CN113222774B - Social network seed user selection method and device, electronic equipment and storage medium - Google Patents
Social network seed user selection method and device, electronic equipment and storage medium Download PDFInfo
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Abstract
The invention discloses a social network seed user selection method and device, electronic equipment and a storage medium. The method comprises the following steps: modeling a social network and user behaviors to obtain a time-aware social network propagation model; selecting any user for reverse propagation simulation for a plurality of times according to the social network propagation model, recording the activated users in the reverse propagation simulation, and forming the users into a reverse reachable set RRsets; and according to the reverse reachable sets RRsets, using the number of intersections of the user sets and the reverse reachable sets RRsets to represent approximate influence of any user in the user sets, and greedy selecting a preset number of users based on the approximate influence to serve as seed users in the social network. The method has high efficiency and robustness.
Description
Technical Field
The invention relates to the problem of maximizing influence, in particular to a social network seed user selection method and device, electronic equipment and storage medium.
Background
With the development of social networks, a large amount of social media (such as microblogs, weChats, facebook, etc.) has become a part of daily life, and more people share and spread information by using social networks; meanwhile, merchants hope to market company products by means of the propagation effect of the social network, so that greater benefits are obtained, and therefore, the virus type marketing problem on the social network is paid great attention. For example, eMarkter estimates that global advertisers spend approximately 350 billions in social networking marketing in 2013-2017; fortunes indicated that advertising costs on social networks exceeded $ 500 billion in 2020.
Specifically, the propagation process on a social network is as follows: a group of influential users are selected as seed users, information is transmitted to friends or fan through their mouth-to-mouth, and friends of the users receive the information with a certain probability (reflecting the influence degree among the users) and become continuous transmission influences to surrounding users. In this way, messages continue to spread out in a cascading fashion until no new users are affected on the social network. The most typical application of the process is social network marketing, as shown in fig. 1, some seed users (such as network red) are selected to carry out marketing promotion of specific products, new users continuously receive marketing information and spread to surrounding users through influence spread of the seed users, and the purpose of marketing promotion is achieved.
Conventional techniques consider how to select a certain number of users to maximize the number of users affected across the social network, i.e., the problem of maximizing impact. However, they assume that propagation between all users does not require time, nor do they consider the impact of propagation time on the probability of success of the propagation, e.g., close users have shorter time to propagate information, and short time propagation has a greater probability of success.
Therefore, for the limitation of the traditional method, the method aims at the problem of maximizing the influence on the social network, considers the effect of time factors in the social network, formalizes the problem of maximizing the influence under the time limit, and provides a simple and efficient algorithm based on the greedy idea.
Disclosure of Invention
The embodiment of the invention aims to provide a social network seed user selection method and device, electronic equipment and storage medium, so as to solve the problem that the traditional scheme does not consider time factors.
According to a first aspect of an embodiment of the present invention, there is provided a social network seed user selection method, the method including:
modeling a social network into a directed probability graph g= (V, E, P), wherein V represents a set of all users, E is a set of social relationships between all users, P represents a set of propagation probabilities on all sides, representing the original activation probabilities between users;
modeling user behaviors in a social network by considering propagation time among users and influence of the propagation time on propagation probability to obtain a user behavior model, wherein the rules of the user behavior model are as follows: each activated user only has one opportunity to try to activate the social friends, the time required for activating the friends obeys the geometric distribution or poisson distribution, and the success probability of activating the friends is the product of the original activation probability among the users and the time delay function;
selecting a part of users of the social network as initial activated users based on the social network and the user behavior model, wherein the users activate the users according to rules in the user behavior model, so that more users are activated and perform activation until no new users are activated or a preset time is reached, and obtaining a time-aware social network propagation model;
selecting any user for reverse propagation simulation for a plurality of times according to the social network propagation model, recording the activated users in the reverse propagation simulation, and forming the users into a reverse reachable set RRsets;
and according to the reverse reachable sets RRsets, using the number of intersections of the user sets and the reverse reachable sets RRsets to represent approximate influence of any user in the user sets, and greedy selecting a preset number of users based on the approximate influence to serve as seed users in the social network.
According to a second aspect of an embodiment of the present invention, there is provided a social network seed user selection apparatus, including:
the social network modeling module is used for modeling the social network into a directed probability graph G= (V, E, P), wherein V represents a set formed by all users, E is a social relation set among all users, P represents a propagation probability set on all sides and represents an original activation probability among the users;
the user behavior modeling module is used for modeling user behaviors in the social network by considering propagation time among users and influence of the propagation time on the propagation probability to obtain a user behavior model, and the rules of the user behavior model are as follows: each activated user only has one opportunity to try to activate the social friends, the time required for activating the friends obeys the geometric distribution or poisson distribution, and the success probability of activating the friends is the product of the original activation probability among the users and the time delay function;
the time-aware social network propagation modeling module is used for selecting part of users of the social network as initial activated users based on the social network and the user behavior model, and enabling the users to activate according to rules in the user behavior model so that more users are activated and conduct activation until no new users are activated or a preset time is reached, so as to obtain a time-aware social network propagation model;
the simulation module is used for selecting any user for reverse propagation simulation for a plurality of times according to the social network propagation model, recording the activated users in the reverse propagation simulation, and forming the users into a reverse reachable set RRsets;
and the selection module is used for selecting a preset number of users based on the approximate influence greedy by using the number of intersections of the user set and the reverse reachable set RRsets to represent the approximate influence of any user in the user set according to the reverse reachable set RRsets and taking the preset number of users as seed users in the social network.
According to a third aspect of an embodiment of the present invention, there is provided an apparatus comprising: one or more processors; a memory for storing one or more programs; the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the method of the first aspect.
According to a fourth aspect of embodiments of the present invention, there is provided a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the method according to the first aspect.
According to the technical scheme, the method provided by the embodiment of the invention models the propagation time and the propagation influence thereof in the social network, so that a time-aware social network propagation model is obtained, and further, reverse reachable sets RRsets are obtained based on the model for multiple reverse propagation simulation, and finally, seed users are selected according to the reverse reachable sets RRsets, so that the limitation that the time factor is not considered in the existing social network seed user selection method is overcome, the problem that the seed users select a non-deterministic polynomial under the consideration of the time factor is solved, the influence of the selected seed users can reach a preset approximate ratio compared with the optimal solution, and the precision guarantee is provided for the effectiveness of the selected seed users.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the invention and do not constitute a limitation on the invention. In the drawings:
FIG. 1 is a diagram of an example social network propagated marketing in the prior art;
FIG. 2 is a flow chart of a social network seed user selection method provided by an exemplary embodiment of the present invention;
FIG. 3 is a block diagram of a social network seed user selection apparatus provided in an exemplary embodiment of the present invention.
Detailed Description
The technical scheme of the invention is further described with reference to the accompanying drawings and specific implementation:
FIG. 2 is a block diagram of a social network seed user selection method that accounts for time factors in accordance with an embodiment of the present invention; referring to fig. 2, an embodiment of the present invention provides a social network seed user selection method, which may include the steps of:
step S11, modeling the social network into a directed probability graph g= (V, E, P), where V represents a set of all users, E is a set of social relationships between all users, and P represents a set of propagation probabilities on all sides, representing an original activation probability between users;
step S12, modeling user behaviors in the social network by considering propagation time among users and influence of the propagation time on the propagation probability to obtain a user behavior model, wherein rules of the user behavior model are as follows: each activated user only has one opportunity to try to activate the social friends, the time required for activating the friends obeys the geometric distribution or poisson distribution, and the success probability of activating the friends is the product of the original activation probability among the users and the time delay function;
step S13, selecting a part of users of the social network as initial activated users based on the social network and the user behavior model, wherein the users activate the users according to rules in the user behavior model, so that more users are activated and perform activation until no new users are activated or a preset time is reached, and obtaining a time-aware social network propagation model;
step S14, selecting any user for reverse propagation simulation for a plurality of times according to the social network propagation model, recording the activated users in the reverse propagation simulation, and forming the users into a reverse reachable set RRsets;
step S15, according to the reverse reachable set RRsets, using the number of intersections of the user set and the reverse reachable set RRsets to represent approximate influence of any user in the user set, and greedy selecting a preset number of users based on the approximate influence to serve as seed users in the social network.
According to the technical scheme, the method provided by the embodiment of the invention models the propagation time and the propagation influence thereof in the social network, so that a time-aware social network propagation model is obtained, and further, reverse reachable sets RRsets are obtained based on the model for multiple reverse propagation simulation, and finally, seed users are selected according to the reverse reachable sets RRsets, so that the limitation that the time factor is not considered in the existing social network seed user selection method is overcome, the problem that the seed users select a non-deterministic polynomial under the consideration of the time factor is solved, the influence of the selected seed users can reach a preset approximate ratio compared with the optimal solution, and the precision guarantee is provided for the effectiveness of the selected seed users. The embodiment of the invention has high efficiency and robustness under different time settings.
In a specific implementation of step S11, the social network is modeled as a directed probability graph g= (V, E, P), where V represents a set of all users, E is a set of social relationships between all users, and P represents a set of propagation probabilities on all sides, representing the original activation probabilities between users;
specifically, each user V is a node in the graph G, V e V is satisfied, and the social relationship between the users u and V is a directed edge e between the nodes in the graph G u，v E, user uThe degree of influence on v is the propagation probability p on the edge u，v e.P. By modeling the social network into the directed probability graph, various social networks in real life can be effectively represented, and the relevant research of the graph provides basis for further analysis on the social network.
In the implementation of step S12, modeling is performed on the user behavior in the social network to obtain a user behavior model, where rules of the user behavior model are as follows: each activated user only has one opportunity to try to activate the social friends, the time required for activating the friends obeys the geometric distribution or poisson distribution, and the success probability of activating the friends is the product of the original activation probability among the users and the time delay function;
specifically, modeling user behavior in a social network results in a user behavior model, the rules of which are as follows, each activated user v (set to its activated time t) has only one opportunity to attempt to activate its social friends u, the time delta required to activate the friends t Obeying geometric distribution or poisson distribution, v is t+delta t The success probability of activating friends at the moment is the original activation probability p between users u，v And a delay function f (delta) t ) And satisfies the following formula:
wherein f (delta) t ) For a delay function taking the propagation time as an independent variable, there are two forms of an exponential function and a power function, and alpha is a super parameter, and represents the influence degree of the propagation time on the propagation probability, and the larger the alpha is, the larger the influence is. Geometric distribution and poisson distribution are commonly used to represent possible time and existence probability, and exponential function and power function as time delay function can represent the characteristic that propagation probability decreases with increasing propagation time, which is consistent with reality.
In a specific implementation of step S13, based on the social network and the user behavior model, a part of the users of the social network are selected as initial activated users, and the users activate the users according to rules in the user behavior model, so that more users are activated and perform activation until no new users are activated or a predetermined time T is reached, and a time-aware social network propagation model is obtained.
Specifically, at an initial time t 0 Selecting part of nodes S epsilon V in G to be marked as an activated state, and activating neighbor nodes u of the nodes V marked as the activated state for the first time at any time t, wherein the activation time and the activation probability are obtained by the user behavior model rule, and if u is successfully activated, u is marked as the activated state at the corresponding time. The above process continues until a certain time T has no first activated node or t=t. The model considers the propagation time among users and the influence of the propagation time on the propagation probability, corresponds to the law of existence of information propagation time in real life and relatively smaller influence of outdated information, researches the propagation process under the preset time T, and meets the requirement of advertisers and the like on the influence effect of advertisements in specific time on a social network in practice.
In the implementation of step S14, according to the social network propagation model, any user is selected for performing the backward propagation simulation multiple times, the activated users in the backward propagation simulation are recorded, and the users are formed into the backward reachable set RRsets.
Selecting any user for reverse propagation simulation for multiple times according to the social network propagation model, specifically selecting any user on the social network as an initial activated user with the same probability for multiple times according to the social network propagation model, and starting reverse activation behavior until no new user is reversely activated or the propagation process reaches a preset time.
This step is further elaborated in connection with the examples below, which can be described in the following steps:
(1) Determining the number N of back propagation simulations based on theoretical evidence m It is defined as follows:
where e is a natural logarithm, δ is a predetermined approximate probability, e is a predetermined error threshold, n is the number of users in the social network G, and k is the number of users to be selected. N (N) m Representing simulation N m After the back propagation process, the probability that the influence ratio of the selected k users in the T time to the influence ratio of the optimal solution has at least 1-delta satisfies a predetermined approximation ratio of 1-1/e E.
(2) In each back propagation simulation, randomly selecting any user z in the social network with the same probability as the user activated at the initial moment, adding z into RRset, and starting back propagation simulation according to the social network propagation model.
(3) At any time t, for a user v that is successfully activated at each time t, if v is not activated at a previous time, v is added to RRset, v tries to activate each of its internal neighbor friends u (i.e., friend u has an edge pointing to v in G), the time delta required to activate the friend t Obeying geometric distribution or poisson distribution, v is t+delta t The success probability of activating friends at the moment is p u，v *f(δ t ) Meaning that u has p u，v *f(δ t ) The probability of (c) is t+delta t The moment is successfully activated. If the activation is successful, u will be at t+delta t The moment is activated. The propagation simulation procedure described above is performed for each reverse neighbor node of v. If v has been activated at a time before t, the user is skipped. If the current time T is not successfully affected by the user or T is larger than the given time limit T, the back propagation simulation is ended, otherwise, the next time t+1 is entered, and the step (3) is repeated;
(4) Repeating the back propagation simulation process N m Next, N is obtained m The RRsets constitute RRsets.
In a specific implementation of step S15, according to the reverse reachable set RRsets, the number of intersections between the user set and the reverse reachable set RRsets is used to represent the approximate influence of any user in the user set, and a predetermined number of users are greedy selected based on the influence.
Specifically, according to the reverse reachable sets RRsets, whether the user set has an intersection with each RRset is sequentially judged, the number of intersections of the user set and the reverse reachable sets RRsets is recorded, the approximate influence of any user in the user set is obtained, and the user is sequentially selected according to the following greedy rule based on the approximate influence:
selecting the first user, selecting the user with the greatest approximate influence, selecting the second user, selecting the user with the greatest approximate influence after being combined with the first selected user, and the like until the preset number of users are selected.
This step is further elaborated in connection with the examples below, which can be described in the following steps:
(1) According to the reverse reachable set RRset, sequentially judging a user set S and each RRset R i Whether intersection exists or not, recording the number of intersections of the user set and the RRsets to obtain approximate influence of any user in the user set, wherein the approximate influence is as follows:
wherein R is i Is one of the RRset which is one of the RRset,
for indicating function, if S and R i There is an intersection of 1 and 0 otherwise.
(2) Using S * Representing a selected set of seed users, initializing to null, greedy selecting the user with the greatest relative approximate influence each time, and joining S * Until a predetermined number of users are selected, wherein the relative approximate influence is as follows:
Λ(v|S * )＝Λ(S * ∪{v})-Λ(S * ) (4)
it can be found that when a first user is selected, the user having the greatest approximate influence is selected, when a second user is selected, the user having the greatest approximate influence after being associated with the first selected user is selected, and so on, until a predetermined number of users are selected. By the selection modeAnd counter-propagation analog number N m The probability that the ratio of the influence of the selected seed user to the optimal solution has at least 1-delta meets the preset approximate ratio of 1-1/e-E can be ensured, so that the selected seed user can generate approximate optimal influence in the actual social network marketing activities and the like.
Corresponding to the embodiment of the social network seed user selection method, the application also provides an embodiment of the social network seed user selection device.
FIG. 3 is a block diagram of a social network seed user selection device, according to an example embodiment. Referring to fig. 3, the apparatus includes:
a social network modeling module 21, configured to model a social network into a directed probability graph g= (V, E, P), where V represents a set of all users, E is a set of social relationships between all users, and P represents a set of propagation probabilities on all edges, and represents an original activation probability between users;
the user behavior modeling module 22 is configured to model user behaviors in the social network in consideration of propagation time between users and influence of the propagation time on the propagation probability, so as to obtain a user behavior model, where rules of the user behavior model are as follows: each activated user only has one opportunity to try to activate the social friends, the time required for activating the friends obeys the geometric distribution or poisson distribution, and the success probability of activating the friends is the product of the original activation probability among the users and the time delay function;
a time-aware social network propagation modeling module 23, configured to select a part of users of the social network as initial activated users based on the social network and the user behavior model, where the users activate the users according to rules in the user behavior model, so that more users are activated and perform activation until no new users are activated or a predetermined time is reached, and obtain a time-aware social network propagation model;
the simulation module 24 is configured to select any user for performing a back propagation simulation multiple times according to the social network propagation model, record the users activated in the back propagation simulation, and form the users into a reverse reachable set RRsets;
a selection module 25, configured to use the number of intersections between the set of users and the reverse reachable set of RRsets to represent the approximate influence of any user in the set of users according to the reverse reachable set of RRsets, and greedy select a predetermined number of users based on the approximate influence as seed users in the social network.
The specific manner in which the various modules perform the operations in the apparatus of the above embodiments have been described in detail in connection with the embodiments of the method, and will not be described in detail herein.
For the device embodiments, reference is made to the description of the method embodiments for the relevant points, since they essentially correspond to the method embodiments. The apparatus embodiments described above are merely illustrative, wherein the elements illustrated as separate elements may or may not be physically separate, and the elements shown as elements may or may not be physical elements, may be located in one place, or may be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purposes of the present application. Those of ordinary skill in the art will understand and implement the present invention without undue burden.
Correspondingly, the application also provides electronic equipment, which comprises: one or more processors; a memory for storing one or more programs; the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the social network seed user selection method as described above.
Accordingly, the present application further provides a computer readable storage medium having stored thereon computer instructions, wherein the instructions, when executed by a processor, implement a social network seed user selection method as described above.
While the foregoing is directed to the preferred embodiments of the present invention, it will be appreciated by those skilled in the art that various modifications and adaptations can be made without departing from the principles of the present invention, and such modifications and adaptations are intended to be comprehended within the scope of the present invention.
Claims (6)
1. A social network seed user selection method, the method comprising:
modeling a social network into a directed probability graph g= (V, E, P), wherein V represents a set of all users, E is a set of social relationships between all users, P represents a set of propagation probabilities on all sides, representing the original activation probabilities between users;
modeling user behaviors in a social network by considering propagation time among users and influence of the propagation time on propagation probability to obtain a user behavior model, wherein the rules of the user behavior model are as follows: each activated user only has one opportunity to try to activate the social friends, the time required for activating the friends is subjected to geometric distribution or poisson distribution, the influence of the activation time on the activation success probability is modeled as a time delay function and is subjected to an exponential function or a power function, and the final success probability of the friends is the product of the original activation probability among the users and the time delay function;
selecting a part of users of the social network as initial activated users based on the social network and the user behavior model, wherein the users activate the users according to rules in the user behavior model, so that more users are activated and perform activation until no new users are activated or a preset time is reached, and obtaining a time-aware social network propagation model;
determining the number of back propagation simulations based on the social network propagation model and theoretical evidence
Selecting any user for back propagation simulation, recording the activated user in the back propagation simulation, repeating the back propagation simulation process N m Second, the users are composed into reverse reachable sets RRsets;
according to the reverse reachable sets RRsets, the number of intersections of the user sets and the reverse reachable sets RRsets is used for representing approximate influence of any user in the user sets, and a preset number of users are selected as seed users in the social network based on the approximate influence greedy, so that the approximate influence ratio of the selected users is ensured;
wherein e is a natural logarithm, δ is a predetermined approximate probability, e is a predetermined error threshold, n is the number of users in the social network G, and k is the number of users to be selected;
wherein, according to the reverse reachable set RRsets, using the intersection number of the user set and the reverse reachable set RRsets to represent the approximate influence of any user in the user set, selecting a predetermined number of users based on the influence greedy, including:
according to the reverse reachable sets RRsets, whether the user sets have intersections with each RRset or not is judged in sequence, the number of intersections of the user sets and the reverse reachable sets RRsets is recorded, the approximate influence of any user in the user sets is obtained, and the users are selected in sequence according to the following greedy rule based on the approximate influence;
selecting the first user, selecting the user with the greatest approximate influence, selecting the second user, selecting the user with the greatest approximate influence after being combined with the first selected user, and the like until the preset number of users are selected.
2. The method of claim 1, wherein selecting any user multiple times for back propagation simulation according to the social network propagation model comprises:
according to the social network propagation model, any user on the social network is selected as an initial activated user with the same probability for a plurality of times, and reverse activation behavior is started until no new user is reversely activated or the propagation process reaches a preset time.
3. A social network seed user selection apparatus, comprising:
the social network modeling module is used for modeling the social network into a directed probability graph G= (V, E, P), wherein V represents a set formed by all users, E is a social relation set among all users, P represents a propagation probability set on all sides and represents an original activation probability among the users;
the user behavior modeling module is used for modeling user behaviors in the social network by considering propagation time among users and influence of the propagation time on the propagation probability to obtain a user behavior model, and the rules of the user behavior model are as follows: each activated user only has one opportunity to try to activate the social friends, the time required for activating the friends is subjected to geometric distribution or Poisson distribution, the influence of the activation time on the activation success probability is modeled as a time delay function, and the time delay function is subjected to an exponential function or a power function, so that the friends are activatedFinal resultThe success probability is the product of the original activation probability and the time delay function between users;
the time-aware social network propagation modeling module is used for selecting part of users of the social network as initial activated users based on the social network and the user behavior model, and enabling the users to activate according to rules in the user behavior model so that more users are activated and conduct activation until no new users are activated or a preset time is reached, so as to obtain a time-aware social network propagation model;
a simulation module for determining the number of back propagation simulations based on the social network propagation model and theoretical evidence
Selecting any user for back propagation simulation, recording the activated user in the back propagation simulation, repeating the back propagation simulation process N m Second, the users are composed into reverse reachable sets RRsets;
the selection module is used for selecting a preset number of users as seed users in the social network based on the approximate influence greedy by using the number of intersections of the user set and the reverse reachable set RRsets to represent the approximate influence of any user in the user set according to the reverse reachable set RRsets;
wherein e is a natural logarithm, δ is a predetermined approximate probability, e is a predetermined error threshold, n is the number of users in the social network G, and k is the number of users to be selected;
wherein, according to the reverse reachable set RRsets, using the intersection number of the user set and the reverse reachable set RRsets to represent the approximate influence of any user in the user set, selecting a predetermined number of users based on the influence greedy, including:
according to the reverse reachable sets RRsets, whether the user sets have intersections with each RRset or not is judged in sequence, the number of intersections of the user sets and the reverse reachable sets RRsets is recorded, the approximate influence of any user in the user sets is obtained, and the users are selected in sequence according to the following greedy rule based on the approximate influence;
selecting the first user, selecting the user with the greatest approximate influence, selecting the second user, selecting the user with the greatest approximate influence after being combined with the first selected user, and the like until the preset number of users are selected.
4. The apparatus of claim 3, wherein selecting any user multiple times for back propagation simulation according to the social network propagation model comprises:
according to the social network propagation model, any user on the social network is selected as an initial activated user with the same probability for a plurality of times, and reverse activation behavior is started until no new user is reversely activated or the propagation process reaches a preset time.
5. An electronic device, comprising:
one or more processors;
a memory for storing one or more programs;
the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the method of any of claims 1-2.
6. A computer readable storage medium, on which a computer program is stored, characterized in that the program, when being executed by a processor, implements the method according to any of claims 1-2.
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