fn represents the Regularization function calculating standard scores, and responsibility represents responsibility,
Capability represents ability, and 0 < α < 1 is a balance factor.
Further, the algorithm of described responsibility realizes including:
Historical record Φ, the set T on limit in Φ, and a limit t ∈ T are propagated in input；
Obtain covering set SA=sc (t, Φ), and do not cover set ST=Φ-SA, described sc (tj, Φ) and={ ci|tj
∈ci∧ci∈Φ}；Initial contingency set
Select limit x ∈ T-Γ；
X is added in Γ, from SA, remove sc (x, SA), from ST, remove sc (x, ST)；
Repeat above-mentioned two step, until ST is empty；
OutputResponsibility value as t.
Further, described selection limit x ∈ T-Γ meets following two condition:
Set in sc (x, ST) covering as much as possible ST；
SA≠sc(x,SA)。
The social network information based on causal inference provided based on the above embodiment of the present invention propagates history sort method,
Creative first propose Information Communication history sequencing problem in social networks based on causal inference, by use responsibility and
The necessary part of ability the two index selection cause-effect and abundant part, it is to avoid complicated algorithm, make propagating history
Limit or node in record are ranked up according to the contribution in Information Communication, the tribute of each participant in calculating social networks
The algorithm offered is simpler, improves the intuitivism apprehension that each participant is contributed by user.
The embodiment of the present invention can be applicable to social networks, education network, Military Network and gaming network, is particularly suited for society
Hand over network.
Below by drawings and Examples, technical scheme is described in further detail.
Accompanying drawing explanation
The accompanying drawing of the part constituting description describes embodiments of the invention, and is used for explaining together with describing
The principle of the present invention.
Referring to the drawings, according to detailed description below, the present invention can be more clearly understood from, wherein:
Fig. 1 is the flow process that present invention social network information based on causal inference propagates one embodiment of history sort method
Figure.
Fig. 2 is the stream that present invention social network information based on causal inference propagates history another embodiment of sort method
Cheng Tu.
Detailed description of the invention
The various exemplary embodiments of the present invention are described in detail now with reference to accompanying drawing.It should also be noted that unless additionally have
Body illustrates, the parts illustrated the most in these embodiments and positioned opposite, the numerical expression of step and numerical value are not intended to this
The scope of invention.
Simultaneously, it should be appreciated that for the ease of describing, the size of the various piece shown in accompanying drawing is not according to reality
Proportionate relationship draw.
Description only actually at least one exemplary embodiment is illustrative below, never as to the present invention
And any restriction applied or use.
May be not discussed in detail for technology, method and apparatus known to person of ordinary skill in the relevant, but suitable
In the case of when, described technology, method and apparatus should be considered a part for description.
It should also be noted that similar label and letter represent similar terms, therefore, the most a certain Xiang Yi in following accompanying drawing
Individual accompanying drawing is defined, then need not it is further discussed in accompanying drawing subsequently.
Fig. 1 is the flow process that present invention social network information based on causal inference propagates one embodiment of history sort method
Figure.As it is shown in figure 1, the method for this embodiment includes:
10, collect the historical record of Information Communication in social networks, the historical record of described Information Communication and node or limit
Relevant, in social networks, the propagation of event refers to the event Information Communication from a resource node a to destination node；
20, the node in the historical record of Information Communication or limit are arranged according to its contribution degree in Information Communication
Sequence, the sequence of described propagation history is the cause-effect difference algorithm improved, and propagates the importance on limit in history in order to assess.
The historical record sign collecting Information Communication in social networks described in step 10 is:
Wherein, the information event of disseminating in social networks is ε, and information source is S, and destination node istiRepresent a propagation
Track, it is by liIndividual continuous print limitComposition；
Φ is a propagation historical record disseminating process, and Τ is the set on limit in Φ；
In the case of there is no ambiguity, can remove subscript (ε,)。
The target propagating historical record data sequence is to carry out to limit all of in T according to the contribution during disseminating
Sequence.
In above-mentioned propagation historical record, for the importance of a certain edges thereof, those skilled in the art is mainly closed
Two problems of the heart: if losing efficacy in this limit, which type of result this can cause to Information Communication？If not losing efficacy in this limit, this is again
Which type of result can be caused to Information Communication？Accordingly, it is considered to the probability that information is delivered successfully, certain specific limit X for
The successful contribution degree of Information Communication have following two weigh mode:
Limit X lost efficacy, and the probability of information transmission success is: P (Φx'=true)；
Limit X is effective, and the probability of information transmission success is: P (Φx=true)；
Wherein, x and x ' respectively representative edge X in random experiments " effectively " and the situation of " inefficacy ", P (Φx'=true) and P
(Φx=true) it is two cause and effect variablees, represent limit X necessary part during Information Communication and abundant part respectively.
Cause-effect difference algorithm described in step 20 is by fully part and two cause and effect variable P (Φ of necessary partx'
=true) and P (Φx=true) combine, consider causal abundant part and necessary part, its algorithmic formula the most simultaneously
For:
DCE (X)=P (Φx=true)-P (Φx'=true) ... ... ... ... ... ... (2)
Carrying out causal calculating, the most correct method is exactly randomized test, and in the present embodiment, we briefly retouch
State its algorithm:
Obtaining inputting limit X, we initialized X for " effectively " (or " inefficacy ")；
In simulation each time, the limit setting at random other is " inefficacy " or " inefficacy ", and detects information biography further
Broadcast the most successful；
By substantial amounts of simulation, obtain probability P (Φx=true) (or P (Φx'=true))；
DCE value is obtained by formula (2).
Although the method for above-mentioned employing randomized experiment can calculate DCE value, but the method needs to run
A stable convergency value can be obtained.
Described cause-effect difference algorithm includes the necessary part during Information Communication and abundant part, and described information passes
Necessary part during broadcasting is referred to as responsibility, the fully part referred to as ability during described Information Communication, multiple for avoiding
Miscellaneous calculating, according to the limit in the cause-effect difference algorithm historical record to Information Communication, according to it in Information Communication
Contribution degree is ranked up, and proposes a kind of " responsibility-ability " strategy, by using two indices simultaneously, obtains cause effect relation effect
Two aspects answered, specific as follows:
The computational methods of described responsibility and ability are:
The set making T be the limit in Information Communication historical record, t is a participation limit in Information Communication history, t ∈ T,
Γ is the set on a part of limit in Information Communication historical record,If met:
After removing Γ from T, from information sourceTo destination nodeInformation Communication the most successful；
After removing Γ from T, remove t further and can cause the inefficacy of Information Communication；
Then t is called the reason that information is disseminated, and Γ is the forecast failure collection of t；
T responsibility in Information Communication is:
Γ contains the forecast failure collection of all t；
Such as, it is Φ={ t at the propagation historical record once disseminating process1,t2,t3, wherein t1={ A, B}, t2=A,
C}, t3={ D}, then the responsibility of limit A is 1/2, because the minimum forecast failure collection of A is { D}.Being similar to, the responsibility of limit D is 1/2,
Because the minimum forecast failure collection of D is { A}.The minimum forecast failure collection of B and C be respectively C, D} and B, D}, and therefore they
Responsibility is all 1/3.
T ability in Information Communication is:
Wherein τ represents all propagation paths through t, the limit that return value is τ of function st (τ)
Collection；
As above described in example, the ability of limit A is 1/2, because it needs, { B} is with { C} just can ensure that and propagates successfully, limit B's and C
Ability is all 1/2, because they are required for, { to transmit information, the ability of limit D is 1 to A}, because D self can ensure that information
Propagate.
By the algorithmic formula of the ordering strategy of above-mentioned analysis available " responsibility-ability " it is:
Score=α * fn (responsibility)+(1-α) fn (capability) ... ... ... ... (3)
Wherein fn represents the Regularization function calculating standard scores, and 0 < α < 1 is a balance factor.
Due to the problem of the NP-hard that the calculating of responsibility value is, therefore, it is proposed that a kind of approximate data:
Fig. 2 is the stream that present invention social network information based on causal inference propagates history another embodiment of sort method
Cheng Tu, as shown in Figure 2:
101, historical record Φ, the set T on limit, and a limit t ∈ T are propagated in input；
102, obtain covering set SA=sc (t, Φ), and do not cover set ST=Φ-SA, described sc (tj, Φ)=
{ci|tj∈ci∧ci∈ Φ }, initial contingency set
103, select limit x ∈ T-Γ；
104, x is added in Γ, from SA, remove sc (x, SA), from ST, remove sc (x, ST)；
105, repeat above-mentioned two step 103,104, until ST be sky；
106, outputResponsibility value as t.
In above-mentioned steps 103, described selection limit x ∈ T-Γ meets following two condition:
(1) set in sc (x, ST) covering as much as possible ST；
(2)SA≠sc(x,SA)。
In above-mentioned condition, first condition is that the limit in forecast failure collection to be added carries out the rule of heuristic sequence
Then.Second condition is a restrictive condition, it is ensured that t, after removing forecast failure collection, remains the reason in Information Communication
One of.
In this specification, each embodiment all uses the mode gone forward one by one to describe, and what each embodiment stressed is and it
The difference of its embodiment, same or analogous part cross-reference between each embodiment.For system embodiment
For, owing to it is the most corresponding with embodiment of the method, so describe is fairly simple, relevant part sees the portion of embodiment of the method
Defend oneself bright.
Methods and apparatus of the present invention may be achieved in many ways.Such as, can pass through software, hardware, firmware or
Software, hardware, any combination of firmware realize methods and apparatus of the present invention.Said sequence for the step of described method
Merely to illustrate, the step of the method for the present invention is not limited to order described in detail above, special unless otherwise
Do not mentionlet alone bright.Additionally, in certain embodiments, the present invention also can be embodied as the program recorded in the recording medium, these programs
Including the machine readable instructions for realizing the method according to the invention.Thus, the present invention also covers storage for performing basis
The record medium of the program of the method for the present invention.
Description of the invention is given for example with for the sake of describing, and is not exhaustively or by the present invention
It is limited to disclosed form.Many modifications and variations are obvious for the ordinary skill in the art.Select and retouch
Stating embodiment is in order to the principle of the present invention and actual application are more preferably described, and enables those of ordinary skill in the art to manage
Solve the present invention thus design the various embodiments with various amendments being suitable to special-purpose.
Claims (8)
1. a social network information based on causal inference propagates history sort method, it is characterised in that including:
Collecting the historical record of Information Communication in social networks, the historical record of described Information Communication is relevant to node or limit, society
The propagation of information in network is handed over to refer to the information Information Communication from a resource node a to destination node；
Being ranked up according to it limit in historical record of Information Communication in the contribution degree of Information Communication, described sequence is to improve
Cause-effect difference algorithm, propagate the importance on limit in history in order to assess, and then weigh this limit in this Information Communication
Importance.
Method the most according to claim 1, it is characterised in that the historical record of Information Communication in described collection social networks
It is characterized as:
The information event of disseminating in social networks is ε, and information source is S, and destination node istiRepresent a propagation trajectories, it by
liIndividual continuous print limitComposition；
Φ is a propagation historical record disseminating process, and Τ is the set on limit in Φ.
Method the most according to claim 2, it is characterised in that in the historical record of described Information Communication, certain specific
Limit X contribution degree successful for Information Communication have following two weigh mode:
Limit X lost efficacy, and the probability of information transmission success is: P (Φx'=true)；
Limit X is effective, and the probability of information transmission success is: P (Φx=true)；
Wherein, x and x ' respectively representative edge X in random experiments " effectively " and the situation of " inefficacy ", P (Φx'=true) and P
(Φx=true) it is two cause and effect variablees, represent limit X necessary part during Information Communication and abundant part respectively.
4. according to the method described in any one of claims 1 to 3, it is characterised in that described cause-effect difference algorithm is by P
(Φx'=true) and P (Φx=true) two cause and effect variablees combine, and consider causal abundant part and necessity the most simultaneously
Part, its algorithmic formula is:
DCE (X)=P (Φx=true)-P (Φx'=true).
5. according to the method described in any one of claims 1 to 3, it is characterised in that described cause-effect difference algorithm includes letter
Necessary part in breath communication process and abundant part, the necessary part during described Information Communication is referred to as responsibility, described
Fully part referred to as ability during Information Communication；
Cause effect relation in Information Communication is:
The set making T be the limit in Information Communication history, t is a participation limit in Information Communication history, and t ∈ T, Γ are information
Propagate the set on a part of limit in history,If met:
After removing Γ from T, from information sourceTo destination nodeInformation Communication the most successful；
After removing Γ from T, remove t further and can cause the inefficacy of Information Communication；
Then t is called the reason that information is disseminated, and Γ is the forecast failure collection of t；
T responsibility in Information Communication is:
Γ contains the forecast failure collection of all t；
T ability in Information Communication is:
Wherein τ represents all propagation paths through t, the limit collection that return value is τ of function st (τ)；
Described utilization cause-effect difference algorithm to the limit in the historical record of Information Communication according to its tribute in Information Communication
The strategy that degree of offering is ranked up, the referred to as ordering strategy of " responsibility-ability ".
Method the most according to claim 5, it is characterised in that the algorithmic formula of the ordering strategy of described " responsibility-ability "
For:
Score=α * fn (responsibility)+(1-α) fn (capability)
Wherein fn represents the Regularization function calculating standard scores, and responsibility represents responsibility, capability generation
Table ability, 0 < α < 1 is a balance factor.
Method the most according to claim 5, it is characterised in that the algorithm of described responsibility realizes including:
Historical record Φ, the set T on limit in Φ, and a limit t ∈ T are propagated in input；
Obtain covering set SA=sc (t, Φ), and do not cover set ST=Φ-SA, described sc (tj, Φ) and={ ci|tj∈ci
∧ci∈Φ}；Initial contingency set
Select limit x ∈ T-Γ；
X is added in Γ, from SA, remove sc (x, SA), from ST, remove sc (x, ST)；
Repeat above-mentioned two step, until ST is empty；
OutputResponsibility value as t.
Method the most according to claim 7, it is characterised in that described selection limit x ∈ T-Γ meets following two condition:
Set in sc (x, ST) covering as much as possible ST；
SA≠sc(x,SA)。
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Cited By (1)
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| CN110362754A (en) * | 2019-06-11 | 2019-10-22 | 浙江大学 | The method that social network information source is detected on line based on intensified learning |
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