CN106228452B - Social network information propagation history ordering method based on causal inference - Google Patents

Social network information propagation history ordering method based on causal inference Download PDF

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CN106228452B
CN106228452B CN201610552249.2A CN201610552249A CN106228452B CN 106228452 B CN106228452 B CN 106228452B CN 201610552249 A CN201610552249 A CN 201610552249A CN 106228452 B CN106228452 B CN 106228452B
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王朝坤
叶晓俊
王学成
王铮
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Abstract

The invention discloses a social network information propagation history sequencing method based on causal inference, which comprises the following steps: and collecting the history records of information propagation in the social network, and sequencing the nodes or edges in the information propagation history data according to the contribution degrees of the nodes or edges in the information propagation history data. The causal inference-based social network information propagation history ordering method provided by the invention creatively provides the causal inference-based information propagation history ordering problem in the social network for the first time, and obtains the necessary part and the sufficient part of the causal effect by using two indexes, namely responsibility and capacity, so that a complex algorithm is avoided, edges or nodes in the propagation history are ordered according to the contribution to information propagation, the algorithm for calculating the contribution of each participant in the social network information propagation is simpler, and the intuitive understanding of the contribution of a user to each participant is improved.

Description

Social network information propagation history ordering method based on causal inference
Technical Field
The invention relates to the technical field of computer social networks, in particular to a social network information propagation history ordering method based on causal inference.
Background
In modern life, online Social Networks (SNS) have deeply affected daily life of each person, a great deal of social information appears and spreads in social network sites, and generally, one user may obtain the same information through different interested persons or friends. In a social network, the information dissemination process records the propagation path through propagation history records, and people can use the propagation history records to explain the behaviors of users in the social network, however, the propagation history records easily become very complex and large in scale, so that the intuitive understanding of the user on the propagation history records is limited.
In order to reduce the overload of the information, for those skilled in the art, it is highly desirable to innovatively propose a measure for ordering the edges or nodes in the propagation history according to the contribution to the information propagation.
Disclosure of Invention
The embodiment of the invention aims to solve the technical problem that: the method for sequencing the social network information propagation history based on causal inference is provided, so that the problem that in a complex social network, due to overload of information, a user cannot conveniently and intuitively understand the information propagation history, and contribution of each participant to social network information propagation is more conveniently and conveniently understood is solved.
According to an aspect of the embodiment of the invention, a social network information propagation history ranking method based on causal inference is provided, which is characterized by comprising the following steps:
collecting a history record of information propagation in a social network, wherein the history record of information propagation is related to nodes or edges, and the propagation of information in the social network refers to the information propagation from a resource node to a target node;
and sorting the edges in the history record of information propagation according to the contribution degrees of the edges in the information propagation, wherein the sorting is an improved causal effect difference algorithm and is used for evaluating the importance of the edges in the propagation history so as to measure the importance of the edges in the information propagation.
Further, the collecting the history of information propagation in the social network is characterized by:
Figure BDA0001048538140000021
the information dissemination event in the social network comprises S as an information source and S as a target node
Figure BDA0001048538140000022
tiRepresents a propagation path consisting ofiA continuous edge
Figure BDA0001048538140000023
Composition is carried out;
phi is a propagation history of the dissemination process, and tau is a set of edges in phi.
Further, in the history of information dissemination, the contribution degree of a certain specific edge X to the success of information dissemination has the following two measurement modes:
the edge X fails, and the probability of successful information transmission is as follows: p (phi)x'＝true)；
The edge X is valid, and the probability of successful information transmission is: p (phi)x＝true)；
Wherein X and X' represent the cases where edge X is "valid" and "invalid" in random experiments, respectively, P (Φ)x'True) and P (Φ)xTrue) are two causal variables that represent the necessary and sufficient portions of the edge X in the information propagation process, respectively.
Further, the causal effect difference algorithm is to select P (phi)x'True) and P (Φ)xTrue) two causal variables are combined, i.e. both the sufficient and necessary parts of the causal relationship are considered, and the algorithm formula is:
DCE(X)＝P(Φx＝true)-P(Φx'＝true)。
further, the causal effect difference algorithm comprises a necessary part and a sufficient part in the information propagation process, wherein the necessary part in the information propagation process is called responsibility, and the sufficient part in the information propagation process is called capacity;
the causal relationship in information dissemination is:
let T be the set of edges in the information dissemination history, T be a participating edge in the information dissemination history, T ∈ T be the set of a portion of edges in the information dissemination history,
Figure BDA0001048538140000031
if so:
after removal from T, from the information source
Figure BDA0001048538140000032
To the target node
Figure BDA0001048538140000033
The information dissemination of (2) was still successful;
after removal from T, further removal of T can lead to failure of information propagation;
then, t is called the reason of information dissemination and is an expected failure set of t;
the responsibility of t in information dissemination is:
Figure BDA0001048538140000034
the expected failure set containing all t;
the ability of t in information dissemination is:
Figure BDA0001048538140000035
where τ represents all propagation paths through t, and the return value of the function st (τ) is the edge set of τ;
the strategy for sorting the edges in the information propagation history record according to the contribution degree of the edges in the information propagation by using the causal effect difference algorithm is called as a 'responsibility-ability' sorting strategy.
Further, the algorithm formula of the "responsibility-ability" ranking strategy is as follows:
score＝α*fn(responsibility)+(1-α)fn(capability)
where fn represents a regularization function for calculating the standard score, responsilibility represents responsibility, capability represents capacity, and 0< alpha <1 is a balance factor.
Further, the algorithm implementation of the responsibility includes:
inputting a propagation history record phi, a set T of edges in the phi and an edge T belonging to T;
obtaining a covered set SA-sc (t, Φ) and an uncovered set ST- Φ -SA, said sc (t, Φ)j,Φ)＝{ci|tj∈ci∧ci∈ phi } and initial forecast accident set
Figure BDA0001048538140000036
Selecting an edge x ∈ T-;
add x to SA, remove sc (x, SA) from SA, remove sc (x, ST) from ST;
repeating the two steps until ST is empty;
output of
Figure BDA0001048538140000041
As a responsibility value for t.
Further, the selection edge x ∈ T-satisfies the following two conditions:
sc (x, ST) covers as many sets in ST as possible;
SA≠sc(x,SA)。
based on the social network information propagation history sequencing method based on causal inference provided by the embodiment of the invention, the information propagation history sequencing problem in the social network based on causal inference is creatively provided for the first time, the necessary part and the sufficient part of the causal effect are obtained by using two indexes of responsibility and capability, so that a complex algorithm is avoided, edges or nodes in the propagation history are sequenced according to contributions in information propagation, the algorithm for calculating the contribution of each participant in the social network is simpler, and the intuitive understanding of the contribution of each participant by a user is improved.
The embodiment of the invention can be applied to social networks, education networks, military networks and game networks, and is particularly suitable for the social networks.
The technical solution of the present invention is further described in detail by the accompanying drawings and embodiments.
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The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description, serve to explain the principles of the invention.
The invention will be more clearly understood from the following detailed description, taken with reference to the accompanying drawings, in which:
FIG. 1 is a flowchart of an embodiment of a social networking information propagation history ranking method based on causal inference according to the present invention.
FIG. 2 is a flow chart of another embodiment of a social networking information dissemination history ranking method based on causal inference according to the present invention.
Detailed Description
Various exemplary embodiments of the present invention will now be described in detail with reference to the accompanying drawings. It should be noted that: the relative arrangement of the components and steps, the numerical expressions and numerical values set forth in these embodiments do not limit the scope of the present invention unless specifically stated otherwise.
Meanwhile, it should be understood that the sizes of the respective portions shown in the drawings are not drawn in an actual proportional relationship for the convenience of description.
The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the invention, its application, or uses.
Techniques, methods, and apparatus known to those of ordinary skill in the relevant art may not be discussed in detail but are intended to be part of the specification where appropriate.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, further discussion thereof is not required in subsequent figures.
FIG. 1 is a flowchart of an embodiment of a social networking information propagation history ranking method based on causal inference according to the present invention. As shown in fig. 1, the method of this embodiment includes:
collecting information propagation history records in a social network, wherein the information propagation history records are related to nodes or edges, and event propagation in the social network refers to information propagation of events from a resource node to a target node;
and 20, sequencing the nodes or edges in the history record of the information propagation according to the contribution degrees of the nodes or edges in the information propagation, wherein the propagation history sequencing is an improved causal effect difference algorithm for evaluating the importance of the edges in the propagation history.
The step 10 of collecting the history of information propagation in the social network is characterized by:
Figure BDA0001048538140000051
the information dissemination event in the social network comprises S as an information source and S as a target node
Figure BDA0001048538140000052
tiRepresents a propagation path consisting ofiA continuous edge
Figure BDA0001048538140000053
Composition is carried out;
phi is a propagation history record of a spreading process, and T is a set of edges in phi;
in the absence of ambiguity, the subscript can be removed (,
Figure BDA0001048538140000054
)。
the goal of the propagation history data sorting is to sort all edges in T according to their contribution during dissemination.
In the propagation history described above, the importance of a particular edge is of major concern to those skilled in the art for two issues: what will this have the effect on information propagation if the edge fails? If the edge is not stale, which in turn will give the information dissemination what results? Therefore, considering the probability of successful information delivery, the contribution of a particular edge X to the success of information propagation has the following two measures:
the edge X fails, and the probability of successful information transmission is as follows: p (phi)x'＝true)；
The edge X is valid, and the probability of successful information transmission is: p (phi)x＝true)；
Wherein X and X' represent the cases where edge X is "valid" and "invalid" in random experiments, respectively, P (Φ)x'True) and P (Φ)xTrue) are two causal variables that represent the necessary and sufficient portions of the edge X in the information propagation process, respectively.
The reason described in step 20The effect difference algorithm is two causal variables P (phi) of the sufficient part and the necessary partx'True) and P (Φ)xTrue), that is, both the sufficient part and the necessary part of the causal relationship are considered, the algorithm formula is:
DCE(X)＝P(Φx＝true)-P(Φx'＝true)………………………………(2)
the causal calculation is performed, the most correct method is the randomization test, and in this example, we briefly describe the algorithm:
get input edge X, we initialize X as "valid" (or "invalid");
in each simulation, other edges are randomly set as 'non-failure' or 'failure', and whether information propagation is successful is further detected;
through a large number of simulations, the probability P (phi) is obtainedxTrue) (or P (Φ)x'＝true))；
The DCE value was obtained by equation (2).
Although the above method using randomization can calculate the DCE value, it needs to be run many times to obtain a stable convergence value.
The causal effect difference algorithm comprises a necessary part and a sufficient part in the information propagation process, wherein the necessary part in the information propagation process is called responsibility, the sufficient part in the information propagation process is called capacity, in order to avoid complex calculation, edges in the history record of information propagation are sorted according to the contribution degree of the edges in the information propagation according to the causal effect difference algorithm in the information propagation, a 'responsibility-capacity' strategy is provided, and two aspects of the causal relationship effect are obtained by simultaneously adopting two indexes, wherein the two aspects are specifically as follows:
the calculation method of the responsibility and the capability comprises the following steps:
let T be the set of edges in the information dissemination history, T be a participating edge in the information dissemination history, T ∈ T be the set of a portion of edges in the information dissemination history,
Figure BDA0001048538140000071
if so:
after removal from T, from the information source
Figure BDA0001048538140000072
To the target node
Figure BDA0001048538140000073
The information dissemination of (2) was still successful;
after removal from T, further removal of T can lead to failure of information propagation;
then, t is called the reason of information dissemination and is an expected failure set of t;
the responsibility of t in information dissemination is:
Figure BDA0001048538140000074
the expected failure set containing all t;
for example, the propagation history in one dissemination process is Φ ═ t1,t2,t3Where t is1＝{A,B}，t2＝{A,C}，t3The responsibility for edge a is 1/2 since the minimum expected failure set for a is { D }. Similarly, edge D has 1/2 as the smallest expected failure set for D is { A }. The minimum expected failure sets for B and C are { C, D } and { B, D } respectively, so their responsibilities are 1/3.
the ability of t in information dissemination is:
Figure BDA0001048538140000075
where τ represents all propagation paths through t, and the return value of the function st (τ) is the edge set of τ;
as described in the example above, edge A has a capability of 1/2 because it requires { B } and { C } to ensure propagation success, edges B and C have a capability of 1/2 because they both require { A } to pass information, and edge D has a capability of 1 because D itself ensures information propagation.
The algorithm formula of the sequencing strategy of 'responsibility-ability' obtained by the analysis is as follows:
score＝α*fn(responsibility)+(1-α)fn(capability)……………………(3)
where fn represents a regularization function that calculates the standard score and 0< alpha <1 is a balance factor.
Since the calculation of responsibility values is an NP-hard problem, we propose an approximation algorithm:
FIG. 2 is a flowchart of another embodiment of a social networking information dissemination history ranking method based on causal inference according to the present invention, as shown in FIG. 2:
101, inputting a propagation history record phi, a set T of edges and an edge T belonging to T;
102, a covered set SA-sc (t, Φ) and an uncovered set ST- Φ -SA are obtained, said sc (t, Φ)j,Φ)＝{ci|tj∈ci∧ci∈ Φ, initial set of forecasted incidents
Figure BDA0001048538140000082
103, selecting an edge x ∈ T-;
104, adding x to the SA, removing sc (x, SA) from the SA, and removing sc (x, ST) from ST;
105, repeating the two steps 103 and 104 until ST is empty;
106, output
Figure BDA0001048538140000081
As a responsibility value for t.
In the above step 103, the selection edge x ∈ T — satisfies the following two conditions:
(1) sc (x, ST) covers as many sets in ST as possible;
(2)SA≠sc(x,SA)。
among the above conditions, the first condition is a rule for heuristically ordering edges to be added to the expected failure set. The second condition is a constraint that ensures that t remains one of the reasons in the information propagation after the expected failure set is removed.
In the present specification, the embodiments are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same or similar parts in the embodiments are referred to each other. For the system embodiment, since it basically corresponds to the method embodiment, the description is relatively simple, and for the relevant points, reference may be made to the partial description of the method embodiment.
The method and apparatus of the present invention may be implemented in a number of ways. For example, the methods and apparatus of the present invention may be implemented in software, hardware, firmware, or any combination of software, hardware, and firmware. The above-described order for the steps of the method is for illustrative purposes only, and the steps of the method of the present invention are not limited to the order specifically described above unless specifically indicated otherwise. Furthermore, in some embodiments, the present invention may also be embodied as a program recorded in a recording medium, the program including machine-readable instructions for implementing a method according to the present invention. Thus, the present invention also covers a recording medium storing a program for executing the method according to the present invention.
The description of the present invention has been presented for purposes of illustration and description, and is not intended to be exhaustive or limited to the invention in the form disclosed. Many modifications and variations will be apparent to practitioners skilled in this art. The embodiment was chosen and described in order to best explain the principles of the invention and the practical application, and to enable others of ordinary skill in the art to understand the invention for various embodiments with various modifications as are suited to the particular use contemplated.

Claims (8)

1. A social network information propagation history ranking method based on causal inference is characterized by comprising the following steps:
collecting a history record of information propagation in a social network, wherein the history record of information propagation is related to nodes or edges, and the propagation of information in the social network refers to the information propagation from a resource node to a target node;
the method comprises the steps that edges in a history record of information propagation are ranked according to the contribution degree of the edges in the history record of the information propagation, the ranking is an improved causal effect difference algorithm, the improved causal effect difference algorithm is ranked according to the edges in the history record of the information propagation and the contribution degree of the edges in the information propagation, a 'responsibility-ability' strategy is provided, two aspects of causal relation effect are obtained by simultaneously adopting two indexes, the importance of the edges in the propagation history is evaluated, and the importance of the edges in the information propagation is further measured;
wherein said responsibility is through
Figure FDA0002549724270000011
Calculating to obtain an expected fault set containing all t, wherein t is a reason for information dissemination;
said capability passing through
Figure FDA0002549724270000012
And calculating, wherein τ represents all propagation paths passing through the t, and the function st (τ) returns an edge set with the value of τ.
2. The method of claim 1, wherein the collecting the history of information propagation in the social network is characterized by:
Figure FDA0002549724270000013
the information dissemination event in the social network comprises S as an information source and S as a target node
Figure FDA0002549724270000015
tnRepresents a propagation path consisting ofnA continuous edge
Figure FDA0002549724270000014
Composition is carried out;
phi is the propagation history of a dissemination process, and t is the set of edges in phi.
3. The method according to claim 2, wherein in the history of information dissemination, the contribution degree of a specific edge X to the success of information dissemination has the following two measures:
the edge X fails, and the probability of successful information transmission is as follows: p (phi)x'＝true)；
The edge X is valid, and the probability of successful information transmission is: p (phi)x＝true)；
Wherein X and X' represent the cases where edge X is "valid" and "invalid" in random experiments, respectively, P (Φ)x'True) and P (Φ)xTrue) are two causal variables that represent the necessary and sufficient portions of the edge X in the information propagation process, respectively.
4. The method according to any of claims 1 to 3, wherein the causal effect difference algorithm is to assign P (Φ)x'True) and P (Φ)xTrue) two causal variables are combined, i.e. both the sufficient and necessary parts of the causal relationship are considered, and the algorithm formula is:
DCE(X)＝P(Φx＝true)-P(Φx'＝true)；
the dce (x) is the result of combining the sufficient fraction and the essential fraction of the two causal variables.
5. The method according to any one of claims 1 to 3, wherein the causal effect difference algorithm comprises a necessary part and a sufficient part in the information propagation process, wherein the necessary part in the information propagation process is called responsibility, and the sufficient part in the information propagation process is called capacity;
the causal relationship in information dissemination is:
let T be the set of edges in the information dissemination history, T be a participating edge in the information dissemination history, T ∈ T be the set of a portion of edges in the information dissemination history,
Figure FDA0002549724270000021
if so:
after removal from T, from the information source
Figure FDA0002549724270000022
To the target node
Figure FDA0002549724270000023
The information dissemination of (2) was still successful;
after removal from T, further removal of T can lead to failure of information propagation;
then, t is called the reason of information dissemination and is an expected failure set of t;
and (3) a strategy of sorting the edges in the history record of information propagation according to the contribution degree of the edges in the information propagation by using the causal effect difference algorithm, which is called as a 'responsibility-ability' sorting strategy.
6. The method according to claim 5, wherein the algorithm formula of the "responsibility-ability" ranking strategy is as follows:
score＝α*fn(responsibility)+(1-α)fn(capability)
where fn represents a regularization function for calculating the standard score, responsilibility represents responsibility, capability represents capacity, and 0< alpha <1 is a balance factor.
7. The method of claim 5, wherein the algorithmic implementation of the responsibilities comprises:
inputting a propagation history record phi, a set T of edges in the phi and an edge T belonging to T;
obtaining a covered set SA-sc (t, Φ) and an uncovered set ST- Φ -SA, said sc (t, Φ)j,Φ)＝{ci|tj∈ci∧ci∈ phi } and initial forecast accident set
Figure FDA0002549724270000031
Selecting an edge x ∈ T-;
add x to SA, remove sc (x, SA) from SA, remove sc (x, ST) from ST;
repeating the steps of selecting an edge x ∈ T-, adding x to the SA, removing sc (x, SA) from SA, and removing sc (x, ST) from ST until ST is empty;
output of
Figure FDA0002549724270000032
As a responsibility value for t.
8. The method of claim 7, wherein the selection edge x e T-satisfies the following two conditions:
sc (x, ST) covers as many sets in ST as possible;
SA≠sc(x,SA)。
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