CN103853848A - Method and device for establishing social monitoring subnetwork - Google Patents

Method and device for establishing social monitoring subnetwork Download PDF

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CN103853848A
CN103853848A CN201410121304.3A CN201410121304A CN103853848A CN 103853848 A CN103853848 A CN 103853848A CN 201410121304 A CN201410121304 A CN 201410121304A CN 103853848 A CN103853848 A CN 103853848A
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event
user
centerdot
subnet
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周异
陈凯
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Huawei Technologies Co Ltd
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    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/90—Details of database functions independent of the retrieved data types
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    • G06F16/958—Organisation or management of web site content, e.g. publishing, maintaining pages or automatic linking
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    • G06—COMPUTING; CALCULATING OR COUNTING
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    • G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/01—Social networking

Abstract

The invention provides method and device for establishing a social monitoring subnetwork. The method comprises the following steps of acquiring a sample event from a social contact network event; screening users who participate in the sample event to obtain a user group; establishing a subnetwork selecting model according to the fact that the event cover degree of the social contact monitoring subnetwork is higher than or equal to preset event cover degree Ne and the event monitoring probability of the social contact monitoring subnetwork is higher than or equal to preset event monitoring probability Pe; determining the number of the users according to the subnetwork selecting model; selecting the users needed by establishing the social contact monitoring subnetwork from the user group. The method provided by the invention can be used for establishing the social contact monitoring subnetwork composed of a small number of active and influential users and acquiring the social contact network event by detecting the social contact monitoring subnetwork, thereby greatly reducing the data quantity treatment, reducing the system cost, realizing the stand-alone detection, eliminating a large amount of noise information and enhancing the event detection accuracy rate.

Description

Method and device that a kind of social monitoring subnet builds
Technical field
The present invention relates to field, a kind of social networking, relate in particular to method and device that a kind of social monitoring subnet builds.
Background technology
In the Web2.0 epoch, social networks has become the important component part of people's network life, monthlyly browse the large class service of the super Domestic News of duration, become one of main flow information consulting platform, " Chinese society's public sentiment annual report (2012) " blue book of having been cooperated with Baidu by public opinion research institute of the Renmin University of China is concentrated and has been presented Social Development of China present situation in 2011 and hot issue, blue book shows, soaring and the right consciousness raising in netizen's quantity, much-talked-about topic emerges in an endless stream, " whole people's sounding ", under the Background of Internet of " surrounding and watching structure ", Chinese society's public sentiment presents complicated variation tendency, on social networks, propagating various fronts simultaneously, negative or even rumour information, and social influence negative and that rumour information is brought is very bad, so the event of propagating on social networks is monitored and is seemed very necessary.
At present, social networks event monitoring technology is mainly, based on traditional topic monitoring technology, all diffusing information of social networks processed to obtain event, concrete implementation is: adopt time segment to gather all event informations, the keyword construction feature vector of extraction event, by similarity comparison, by an event of information composition relevant all topics, whether be then the judgement of focus incident according to the number event of how much carrying out that participates in topic.
But, existing social networks event monitoring method need to be processed ability acquisition event to all social network informations, and social network data quantity of information is huge, require high to processing speed, cannot realize unit Real-Time Monitoring, and take microblogging network as example, by a small amount of microblogging keyword search topic and merging, can cause merged with the irrelevant noise microblogging of topic in a large number, be that the less topic merging of content accuracy rate is low, such as topic is generally all made up of multiple keywords, a lot of irrelevant microbloggings are but for no other reason than that comprise certain keyword and be also added in topic, finally can disturb the judgement of focus incident.
Summary of the invention
The invention provides method and device that a kind of social monitoring subnet builds, realized unit and detected in real time social networks event, reduced data volume processing, reduce systematic cost, and remove much noise information, improve the accuracy rate of event detection.
First aspect, the method that provides social monitoring subnet to build, comprising:
From social networks event, obtain sample event;
Screen and obtain user's group participating in the user of described sample event, described user's group comprises N user, and described N is positive integer;
Be more than or equal to predeterminable event coverage N according to the event coverage degree that builds social monitoring subnet e, and the event monitoring probability of described social monitoring subnet is more than or equal to predeterminable event monitoring probability P ebuild subnet Selection Model, described subnet Selection Model builds for determining the number of users that described social monitoring subnet should be chosen from described user's group, wherein, described event coverage degree is the number of users that participates in same event, and described event monitoring probability calculates according to event monitoring new probability formula;
The described number of users of determining according to described subnet Selection Model is chosen user and is built described social monitoring subnet from described user's group.
In the possible implementation of the first of first aspect, the described event coverage degree according to the described social monitoring subnet of structure is more than or equal to described N e, and the event monitoring probability of described social monitoring subnet is more than or equal to described P e, build subnet Selection Model, comprising:
The event of calculating each user in described user's group participates in probability P i, according to described P icalculate described event monitoring probability P with following described event monitoring new probability formula (1) r(x 1, x 2... x i..., x n):
P r ( x 1 , x 2 , · · · , x i , · · · , x N ) = Π i = 1 N P i x i * ( 1 - P i ) ( 1 - x i ) x 1 + x 2 + · · · + x i + · · · + x N ≤ N - - - ( 1 )
Wherein, described P ifor described user i participates in the ratio of described sample event number and described sample total number of events, x irepresent whether user i participates in the propagation of described sample event, and participation is 1, and not participating in is 0, i ∈ [1, N], r ∈ [1, N];
Described subnet Selection Model builds by following formula (2):
Obtain according to formula (1) and (2) the number of users n that following formula (3) determines that the described social monitoring subnet of structure should be chosen from described user's group:
Σ x 1 + x 2 + . . x i + . . . + x N ≥ N e x 1 + x 2 + . . x i + . . . + x N ≤ N ( Π i = 1 N P i x i * ( 1 - P i ) ( 1 - x i ) ) ≥ P e - - - ( 3 )
The implementation possible according to the first of first aspect in the possible implementation of the second, chosen user and built described social monitoring subnet from described user's group, comprising:
Adopt the method for dynamic programming from described user's group, to choose the required user of the described social monitoring subnet of structure, be specially:
The event of each user in described user's group is participated in to probability P iaccording to arranging from big to small, the node sequence after sequence is M 1..., M j..., M n;
From j node, getting the probability that a front k node participates in described sample event is P m(k, j), described P m(k, j) drawn by following formula (4) recursion:
P m ( k , j ) = 1 , k = 0 , j = 0 0 , k > j ( 1 - P j ) * P m ( k , j - 1 ) + P j * P m ( k - 1 , j - 1 ) , k ≤ j , j ∈ [ 1 , N ] , - - - ( 4 )
Wherein initial value is, P m ( 0,0 ) = 1 , P m ( 0,1 ) = 1 - P 1 , · · · , P m ( 0 , j ) = ( 1 - P 1 ) * · · · * ( 1 - P j ) , P m ( k , k - 1 ) = P m ( k , k - 2 ) = · · · = P m ( k , 0 ) = 0 ;
Judge the described P that recursion obtains each time mwhether (k, j) and described k meet following formula (5):
k ≥ n P m ( k , j ) ≥ P e - - - ( 5 )
If meet, choose node M from described user's group 1..., M jcorresponding user is as building the required user of described social monitoring subnet;
If do not meet, continue according to described formula (4) recursion until described P mtill (k, j) and described k meet described formula (5).
According to the first of first aspect, first aspect, to the possible arbitrary implementation of the second, in the third possible implementation, the described sample event of obtaining from social networks event, comprising:
Choosing and participating in the event that the number of users of event forwarding exceedes default number of users is described social networks event;
According to forwarding quantity and the event type of described social networks event, from described social networks event, obtain described sample event.
,, in the 4th kind of possible implementation, describedly, also comprise before the user of described sample event screens and obtain user's group participating in to the third possible arbitrary implementation according to the first of first aspect, first aspect:
Obtain the event information that participates in described sample event, described event information comprises the user name that participates in described sample event, and user participates in the time of described sample event, and user participates in the relation that described sample event procedure forwards and is forwarded.
According to four kinds of possible arbitrary implementations of the first to the of first aspect, first aspect, in the 5th kind of possible implementation, describedly screen and obtain user's group participating in the user of described sample event, comprising:
Number of times or the bean vermicelli number of the event of participation are screened out lower than the user of preset value; And/or
Be greater than the default user who forwards number and screen out repeating to forward identical information and hop count; And/or
The user who propagates malice link is screened out.
According to five kinds of possible arbitrary implementations of the first to the of first aspect, first aspect, in the 6th kind of possible implementation, from described user's group, choose after user builds described social monitoring subnet, also comprise:
According to the event of newly collecting and participate in the user profile of described event, upgrade the user of described social monitoring subnet.
Second aspect, provides a kind of social monitoring subnet device, comprising:
Acquisition module, for obtaining sample event from social networks event;
Screening module, obtains user's group for the user who participates in described sample event is screened, and described user's group comprises N user, and described N is positive integer;
Subnet model construction module, for being more than or equal to predeterminable event coverage N according to the event coverage degree that builds social monitoring subnet e, and the event monitoring probability of described social monitoring subnet is more than or equal to predeterminable event monitoring probability P ebuild subnet Selection Model, described subnet Selection Model builds for determining the number of users that described social monitoring subnet should be chosen from described user's group, wherein, described event coverage degree is the number of users that participates in same event, and described event monitoring probability calculates according to event monitoring new probability formula;
Subnet is chosen module, for the described number of users of determining according to described subnet Selection Model, chooses user and build described social monitoring subnet from described user's group.
In the possible implementation of the first of second aspect, described subnet model construction module, comprising:
Processing unit, participates in probability P for the event of calculating the each user of described user's group i, according to described P icalculate described event monitoring probability P with following described event monitoring new probability formula (1) r(x 1, x 2... x i..., x n):
P r ( x 1 , x 2 , · · · , x i , · · · , x N ) = Π i = 1 N P i x i * ( 1 - P i ) ( 1 - x i ) x 1 + x 2 + · · · + x i + · · · + x N ≤ N - - - ( 1 )
Wherein, described P ifor described user i participates in the ratio of described sample event number and described sample total number of events, x irepresent whether user i participates in the propagation of described sample event, and participation is 1, and not participating in is 0, i ∈ [1, N], r ∈ [1, N];
Described subnet Selection Model builds by following formula (2):
Figure BDA0000483359740000052
Determining unit, for obtaining according to formula (1) and (2) the number of users n that following formula (3) determines that the described social monitoring subnet of structure should be chosen from described user's group:
Σ x 1 + x 2 + . . x i + . . . + x N ≥ N e x 1 + x 2 + . . x i + . . . + x N ≤ N ( Π i = 1 N P i x i * ( 1 - P i ) ( 1 - x i ) ) ≥ P e - - - ( 3 )
The implementation possible according to the first of second aspect, in the possible implementation of the second of second aspect, described subnet is chosen module, comprising:
Arrangement units, for the each user's of described user's group event is participated in to probability P i according to arranging from big to small, the node sequence after sequence is M 1..., M j..., M n;
Choose unit, the probability that participates in described sample event for k node from j node got is P m(k, j), described P m(k, j) drawn by following formula (4) recursion:
P m ( k , j ) = 1 , k = 0 , j = 0 0 , k > j ( 1 - P j ) * P m ( k , j - 1 ) + P j * P m ( k - 1 , j - 1 ) , k ≤ j , j ∈ [ 1 , N ] , - - - ( 4 )
Wherein initial value is, P m ( 0,0 ) = 1 , P m ( 0,1 ) = 1 - P 1 , · · · , P m ( 0 , j ) = ( 1 - P 1 ) * · · · * ( 1 - P j ) , P m ( k , k - 1 ) = P m ( k , k - 2 ) = · · · = P m ( k , 0 ) = 0 ;
Judge the described P that recursion obtains each time mwhether (k, j) and described k meet following formula (5):
k ≥ n P m ( k , j ) ≥ P e - - - ( 5 )
If meet, choose node M from described user's group 1..., M jcorresponding user is as building the required user of described social monitoring subnet;
If do not meet, continue according to described formula (4) recursion until described P mtill (k, j) and described k meet described formula (5).
According to the first of second aspect, second aspect to the possible arbitrary implementation of the second, in the third possible implementation of second aspect, described acquisition module specifically for: choosing and participating in the event that the number of users of event forwarding exceedes default number of users is described social networks event;
According to forwarding quantity and the event type of described social networks event, from described social networks event, obtain described sample event.
According to the first of second aspect, second aspect to the third possible arbitrary implementation, in the 4th kind of possible implementation of second aspect, described acquisition module also for: in described screening module to participating in before the user of described sample event screens and obtain user's group, obtain the event information that participates in described sample event, described event information comprises the user name that participates in described sample event, user participates in the time of described sample event, and user participates in the relation that described sample event procedure forwards and is forwarded.
According to four kinds of possible arbitrary implementations of the first to the of second aspect, second aspect, in the 5th kind of possible implementation of second aspect, described screening module specifically for: screen out lower than the user of preset value participating in number of times or the bean vermicelli number of event; And/or
Be greater than the default user who forwards number and screen out repeating to forward identical information and hop count; And/or
The user who propagates malice link is screened out.
According to five kinds of possible arbitrary implementations of the first to the of second aspect, second aspect, in the 6th kind of possible implementation of second aspect, also comprise:
Update module, for according to the event of newly collecting and the user profile that participates in described event, upgrades the user of described social monitoring subnet.
Method and device that the social activity monitoring subnet that the embodiment of the present invention provides builds, build by the social activity monitoring subnet enlivening on a small quantity and influential user forms by the method, by the detection of social activity monitoring subnet is obtained to social networks event, not only greatly reduce data volume processing, reduce systematic cost, also realize unit and detected in real time, and can remove much noise information, improved the accuracy rate of event detection.
Accompanying drawing explanation
In order to be illustrated more clearly in the technical scheme in the embodiment of the present invention, below the accompanying drawing of required use during embodiment is described is briefly described, apparently, accompanying drawing in the following describes is some embodiments of the present invention, for those of ordinary skills, do not paying under the prerequisite of creative work, can also obtain according to these accompanying drawings other accompanying drawing.
Fig. 1 is the schematic flow sheet of the social monitoring of the present invention subnet construction method embodiment mono-;
Fig. 2 is the schematic flow sheet of the social monitoring of the present invention subnet construction method embodiment bis-;
Fig. 3 is the structural representation of the social monitoring of the present invention subnet device embodiment mono-;
Fig. 4 is the structural representation of the social monitoring of the present invention subnet device embodiment bis-.
Embodiment
For making object, technical scheme and the advantage of the embodiment of the present invention clearer, below in conjunction with the accompanying drawing in the embodiment of the present invention, technical scheme in the embodiment of the present invention is clearly and completely described, obviously, described embodiment is the present invention's part embodiment, rather than whole embodiment.Based on the embodiment in the present invention, those of ordinary skills, not making the every other embodiment obtaining under creative work prerequisite, belong to the scope of protection of the invention.
Fig. 1 is the schematic flow sheet of the social monitoring of the present invention subnet construction method embodiment mono-.As shown in Figure 1, the method for the present embodiment comprises:
Step 101, from social networks event, obtain sample event.
Concrete: choosing the event that the number of users that participates in event forwarding exceedes default number of users is social networks event, then according to number and the event type of the forwarding quantity of selected social networks event, from social networks event, choose a sample event, wherein default number of users and event forwarding quantity number specifically can be according to setting in practical application, for example social networks event repeating quantity can be reached to the sample event that is chosen for of 100,000, can be self-defined for event type, it can be for example people's livelihood event, disaster event, ruling by law event, Official corruption's event, Culture Events, international event etc., do not limited forwarding quantity and event type in the present embodiment.
Step 102, screen and obtain user's group participating in the user of described sample event, described user's group comprises N user, and described N is positive integer.
Concrete: before step 102, also comprise: obtain the event information that participates in described sample event, described event information can be for participating in the user name of described sample event, user participates in the temporal information of described sample event, the relation that user participates in described sample event procedure repeating and is forwarded, can also be other information, the present embodiment is not limited, when acquiring this event information, then according to the screening conditions of setting, the user who participates in sample event is screened, concrete screening can be for screening out number of times or the bean vermicelli number of the event of participation lower than the user of preset value, , it is less that some user in sample event participates in the number of times of event in social networks, lower than preset value, or the bean vermicelli number of some user in sample event is less, lower than preset value, now, those users are screened out, also can be greater than the default user who forwards number and screen out repeating to forward identical information and hop count, the user who propagates malice link can also be screened out, can also be according to practical application, adopt other screening technique, in the present embodiment, do not limited, after above-mentioned screening conditions screening, can effectively remove the participating user less with sample event correlation, the user who meets the most at last screening conditions forms user's group, this user's group comprises N user.
Step 103, be greater than N according to the event coverage degree that builds social monitoring subnet e, and the event monitoring probability of described social monitoring subnet is more than or equal to P e, build subnet Selection Model.
In the present embodiment, in the time obtaining user and organize, need to determine that from this user's group, choosing how many users builds social monitoring subnet, be not less than P so that the social activity building monitoring subnet event coverage degree is more than or equal to the event monitoring probability of the social activity monitoring subnet of Ne and structure e, wherein, event coverage degree is the number of users that participates in same event, N in this enforcement evalue can be estimated according to the average event coverage degree of event, N evalue be generally greater than 5 better, in the social activity monitoring subnet building, at least to have 5 above users to participate in the propagation of same topic, this topic just can be detected as event, P ecan set according to practical application, be greater than N according to the event coverage degree of the described social monitoring subnet building e, and the event monitoring probability of described social monitoring subnet is more than or equal to P e, building subnet Selection Model, this subnet Selection Model can be determined and at least will from user's group, choose how many users, and accuracy rate when guarantee system detects social activity monitoring subnet exceedes P e.
Step 104, the described number of users of determining according to described subnet Selection Model are chosen user and are built described social monitoring subnet from described user's group.
In the present embodiment, when determine the number of users that should choose from described user's group according to subnet Selection Model, can the computing method based on dynamic programming choose the required user of structure social monitoring subnet, wherein, those skilled in the art are clear, dynamic programming be a kind of in mathematics and computer science for solving the method for the optimization problem that comprises overlapping subproblem, its basic thought is, be similar subproblem by former PROBLEM DECOMPOSITION, in the process solving, obtain the solution of former problem by recursive resolve subproblem, from user's group, choose the user who meets the demands according to computing method, those users form the node of social monitoring subnet, in the time that system detects network event, the user node that only need monitor in subnet social activity is monitored, just can obtain some social networks focus incidents.
In the present embodiment, build by the social activity monitoring subnet enlivening on a small quantity and influential user forms by the method based on probability, by the detection of social activity monitoring subnet is obtained to social networks focus incident, not only greatly reduce data volume processing, reduce systematic cost, also realize unit and detected in real time, and can remove much noise information, improved the accuracy rate of event detection.
Fig. 2 is the schematic flow sheet of the social monitoring of the present invention subnet construction method embodiment bis-, and on the basis of above-described embodiment, in the present embodiment, as shown in Figure 2, the method in the present embodiment comprises:
Step 201, from social networks event, obtain sample event.
Step 202, screen and obtain user's group participating in the user of described sample event.
Step 203, calculate the P of each user in described user's group i, according to described P icalculate described event monitoring probability P with following described event monitoring new probability formula r(x 1, x 2... x i..., x n).
In the present embodiment, calculate each user's of described user's group P i, this P icomputing method participate in the ratio of sample event number and sample total number of events for user i, according to the P calculating icalculate P with event monitoring new probability formula (1) r(x 1, x 2... x i..., x n).
P r ( x 1 , x 2 , · · · , x i , · · · , x N ) = Π i = 1 N P i x i * ( 1 - P i ) ( 1 - x i ) x 1 + x 2 + · · · + x i + · · · + x N ≤ N - - - ( 1 )
Wherein, x irepresent whether user i participates in the propagation of described sample event, and participation is 1, and not participating in is 0, i ∈ [1, N], r ∈ [1, N], for instance, if i=5, wherein x 1=1, x 2=1, x 3=0, x 4=1, x 5=1, user i=3 does not participate in the propagation of sample event, now, and event monitoring probability P r(x 1, x 2, x 3, x 4, x 5)=P 1* P 2* (1-P 3) * P 4* P 5,, when choose 5 users from user's group time, event coverage degree is 4, event detection probability is P r(x 1, x 2, x 3, x 4, x 5).
Step 204, structure subnet Selection Model.
In the present embodiment, be more than or equal to N because build the event coverage degree of described social monitoring subnet e, and the event monitoring probability of described social monitoring subnet is more than or equal to P eso,, suppose to choose i=n from user's group n≤N, and event coverage degree
Figure BDA0000483359740000092
p n(x 1, x 2... x n)>=Pe, the subnet Selection Model now obtaining as shown in Equation (2):
Figure BDA0000483359740000101
obtain according to formula (1) and (2): Σ x 1 + x 2 + . . x i + . . . + x N ≥ N e x 1 + x 2 + . . x i + . . . + x N ≤ N P r ( x 1 , x 2 , · · · x i , · · · , x N ) ≥ P e , Formula (1) is brought into, and obtaining final subnet Selection Model is shown in formula (3):
Σ x 1 + x 2 + . . x i + . . . + x N ≥ N e x 1 + x 2 + . . x i + . . . + x N ≤ N ( Π i = 1 N P i x i * ( 1 - P i ) ( 1 - x i ) ) ≥ P e - - - ( 3 )
This subnet Selection Model, the problem of solution is: at least will select how many users, the event coverage degree of the social activity monitoring subnet that guarantee builds is greater than N e, and event detection probability exceedes P e, determine and build the number of users n that described social monitoring subnet should at least be chosen from described user's group according to subnet Selection Model, so in the present invention, the social activity monitoring subnet providing is the social networks event detection subnet based on probability.
Step 205, according to subnet Selection Model, calculate and build the required number of users of social monitoring subnet.
In the present embodiment, according to the subnet Selection Model shown in formula (3), calculate n value, need to from user's group, at least choose n user and build social monitoring subnet.
Step 206, from described user's group, choose corresponding user and build described social monitoring subnet.
In the present embodiment, computing method based on dynamic programming are organized and N user, are chosen at least n user and build social monitoring subnet from user, in the present embodiment, the computing method of the fast selecting user based on dynamic programming are specially, by the P of each user in described user's group iaccording to sorting from big to small, the node sequence after sequence is: M 1..., M j..., M n, use P m(k, j) represents from j node, to get a front k node and participate in the probability of described sample event, wherein P m(k, j) calculated by following formula (4) recursion:
P m ( k , j ) = 1 , k = 0 , j = 0 0 , k > j ( 1 - P j ) * P m ( k , j - 1 ) + P j * P m ( k - 1 , j - 1 ) , k ≤ j , j ∈ [ 1 , N ] , - - - ( 4 )
Wherein initial value: P m ( 0,0 ) = 1 , P m ( 0,1 ) = 1 - P 1 , · · · , P m ( 0 , j ) = ( 1 - P 1 ) * · · · * ( 1 - P j ) , P m ( k , k - 1 ) = P m ( k , k - 2 ) = · · · = P m ( k , 0 ) = 0 ;
Recursion calculates P each time m(k, j), and judge P mwhether (k, j) and the k value of getting meet following formula (5):
k ≥ n P m ( k , j ) ≥ P e - - - ( 5 )
When meet shown in formula (5) condition time, choose M 1, P, M jcorresponding user can build social detection sub-network, if do not meet, to k and/or j assignment again, calculates P according to described formula (4) recursion m(k, j) is until described P mtill (k, j) and described k meet described formula (5).
The event that step 207, basis are newly collected and the user profile that participates in described event, upgrade the described social user who monitors subnet.
In the present embodiment, at set intervals, the user profile of the event of newly collecting and the event of participation is joined in sample database, recalculate the user who chooses social monitoring subnet, antithetical phrase network users upgrades.
In the present embodiment, build by the social activity monitoring subnet enlivening on a small quantity and influential user forms by the method, by the detection of social activity monitoring subnet is obtained to social networks focus incident, not only greatly reduce data volume processing, reduce systematic cost, also realize unit and detected in real time, and can remove much noise information, improved the accuracy rate of event detection.
Fig. 3 is the structural representation of the social monitoring of the present invention subnet device embodiment mono-, as shown in Figure 3, the social activity monitoring subnet device 30 that the present embodiment provides comprises: acquisition module 301, screening module 302, subnet model construction module 303, subnet are chosen module 304.
Wherein, acquisition module 301, for obtaining sample event from social networks event;
Screening module 302, obtains user's group for the user who participates in described sample event is screened, and described user's group comprises N user, and described N is positive integer;
Subnet model construction module 303, for being more than or equal to predeterminable event coverage N according to the event coverage degree that builds described social monitoring subnet e, and the event monitoring probability of described social monitoring subnet is more than or equal to predeterminable event monitoring probability P ebuild subnet Selection Model, described subnet Selection Model builds for determining the number of users that described social monitoring subnet should be chosen from described user's group, wherein, described event coverage degree is the number of users that participates in same event, and described event monitoring probability calculates according to event monitoring new probability formula;
Subnet is chosen module 304, for the described number of users of determining according to described subnet Selection Model, chooses user and build described social monitoring subnet from described user's group.
The equipment of above-described embodiment, for the technical scheme of embodiment of the method one shown in execution graph 1, it realizes principle and technique effect is similar, repeats no more herein.
In the present embodiment, by building by the social activity monitoring subnet enlivening on a small quantity and influential user forms, and the detection of social activity monitoring subnet is obtained to social networks focus incident, not only greatly reduce data volume processing, reduce systematic cost, also realize unit and detected in real time, and can remove much noise information, improved the accuracy rate of event detection.
Further, on the basis of the present embodiment, subnet model construction module 303 also comprises:
Processing unit 3031, participates in probability P for the event of calculating the each user of described user's group i, according to described P icalculate described event monitoring probability P with following described event monitoring new probability formula (1) r(x 1, x 2... x i..., x n):
P r ( x 1 , x 2 , · · · , x i , · · · , x N ) = Π i = 1 N P i x i * ( 1 - P i ) ( 1 - x i ) x 1 + x 2 + · · · + x i + · · · + x N ≤ N - - - ( 1 )
Wherein, described P ifor described user i participates in the ratio of described sample event number and described sample total number of events, x irepresent whether user i participates in the propagation of described sample event, and participation is 1, and not participating in is 0, i ∈ [1, N], r ∈ [1, N];
Described subnet Selection Model builds by following formula (2):
Figure BDA0000483359740000122
Determining unit 3032, for obtaining according to formula (1) and (2) the number of users n that following formula (3) determines that the described social monitoring subnet of structure should be chosen from described user's group:
Σ x 1 + x 2 + . . x i + . . . + x N ≥ N e x 1 + x 2 + . . x i + . . . + x N ≤ N ( Π i = 1 N P i x i * ( 1 - P i ) ( 1 - x i ) ) ≥ P e - - - ( 3 )
Further, on the basis of the present embodiment, described subnet is chosen module 304, comprising:
Arrangement units 3041, for participating in the each user's of described user's group event in probability P iaccording to arranging from big to small, the node sequence after sequence is M 1..., M j..., M n;
Choose unit 3042, the probability that participates in described sample event for k node from j node got is P m(k, j), described P m(k, j) drawn by following formula (4) recursion:
P m ( k , j ) = 1 , k = 0 , j = 0 0 , k > j ( 1 - P j ) * P m ( k , j - 1 ) + P j * P m ( k - 1 , j - 1 ) , k ≤ j , j ∈ [ 1 , N ] , - - - ( 4 )
Wherein initial value is, P m ( 0,0 ) = 1 , P m ( 0,1 ) = 1 - P 1 , · · · , P m ( 0 , j ) = ( 1 - P 1 ) * · · · * ( 1 - P j ) , P m ( k , k - 1 ) = P m ( k , k - 2 ) = · · · = P m ( k , 0 ) = 0 ;
Judge the described P that recursion obtains each time mwhether (k, j) and described k meet following formula (5):
k ≥ n P m ( k , j ) ≥ P e - - - ( 5 )
If meet, choose node M from described user's group 1..., M jcorresponding user is as building the required user of described social monitoring subnet;
If do not meet, continue according to described formula (4) recursion until described P mtill (k, j) and described k meet described formula (5).
Further, on the basis of the present embodiment, described acquisition module 301 specifically for: choosing and participating in the event that the number of users of event forwarding exceedes default number of users is described social networks event;
According to forwarding quantity and the event type of described social networks event, from described social networks event, obtain described sample event.
Further, on the basis of the present embodiment, described acquisition module 301 also for: in described screening module to participating in before the user of described sample event screens and obtain user's group, obtain the event information that participates in described sample event, described event information comprises the user name that participates in described sample event, user participates in the time of described sample event, and user participates in the relation that described sample event procedure forwards and is forwarded.
Further, on the basis of the present embodiment, described screening module 302 specifically for: screen out lower than the user of preset value participating in number of times or the bean vermicelli number of event; And/or
Be greater than the default user who forwards number and screen out repeating to forward identical information and hop count; And/or
The user who propagates malice link is screened out.
Further, on the basis of the present embodiment, social monitoring subnet device 30 also comprises:
Update module 305, for according to the event of newly collecting and the user profile that participates in described event, upgrades the user of described social monitoring subnet.
The equipment of above-described embodiment, for the technical scheme of embodiment of the method two shown in execution graph 2, it realizes principle and technique effect is similar, repeats no more herein.
In the present embodiment, by building by the social activity monitoring subnet enlivening on a small quantity and influential user forms, and the detection of social activity monitoring subnet is obtained to social networks focus incident, not only greatly reduce data volume processing, reduce systematic cost, also realize unit and detected in real time, and can remove much noise information, improved the accuracy rate of event detection.
Fig. 4 is the structural representation of the social monitoring of the present invention subnet device embodiment bis-, as shown in Figure 4, the social activity monitoring subnet device 40 that the present embodiment provides comprises: processor 401 and storer 402, social monitoring subnet device 40 can also comprise transmitter 403 and receiver 404.Transmitter 403 can be connected with processor 401 with receiver 404.Wherein, transmitter 403 is for sending data or message, receiver 404 is for receiving data or message, instruction is carried out in storer 402 storages, in the time that social activity monitoring subnet device 40 moves, between processor 401 and storer 402, communicate by letter, processor 401 calls the execution instruction in storer 402, for carrying out following operation:
From social networks event, obtain sample event;
Screen and obtain user's group participating in the user of described sample event, described user's group comprises N user, and described N is positive integer;
Be more than or equal to predeterminable event coverage N according to the event coverage degree that builds described social monitoring subnet e, and the event monitoring probability of described social monitoring subnet is more than or equal to predeterminable event monitoring probability P ebuild subnet Selection Model, described subnet Selection Model builds for determining the number of users that described social monitoring subnet should be chosen from described user's group, wherein, described event coverage degree is the number of users that participates in same event, and described event monitoring probability calculates according to event monitoring new probability formula;
The described number of users of determining according to described subnet Selection Model is chosen user and is built described social monitoring subnet from described user's group.
Alternatively, also comprise:
The described event coverage degree according to the described social monitoring subnet of structure is more than or equal to described N e, and the event monitoring probability of described social monitoring subnet is more than or equal to described P e, build subnet Selection Model, comprising:
The event of calculating each user in described user's group participates in probability P i, according to described P icalculate described event monitoring probability P with following described event monitoring new probability formula (1) r(x 1, x 2... x i..., x n):
P r ( x 1 , x 2 , · · · , x i , · · · , x N ) = Π i = 1 N P i x i * ( 1 - P i ) ( 1 - x i ) x 1 + x 2 + · · · + x i + · · · + x N ≤ N - - - ( 1 )
Wherein, described P ifor described user i participates in the ratio of described sample event number and described sample total number of events, x irepresent whether user i participates in the propagation of described sample event, and participation is 1, and not participating in is 0, i ∈ [1, N], r ∈ [1, N];
Described subnet Selection Model builds by following formula (2):
Obtain according to formula (1) and (2) the number of users n that following formula (3) determines that the described social monitoring subnet of structure should be chosen from described user's group:
Σ x 1 + x 2 + . . x i + . . . + x N ≥ N e x 1 + x 2 + . . x i + . . . + x N ≤ N ( Π i = 1 N P i x i * ( 1 - P i ) ( 1 - x i ) ) ≥ P e - - - ( 3 )
Alternatively, from described user's group, choose user and build described social monitoring subnet, comprising:
Adopt the method for dynamic programming from described user's group, to choose the required user of the described social monitoring subnet of structure, be specially:
The event of each user in described user's group is participated in to probability P iaccording to arranging from big to small, the node sequence after sequence is M 1..., M j..., M n;
From j node, getting the probability that a front k node participates in described sample event is P m(k, j), described P m(k, j) drawn by following formula (4) recursion:
P m ( k , j ) = 1 , k = 0 , j = 0 0 , k > j ( 1 - P j ) * P m ( k , j - 1 ) + P j * P m ( k - 1 , j - 1 ) , k ≤ j , j ∈ [ 1 , N ] , - - - ( 4 )
Wherein initial value is, P m ( 0,0 ) = 1 , P m ( 0,1 ) = 1 - P 1 , · · · , P m ( 0 , j ) = ( 1 - P 1 ) * · · · * ( 1 - P j ) , P m ( k , k - 1 ) = P m ( k , k - 2 ) = · · · = P m ( k , 0 ) = 0 ;
Judge the described P that recursion obtains each time mwhether (k, j) and described k meet following formula (5):
k ≥ n P m ( k , j ) ≥ P e - - - ( 5 )
If meet, choose node M from described user's group 1..., M jcorresponding user is as building the required user of described social monitoring subnet;
If do not meet, continue according to described formula (4) recursion until described P mtill (k, j) and described k meet described formula (5).
Alternatively, the described sample event of obtaining from social networks event, comprising:
Choosing and participating in the event that the number of users of event forwarding exceedes default number of users is described social networks event;
According to forwarding quantity and the event type of described social networks event, from described social networks event, obtain described sample event.
Alternatively, describedly, also comprise before the user of described sample event screens and obtain user's group participating in:
Obtain the event information that participates in described sample event, described event information comprises: participate in the user name of described sample event, user participates in the time of described sample event, the relation that user participates in described sample event procedure repeating and is forwarded.
Alternatively, describedly screen and obtain user's group participating in the user of described sample event, comprising:
Number of times or the bean vermicelli number of the event of participation are screened out lower than the user of preset value; And/or
Be greater than the default user who forwards number and screen out repeating to forward identical information and hop count; And/or
The user who propagates malice link is screened out.
Alternatively, described from described user's group, choose build the required user of described social monitoring subnet after, also comprise:
According to the event of newly collecting and participate in the user profile of described event, upgrade the user of described social monitoring subnet.
The social activity monitoring subnet device of the present embodiment, the technical scheme that can provide for carrying out the inventive method embodiment mono-or two, it realizes principle and technique effect is similar, repeats no more herein.
One of ordinary skill in the art will appreciate that: all or part of step that realizes above-mentioned each embodiment of the method can complete by the relevant hardware of programmed instruction.Aforesaid program can be stored in a computer read/write memory medium.This program, in the time carrying out, is carried out the step that comprises above-mentioned each embodiment of the method; And aforesaid storage medium comprises: various media that can be program code stored such as ROM, RAM, magnetic disc or CDs.
Finally it should be noted that: above each embodiment, only in order to technical scheme of the present invention to be described, is not intended to limit; Although the present invention is had been described in detail with reference to aforementioned each embodiment, those of ordinary skill in the art is to be understood that: its technical scheme that still can record aforementioned each embodiment is modified, or some or all of technical characterictic is wherein equal to replacement; And these modifications or replacement do not make the essence of appropriate technical solution depart from the scope of various embodiments of the present invention technical scheme.

Claims (14)

1. the method that social monitoring subnet builds, is characterized in that, the method comprises:
From social networks event, obtain sample event;
Screen and obtain user's group participating in the user of described sample event, described user's group comprises N user, and described N is positive integer;
Be more than or equal to predeterminable event coverage N according to the event coverage degree that builds social monitoring subnet e, and the event monitoring probability of described social monitoring subnet is more than or equal to predeterminable event monitoring probability P ebuild subnet Selection Model, described subnet Selection Model builds for determining the number of users that described social monitoring subnet should be chosen from described user's group, wherein, described event coverage degree is the number of users that participates in same event, and described event monitoring probability is to calculate according to event monitoring new probability formula;
The described number of users of determining according to described subnet Selection Model is chosen user and is built described social monitoring subnet from described user's group.
2. method according to claim 1, is characterized in that, the described event coverage degree according to the described social monitoring subnet of structure is more than or equal to described N e, and the event monitoring probability of described social monitoring subnet is more than or equal to described P e, build subnet Selection Model, comprising:
The event of calculating each user in described user's group participates in probability P i, according to described P icalculate described event monitoring probability P with following described event monitoring new probability formula (1) r(x 1, x 2... x i..., x n):
P r ( x 1 , x 2 , · · · , x i , · · · , x N ) = Π i = 1 N P i x i * ( 1 - P i ) ( 1 - x i ) x 1 + x 2 + · · · + x i + · · · + x N ≤ N - - - ( 1 )
Wherein, described P ifor described user i participates in the ratio of described sample event number and described sample total number of events, x irepresent whether user i participates in the propagation of described sample event, and participation is 1, and not participating in is 0, i ∈ [1, N], r ∈ [1, N];
Described subnet Selection Model builds by following formula (2):
Figure FDA0000483359730000012
Obtain according to formula (1) and (2) the number of users n that following formula (3) determines that the described social monitoring subnet of structure should be chosen from described user's group:
Σ x 1 + x 2 + . . x i + . . . + x N ≥ N e x 1 + x 2 + . . x i + . . . + x N ≤ N ( Π i = 1 N P i x i * ( 1 - P i ) ( 1 - x i ) ) ≥ P e - - - ( 3 )
3. method according to claim 2, is characterized in that, describedly from described user's group, chooses user and builds described social monitoring subnet, comprising:
Adopt the method for dynamic programming from described user's group, to choose the required user of the described social monitoring subnet of structure, be specially:
The event of each user in described user's group is participated in to probability P iaccording to arranging from big to small, the node sequence after sequence is M 1..., M j..., M n;
From j node, getting the probability that a front k node participates in described sample event is P m(k, j), described P m(k, j) drawn by following formula (4) recursion:
P m ( k , j ) = 1 , k = 0 , j = 0 0 , k > j ( 1 - P j ) * P m ( k , j - 1 ) + P j * P m ( k - 1 , j - 1 ) , k ≤ j , j ∈ [ 1 , N ] , - - - ( 4 )
Wherein initial value is, P m ( 0,0 ) = 1 , P m ( 0,1 ) = 1 - P 1 , · · · , P m ( 0 , j ) = ( 1 - P 1 ) * · · · * ( 1 - P j ) , P m ( k , k - 1 ) = P m ( k , k - 2 ) = · · · = P m ( k , 0 ) = 0 ;
Judge the described P that recursion obtains each time mwhether (k, j) and described k meet following formula (5):
k ≥ n P m ( k , j ) ≥ P e - - - ( 5 )
If meet, choose node M from described user's group 1..., M jcorresponding user is as building the required user of described social monitoring subnet;
If do not meet, continue according to described formula (4) recursion until described P mtill (k, j) and described k meet described formula (5).
4. according to the arbitrary described method of claim 1-3, it is characterized in that, the described sample event of obtaining from social networks event, comprising:
Choosing and participating in the event that the number of users of event forwarding exceedes default number of users is described social networks event;
According to forwarding quantity and the event type of described social networks event, from described social networks event, obtain described sample event.
5. according to the arbitrary described method of claim 1-4, it is characterized in that, describedly, also comprise before the user of described sample event screens and obtain user's group participating in:
Obtain the event information that participates in described sample event, described event information comprises: participate in the user name of described sample event, user participates in the time of described sample event, the relation that user participates in described sample event procedure repeating and is forwarded.
6. according to the arbitrary described method of claim 1-5, it is characterized in that, describedly screen and obtain user's group participating in the user of described sample event, comprising:
Number of times or the bean vermicelli number of the event of participation are screened out lower than the user of preset value; And/or
Be greater than the default user who forwards number and screen out repeating to forward identical information and hop count; And/or
The user who propagates malice link is screened out.
7. according to the arbitrary described method of claim 1-6, it is characterized in that, described choosing from described user's group after user builds described social monitoring subnet, also comprises:
According to the event of newly collecting and participate in the user profile of described event, upgrade the user of described social monitoring subnet.
8. a social monitoring subnet device, is characterized in that, described device comprises:
Acquisition module, for obtaining sample event from social networks event;
Screening module, obtains user's group for the user who participates in described sample event is screened, and described user's group comprises N user, and described N is positive integer;
Subnet model construction module, for being more than or equal to predeterminable event coverage N according to the event coverage degree that builds social monitoring subnet e, and the event monitoring probability of described social monitoring subnet is more than or equal to predeterminable event monitoring probability P ebuild subnet Selection Model, described subnet Selection Model builds for determining the number of users that described social monitoring subnet should be chosen from described user's group, wherein, described event coverage degree is the number of users that participates in same event, and described event monitoring probability calculates according to event monitoring new probability formula;
Subnet is chosen module, for the described number of users of determining according to described subnet Selection Model, describedly from described user's group, chooses user and builds described social monitoring subnet.
9. device according to claim 8, is characterized in that, described subnet model construction module, comprising:
Processing unit, participates in probability P for the event of calculating the each user of described user's group i, according to described P icalculate described event monitoring probability P with following described event monitoring new probability formula (1) r(x 1, x 2... x i..., x n):
P r ( x 1 , x 2 , · · · , x i , · · · , x N ) = Π i = 1 N P i x i * ( 1 - P i ) ( 1 - x i ) x 1 + x 2 + · · · + x i + · · · + x N ≤ N - - - ( 1 )
Wherein, described P ifor described user i participates in the ratio of described sample event number and described sample total number of events, x irepresent whether user i participates in the propagation of described sample event, and participation is 1, and not participating in is 0, i ∈ [1, N], r ∈ [1, N];
Described subnet Selection Model builds by following formula (2):
Figure FDA0000483359730000041
Determining unit, for obtaining according to formula (1) and (2) the number of users n that following formula (3) determines that the described social monitoring subnet of structure should be chosen from described user's group:
Σ x 1 + x 2 + . . x i + . . . + x N ≥ N e x 1 + x 2 + . . x i + . . . + x N ≤ N ( Π i = 1 N P i x i * ( 1 - P i ) ( 1 - x i ) ) ≥ P e - - - ( 3 )
10. device according to claim 9, is characterized in that, described subnet is chosen module, comprising:
Arrangement units, for participating in the each user's of described user's group event in probability P iaccording to arranging from big to small, the node sequence after sequence is M 1..., M j..., M n;
Choose unit, the probability that participates in described sample event for k node from j node got is P m(k, j), described P m(k, j) drawn by following formula (4) recursion:
P m ( k , j ) = 1 , k = 0 , j = 0 0 , k > j ( 1 - P j ) * P m ( k , j - 1 ) + P j * P m ( k - 1 , j - 1 ) , k ≤ j , j ∈ [ 1 , N ] , - - - ( 4 )
Wherein initial value is, P m ( 0,0 ) = 1 , P m ( 0,1 ) = 1 - P 1 , · · · , P m ( 0 , j ) = ( 1 - P 1 ) * · · · * ( 1 - P j ) , P m ( k , k - 1 ) = P m ( k , k - 2 ) = · · · = P m ( k , 0 ) = 0 ;
Judge the described P that recursion obtains each time mwhether (k, j) and described k meet following formula (5):
k ≥ n P m ( k , j ) ≥ P e - - - ( 5 )
If meet, choose node M from described user's group 1..., M jcorresponding user is as building the required user of described social monitoring subnet;
If do not meet, continue according to described formula (4) recursion until described P mtill (k, j) and described k meet described formula (5).
11. according to Claim 8-10 arbitrary described devices, is characterized in that, described acquisition module specifically for: choosing the event that the number of users that participates in event forwarding exceedes default number of users is described social networks event;
According to forwarding quantity and the event type of described social networks event, from described social networks event, obtain described sample event.
12. according to Claim 8-11 arbitrary described devices, it is characterized in that, described acquisition module also for: in described screening module to participating in before the user of described sample event screens and obtain user's group, obtain the event information that participates in described sample event, described event information comprises the user name that participates in described sample event, user participates in the time of described sample event, and user participates in the relation that described sample event procedure forwards and is forwarded.
13. according to Claim 8-12 arbitrary described devices, is characterized in that, described screening module specifically for: number of times or the bean vermicelli number of the event of participation are screened out lower than the user of preset value; And/or
Be greater than the default user who forwards number and screen out repeating to forward identical information and hop count; And/or
The user who propagates malice link is screened out.
14. according to Claim 8-13 arbitrary described devices, is characterized in that, also comprise:
Update module, for according to the event of newly collecting and the user profile that participates in described event, upgrades the user of described social monitoring subnet.
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