CN109086341A - The focus incident temperature measure of application group's intelligence - Google Patents
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Abstract
Present invention discloses a kind of focus incident temperature measure of application group's intelligence, includes the following steps: S1, describes the interaction scenario between user using interaction figure；S2, interaction figure is divided into continuous timeslice, in each timeslice, calculates separately node degree distribution, point degree centrality and gather coefficient；S3, in each timeslice, according in the timeslice node degree distribution, point degree centrality and gather coefficient, COMPREHENSIVE CALCULATING goes out the partially interior focus incident temperature of current event.The interaction figure of one of the important tool that the present invention is studied using complex network models the interbehavior between user, the global feature of interaction figure is described using the Measure Indexes of complex network, each stage in focus incident development process is embodied, it matches with the development process of focus incident, ensure that the accuracy and validity of final measurement results.
Description
Technical field
The present invention relates to a kind of event temperature measure, in particular to being made in a kind of social big data analysis
The focus incident temperature measure of application group's intelligence, belongs to artificial intelligence and the field of data mining.
Background technique
With internet, information-based rapid development, social networks gradually penetrates into daily life and plays the part of
It drills increasingly important role, become the platform that people share, interact.In social networks, frequent User Activity is expedited the emergence of
Social big data out, wherein containing public view, viewpoint, emotion etc., also just because of the presence of these factors, to make
Social networks becomes a reality the mapping in the world.
Focus incident embodies the focus of current masses as the pith in social big data.Currently, can lead to
The method for crossing machine learning carries out data processing to social big data, and then excavates focus incident therein, and predict its hair
Exhibition trend, this has great importance to public sentiment monitoring, user interest modeling etc..And no matter the measurement of focus incident temperature is in heat
Point event is excavated or is all a crucial index in focus incident prediction.
In the prior art, the mode for generalling use statistics keyword frequency to the measurement of focus incident temperature carries out.It is logical
It crosses natural language processing technique and counts keyword relevant to focus incident or burst word, and calculate the frequency of its appearance, in turn
The sequence to focus incident is realized according to the size of frequency values.This method has been widely used for search, event index, hot spot
The fields such as topic.However, this method is during use, it is faced with natural language ambiguousness, ambiguity, ambiguity etc. and asks
The puzzlement of topic.
User is the main body during focus incident occurs, develops, disappearing.For the higher event of temperature, can attract more
More users participates in；For the lower minority's event of temperature, the participation of user is lower.Formation is interacted between user and user
Swarm intelligence pushes the development of focus incident, therefore, substantially, the measurement of the temperature of focus incident and the characterization of swarm intelligence
The trend of strong correlation is presented.
In conclusion how to propose a kind of completely new event temperature measure, swarm intelligence is fully utilized, it is accurate fast
The temperature measurement for completing the hot spot time fastly, also just becomes current those skilled in that art institute urgent problem to be solved.
Summary of the invention
In view of the prior art, there are drawbacks described above, and the purpose of the present invention is to propose to used in a kind of social big data analysis
Application group's intelligence focus incident temperature measure.
Specifically, including the following steps:
S1, description step, the interaction scenario between user is described using interaction figure；
Interaction figure is divided into continuous timeslice, in each timeslice, calculates separately node by S2, pre-treatment step
It spends distribution, point degree centrality and gathers coefficient；
S3, comprehensive measurement step, in each timeslice, according to the node degree distribution in the timeslice, point degree centrality
And gather coefficient, COMPREHENSIVE CALCULATING goes out the partially interior focus incident temperature of current event.
Preferably, each node in the interaction figure corresponds to a user, and each edge in the interaction figure is right
Using the interactive relation between family.
Preferably, each node all has nodal community, the nodal community include age of user, user's occupation with
And user location.
Preferably, the interactive relation includes forwarding between user, comments on and thumb up, and every described to belong to when all having
Property, the side attribute includes frequency of interaction.
Preferably, pre-treatment step described in S2 includes:
S21, by interaction figure with preset time interval, be divided into m continuous timeslices；
S22, the node degree calculated in each timeslice are distributed D；
S23, point degree centrality DC in each timeslice is calculated；
S24, it calculates in each timeslice and gathers coefficient CL.
Preferably, in each timeslice, the node degree distribution D is distributed in double-log using the timeslice interior nodes degree
Power exponent under coordinate indicates that described degree centrality DC is indicated using the central average value of the timeslice interior nodes point degree,
It is described gather coefficient CL using gather in the timeslice coefficient value indicate.
Preferably, comprehensive measurement step described in S3, comprising:
S31, it obtains in single timeslice and gathers coefficient CL, point degree centrality DC and node degree distribution D；
S32, gather coefficient (x to what is obtained1), node degree be distributed (x2) and point degree centrality (x3) carry out dimensionless
Change processing, handling formula is,
Wherein, xiIt indicates node degree distribution, point degree centrality and gathers coefficient value, xi' indicate to go it is after dimension as a result,
Function max (x) is used to calculate the maximum value in three values；
S33, gather coefficient, node degree and point degree centrality after treatment is compared two-by-two, is compared
Matrix,
Wherein, the element a in comparator matrixijIndicate the importance between two reduced values, the size and importance of value at
Direct ratio, wherein i, j={ 1,2,3 }；
S34, consistency check is carried out to comparator matrix, calculates the average coincident indicator CI immediately of comparator matrix, calculates
Formula is,
Wherein, n indicates element number, λmax(A) maximum value of the characteristic value of comparator matrix A is indicated；
S35, test rating CR being calculated, calculation formula is,
Wherein, the value of RI is constant, and value is different with order and changes；
S36, weight is calculated, the corresponding feature vector of comparator matrix maximum eigenvalue obtains feature after normalized
Vector, described eigenvector are the weight vectors of corresponding index, and expression formula is,
The evaluation index P of S37, COMPREHENSIVE CALCULATING focus incident temperature, calculation formula be,
P=0.6370*CL+0.2583*D+0.1047*DC,
Wherein, coefficient is gathered in CL expression, and D indicates the distribution of node center degree, and DC indicates point degree centrality.
Compared with prior art, advantages of the present invention is mainly reflected in the following aspects:
The interaction figure of one of the important tool that the present invention is studied using complex network carries out the interbehavior between user
Modeling, the global feature of interaction figure is described using the Measure Indexes of complex network, has been embodied in focus incident development process
Each stage matches with the development process of focus incident, ensure that the accuracy and validity of final measurement results.
In addition, the present invention also provides reference for other relevant issues in same domain, can be opened up on this basis
Extension is stretched, and is applied in field in the technical solution of other event temperature measures, has very strong applicability and wide
Application prospect.
In general, the present invention has taken into account the accuracy of efficiency and result in metrics process, and using effect is good, tool
There are very high use and promotional value.
Just attached drawing in conjunction with the embodiments below, the embodiment of the present invention is described in further detail, so that of the invention
Technical solution is more readily understood, grasps.
Detailed description of the invention
Fig. 1 is flow diagram of the invention；
Fig. 2 is the interaction figure and each indicatrix of the social networks of different time on piece；
Fig. 3 is the result schematic diagram using the method for the present invention；
Fig. 4 is the true index of focus incident temperature variation.
Specific embodiment
As shown in FIG. 1 to FIG. 2, present invention discloses application group's intelligence used in a kind of social big data analysis
Focus incident temperature measure.
There is a large amount of User Activities in social networks.Wherein, it is important that one kind be user interbehavior, mainly
Caused by interest, the emotion etc. between user, for example, forwarding, commenting on, thumbing up.Frequent friendship between user and user
Mutually, embody swarm intelligence, correspond to focus incident Emergence and Development, climax, end each stage, with focus incident
Development process is consistent.Therefore, the temperature of use groups intelligence value metric focus incident of the present invention.
Specifically, focus incident temperature measure of the invention, includes the following steps:
S1, description step, the interaction scenario between user is described using interaction figure；
Interaction figure is divided into continuous timeslice, in each timeslice, calculates separately node by S2, pre-treatment step
It spends distribution, point degree centrality and gathers coefficient；
S3, comprehensive measurement step, in each timeslice, according to the node degree distribution in the timeslice, point degree centrality
And gather coefficient, COMPREHENSIVE CALCULATING goes out the partially interior focus incident temperature of current event.
Each node in the interaction figure corresponds to a user, each edge in the interaction figure correspond to user it
Between interactive relation.
Each node all has nodal community, and the nodal community includes age of user, user's occupation, user place
Ground etc..The interactive relation includes forwarding between user, comments on and thumb up, and every side all has side attribute, the side
Attribute includes frequency of interaction.
Obviously, interaction figure is a complex network.In the research of complex network, pass through node degree, point degree centrality, collection
The indexs such as poly- coefficient can describe the global feature of complex network.Wherein, degree distribution refers to the distribution situation of each node degree in figure,
Reflect the association situation of whole network.Point degree centrality refers to that the node degree of a node is meant that more greatly in the degree of this node
Disposition is higher, and the node is more important in a network.Bunching coefficient describes to concentrate pockets of degree between the vertex in a figure
Coefficient, specifically, being degree interconnected between the abutment points of a point.Therefore, the present invention is proposed using finger above
Scale amount interaction figure, and then the temperature of the swarm intelligence of measure user interaction and focus incident.
Specifically, in the present invention, pre-treatment step described in S2 includes:
S21, by interaction figure with preset time interval, be divided into m continuous timeslices, each timeslice
Inside include interaction figure, spend distribution map, point degree centrality figure and gather coefficient figure；
S22, the node degree calculated in each timeslice are distributed D；
S23, point degree centrality DC in each timeslice is calculated；
S24, it calculates in each timeslice and gathers coefficient CL.
It should be noted that the node degree distribution D is distributed in using the timeslice interior nodes degree in each timeslice
Power exponent under log-log coordinate indicates that described degree centrality DC uses the central average value of timeslice interior nodes point degree
Indicate, it is described gather coefficient CL using gather in the timeslice coefficient value indicate.
As shown in Fig. 2, in single timeslice, for node degree, showing a small number of nodes, relatively more active (i.e. degree is big
Interstitial content is less), and most of node is inactive, has been only involved in and has interacted once or several times.The situation is shown absolutely mostly
Several users is free in except focus incident, only Guiding Object of a few users as event, the participation of high degree or
By the discussion of participation focus incident.Power exponent variation tendency is to gradually decrease at any time or constant, is shown more and more
User dramatically participates in the discussion of focus incident.And the power exponent speed that on piece reduces in different times is different,
Continuous time on piece show be reduce speed gradually slow down, show that the increased degree of participation of user gradually slows down.
For degree centrality, point degree centrality shows that more greatly the participation of user's participation focus incident is higher.Hot spot thing
Over time, user issues to increase part about the new content of focus incident, and the user of original publication content is forwarded also to increase
More, then the node of different degrees increases (or constant) with different growth rate or starts to occur, i.e. the participation of user exists
It is continuously increased.
For coefficient is gathered, over time, bunching coefficient first increases and reduces afterwards, i.e., in social networks, user
Discussion trend for hot spot thing is first to concentrate to disperse afterwards.The appearance of one focus incident is often increased along with user
Attention rate and discussion degree；And after an event becomes focus incident, it will attract more users to pay close attention to, so that a heat
The temperature heating of point event；Therewith after a focus incident is adequately discussed, the discussion emphasis of user starts point
It dissipates, from the mainstream event that the focus incident is discussed, to the branch's focus incident discussed in the focus incident, so that clustering situation is opened
Beginning tends to disperse.
Therefore, in order to which the variation tendency for observing three above value in continuous time on piece is all formed for each index
The index series of interaction figure, i.e., node degree series described above, point degree centrality sequence and gather coefficient sequence.
Comprehensive measurement step described in S3, comprising:
S31, it obtains in single timeslice and gathers coefficient, point degree centrality and node degree distribution；
S32, coefficient (x is gathered to the section obtained1), node degree be distributed (x2) and point degree centrality (x3) carry out it is immeasurable
Guiding principleization processing, handling formula is,
Wherein, xiIt indicates node degree distribution, point degree centrality and gathers coefficient value, xi' indicate to go it is after dimension as a result,
Function max (x) is used to calculate the maximum value in three values；；
S33, gather coefficient, node degree and point degree centrality after treatment is compared two-by-two, is compared
Matrix,
Wherein, the element a in comparator matrixijIndicate the importance between two reduced values, value is bigger to illustrate value xiCompare xj
It is more important, wherein i, j={ 1,2,3 }；
S34, consistency check is carried out to comparator matrix, calculates the average coincident indicator CI immediately of comparator matrix, calculates
Formula is,
Wherein, n indicates element number, λmax(A) maximum value of the characteristic value of comparator matrix A is indicated；
S35, test rating CR being calculated, calculation formula is,
Wherein, the value of RI is constant, and value is different with order and changes；
S36, weight is calculated, the corresponding feature vector of comparator matrix maximum eigenvalue obtains feature after normalized
Vector, described eigenvector are the weight vectors of corresponding index, and expression formula is,
The evaluation index P of S37, COMPREHENSIVE CALCULATING focus incident temperature, calculation formula be,
P=0.6370*CL+0.2583*D+0.1047*DC,
Wherein, coefficient is gathered in CL expression, and D indicates the distribution of node center degree, and DC indicates point degree centrality.
Next, in conjunction with a specific embodiment, invention is further explained, using Sina weibo data, using this
Method in invention measures the temperature of focus incident therein.Choose about " Ching Ming Festival " this focus incident is on a left side on April 5
228129 right microblog datas.Using microblog users as node, using the forwarding relationship between user as side, interaction figure is constructed.
Timeslice is divided with 1 hour on the interaction figure of building, is referred in each timeslice using the present invention
Method calculates the temperature of focus incident, as a result as shown in Figure 3.In order to examine the validity of the proposed method of the present invention, for " clear and bright
Baidu's index variation trend of section " focus incident is as shown in Figure 4.Pass through the comparison of two figures, it can be seen that proposed by the invention
Method measurement focus incident temperature on it is unanimous on the whole with the development trend of real event, have validity.
The interaction figure of one of the important tool that the present invention is studied using complex network carries out the interbehavior between user
Modeling, the global feature of interaction figure is described using the Measure Indexes of complex network, has been embodied in focus incident development process
Each stage matches with the development process of focus incident, ensure that the accuracy and validity of final measurement results.
In addition, the present invention also provides reference for other relevant issues in same domain, can be opened up on this basis
Extension is stretched, and is applied in field in the technical solution of other event temperature measures, has very strong applicability and wide
Application prospect.
In general, the present invention has taken into account the accuracy of efficiency and result in metrics process, and using effect is good, tool
There are very high use and promotional value.
It is obvious to a person skilled in the art that invention is not limited to the details of the above exemplary embodiments, Er Qie
In the case where without departing substantially from spirit and essential characteristics of the invention, the present invention can be realized in other specific forms.Therefore, no matter
From the point of view of which point, the present embodiments are to be considered as illustrative and not restrictive, and the scope of the present invention is by appended power
Benefit requires rather than above description limits, it is intended that all by what is fallen within the meaning and scope of the equivalent elements of the claims
Variation is included within the present invention, and any reference signs in the claims should not be construed as limiting the involved claims.
In addition, it should be understood that although this specification is described in terms of embodiments, but not each embodiment is only wrapped
Containing an independent technical solution, this description of the specification is merely for the sake of clarity, and those skilled in the art should
It considers the specification as a whole, the technical solutions in the various embodiments may also be suitably combined, forms those skilled in the art
The other embodiments being understood that.
Claims (7)
1. a kind of focus incident temperature measure of application group's intelligence, which comprises the steps of:
S1, description step, the interaction scenario between user is described using interaction figure；
Interaction figure is divided into continuous timeslice by S2, pre-treatment step, in each timeslice, calculates separately node degree point
Cloth, point degree centrality and gather coefficient；
S3, comprehensive measurement step, in each timeslice, according in the timeslice node degree distribution, point degree centrality and
Gather coefficient, COMPREHENSIVE CALCULATING goes out the partially interior focus incident temperature of current event.
2. the focus incident temperature measure of application group's intelligence according to claim 1, it is characterised in that: the friendship
Each node in mutual figure corresponds to a user, and each edge in the interaction figure corresponds to the interactive relation between user.
3. the focus incident temperature measure of application group's intelligence according to claim 2, it is characterised in that: Mei Gesuo
It states node and all has nodal community, the nodal community includes age of user, user's occupation and user location.
4. the focus incident temperature measure of application group's intelligence according to claim 2, it is characterised in that: the friendship
Mutual relation includes forwarding between user, comments on and thumb up, and every side all has side attribute, and the side attribute includes interaction frequency
Rate.
5. the focus incident temperature measure of application group's intelligence according to claim 1, which is characterized in that described in S2
Pre-treatment step includes:
S21, by interaction figure with preset time interval, be divided into m continuous timeslices；
S22, the node degree calculated in each timeslice are distributed D；
S23, point degree centrality DC in each timeslice is calculated；
S24, it calculates in each timeslice and gathers coefficient CL.
6. the focus incident temperature measure of application group's intelligence according to claim 6, it is characterised in that: each
In timeslice, the node degree distribution D, which is distributed in the power exponent under log-log coordinate using the timeslice interior nodes degree, to be indicated, institute
State a degree centrality DC is indicated using the central average value of the timeslice interior nodes point degree, described when gathering coefficient CL using this
Between the poly- coefficient value of chip integration indicate.
7. the focus incident temperature measure of application group's intelligence according to claim 1, which is characterized in that described in S3
Comprehensive measurement step, comprising:
S31, it obtains in single timeslice and gathers coefficient CL, point degree centrality DC and node degree distribution D；
S32, gather coefficient (x to what is obtained1), node degree be distributed (x2) and point degree centrality (x3) carry out at nondimensionalization
Reason, handling formula is,
Wherein, xiIt indicates node degree distribution, point degree centrality and gathers coefficient value, xi' indicate to go after dimension as a result, function
Max (x) is used to calculate the maximum value in three values；
S33, gather coefficient, node degree and point degree centrality after treatment is compared two-by-two, obtains comparator matrix,
Wherein, the element a in comparator matrixijIndicating the importance between two reduced values, the size of value is directly proportional to importance,
Wherein, i, j={ 1,2,3 }；
S34, consistency check is carried out to comparator matrix, calculates the average coincident indicator CI immediately of comparator matrix, calculation formula
For,
Wherein, n indicates element number, λmax(A) maximum value of the characteristic value of comparator matrix A is indicated；
S35, test rating CR being calculated, calculation formula is,
Wherein, the value of RI is constant, and value is different with order and changes；
S36, calculate weight, the corresponding feature vector of comparator matrix maximum eigenvalue obtained after normalized feature to
Amount, described eigenvector be the weight vectors of correspondence index, and expression formula is,
The evaluation index P of S37, COMPREHENSIVE CALCULATING focus incident temperature, calculation formula be,
P=0.6370*CL+0.2583*D+0.1047*DC,
Wherein, coefficient is gathered in CL expression, and D indicates the distribution of node center degree, and DC indicates point degree centrality.
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