CN107316246A - A kind of method for digging of social networks key user - Google Patents
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Abstract
The present invention relates to a kind of method for digging of social networks key user, a：Carry out calculating the technorati authority for introducing website using Au PageRank algorithms；b：Using 2S PageRank algorithms calculate the probability of user's next step browsing pages；c：With reference to the probability of a technorati authorities drawn and the b browsing pages drawn, when the user is surfed the Net again more see a step, so as to draw Au 2S PageRank algorithms, pass through Au 2S PageRank algorithms and produce ranking fruit.The method for digging of described social networks key user a kind of, Au PageRank algorithms and 2S PageRank algorithms are combined to form into Au 2S PageRank algorithms, classical PageRank algorithms are solved because mean allocation weights are to the influence caused by result, and convergence rate can be accelerated when calculating, ranking results are more accurate.
Description
Technical field
The present invention relates to social network information algorithm field, especially a kind of method for digging of social networks key user.
Background technology
Social networks is a kind of social structure got up by one or more correlates by personal and Canopy structure.
The Internet space produces new social mode for user and provides great feasibility.Nineteen sixty, the concept first of social networks
It is secondary to be proposed in Illinois, US university.Afterwards, first social network sites, i.e. " Six Degrees.com " have been set up.2002
Afterwards, various such as LinkIn etc social network sites are in full flourish, great revolution brought once to the field, greatly
Enrich social networks.Today, social networks is greatly welcome, and it provide the user substantial amounts of media of communication.No matter
It is the addition of newcomer, or new contact is set up between member, whole social networks can be all increased, social network data
Also drastically expanding.
As the development of online community network, business activity also progress into this when these social networks are analyzed
How field, more rapid and better analyze and recognize the key user in social networks, pole is suffered to advertizing, the marketing
Its important effect.Key user is excavated in social networks, hot information instantly can be not only excavated, may also be used for
The prediction propagated Future Information, has particularly important meaning to public sentiment monitoring.
PageRank algorithms be it is a kind of for link analyzed and calculate grade and importance of the webpage in internet
Sort algorithm, the higher grade of a webpage, and importance is more prominent, and the ranking occurred in a search engine will be more forward.
The use of PageRank algorithms is based on two premises, and the hyperlink quantity that a premise points to a webpage is more, shows this
Webpage is more important；What another premise was directed to the page enters that chain quality is different, and the high page of quality can be by linking to other pages
More weights are transmitted in face, so the PageRank that the high page of quality is pointed to is also higher.
Classical PageRank algorithms think that user can have access to whole internet by the link between webpage, but
It is due to have one group in actual internet to connect each other, not to organizing web pages of outer web page interlinkage, therefore
Its PR value is just always inside this web pages, it is impossible to pass, and is referred to as PR value deposited phenomenons.In order to avoid this phenomenon goes out
It is existing, damped coefficient d is introduced, i.e. PageRank formula are：
Damped coefficient d typically takes 0.85.
The advantage of PageRank algorithms is that it belongs to the state algorithm unrelated with inquiry, all PR values can by from
Line computation is obtained, fast response time, and Google searches the success of multi engine and also demonstrates the efficient and rational property of the algorithm.
Strategy and resolving can be seen that at least following drawback of the flow more than：PageRank algorithms ought
Weight average on preceding webpage distributes to its whole links.But the quality value of each webpage in internet is thousand poor
Ten thousand is other, even in the identical page, different links also have different quality values, and they there may be very big excellent
Bad difference.Actually there are many advertisement or annotation information in network, these information are classical equivalent to noise information
PageRank algorithms these information in mean allocation weights have also been assigned to the weights with other link identicals.If one
Noise information link is present in an important page, then it is possible to obtain also more important than normal page link
Ranking, so as to can be impacted to final ranking accuracy.
The content of the invention
The technical problem to be solved in the present invention is：For the shortcoming of classical PageRank algorithms mean allocation weights, entering
Corresponding technorati authority is assigned for each node when row weights are distributed, and with reference to user's practical operation situation, analog subscriber is browsed
Selection next step operation can be carried out during webpage according to subjective purpose, so as to carry out effective mistake to the noise information in network
Filter solves classical PageRank algorithms because of mean allocation weights there is provided a kind of method for digging of social networks key user
The influence caused to result.
The technical solution adopted for the present invention to solve the technical problems is：A kind of excavation side of social networks key user
Method, specific method is as follows：
a：When carrying out weights distribution, carry out calculating the technorati authority for introducing website, its Au- using Au-PageRank algorithms
PageRank calculation formula is：Wherein p (vi) it is technorati authority；
b：With reference to user's practical operation situation, go to judge that webpage is important according to the text message of link display and subjective purpose
With meet the demand of oneself so that using 2S-PageRank algorithms carry out calculate user's next step browsing pages probability, herein
On the basis of the probability transfer matrix that calculates will be more sparse than classical PageRank transfer matrix, can add when calculating
Rapid convergence speed, its 2S-PageRank calculation formula is：Wherein C (vk) represent to work as
Connection number in the page that k-th is linked in the preceding page；
c：In website, with reference to the probability of a technorati authorities drawn and the b browsing pages drawn, user is set to surf the Net again
When more see a step, so as to draw Au-2S-PageRank algorithms, pass through Au-2S-PageRank algorithms and produce ranking fruit, its Au-
2S-PageRank calculation formula is：
It is characterized in that：Described technorati authority p (vi) determined by webpage is pointed at link with the ratio that links of sensing：Wherein, LinkOut is the number of links for quoting webpage i；LinkIn is that the webpage quotes other webpages
Number of links；Q is a constant related to damped coefficient d.
Described Au-PageRank algorithms are comprised the following steps that：(1) data prediction；(2) by the data of processing according to
Node link relation generates adjacency matrix；(3) according to the technorati authority for going out in-degree situation calculate node of each node；(4) will section
Point technorati authority substitutes into Au-PageRank formula and is iterated calculating；(5) it is final to produce ranking fruit.
Described 2S-PageRank algorithms are comprised the following steps that：(1) data prediction；(2) by the data of processing according to
Node link relation generates adjacency matrix；(3) the first level of child nodes is calculated according to the second level of child nodes out-degree of each node to exist
Shared probability right when distributing weights；(4) node weight weight values substitution 2S-PageRank formula are iterated calculating；(5) it is final
Produce ranking fruit.
Described Au-2S-PageRank algorithms are comprised the following steps that：(1) data prediction；(2) by the data of processing
Adjacency matrix is generated according to node link relation；(3) according to the technorati authority and root that go out in-degree situation calculate node of each node
The first level of child nodes shared probability right when distributing weights is calculated according to the second level of child nodes out-degree of each node；(4) will section
The probability right when technorati authority of point is with node distribution weights substitutes into Au-2S-PageRank formula and is iterated calculating simultaneously；
(5) it is final to produce ranking fruit.
The beneficial effects of the invention are as follows：The method for digging of described a kind of social networks key user, by Au-PageRank
Algorithm and 2S-PageRank algorithms combine to form Au-2S-PageRank algorithms, solve classical PageRank algorithms because average
The influence that distribution weights are caused to result, and can accelerate convergence rate when calculating, ranking results are more accurate.
Brief description of the drawings
The present invention is further described with reference to the accompanying drawings and examples.
Fig. 1 is a kind of flow chart of the method for digging of social networks key user of the present invention；
Fig. 2 is the flow of Au-PageRank algorithms in Fig. 1；
Fig. 3 is the flow of 2S-PageRank algorithms in Fig. 1；
Fig. 4 is the flow of Au-2S-PageRank algorithms in Fig. 1；
Fig. 5 is the run time line chart of four kinds of algorithms.
Embodiment
In conjunction with the accompanying drawings, the present invention is further explained in detail.These accompanying drawings are simplified schematic diagram, only with
Illustration illustrates the basic structure of the present invention, therefore it only shows the composition relevant with the present invention.
A kind of method for digging of social networks key user as shown in Figure 1, specific method is as follows：a：Carrying out weights point
Timing, carries out calculating the technorati authority for introducing website, Au-PageRank algorithms are comprised the following steps that using Au-PageRank algorithms,
As shown in Figure 2：(1) data prediction；(2) data of processing are generated into adjacency matrix according to node link relation；(3) according to each
The technorati authority for going out in-degree situation calculate node of node；(4) node technorati authority substitution Au-PageRank formula are iterated meter
Calculate；(5) final to produce ranking fruit, its Au-PageRank calculation formula is：
Wherein p (vi) it is technorati authority, technorati authority p (vi) determined by webpage is pointed at link with the ratio that links of sensing：Wherein, LinkOut is the number of links for quoting webpage i；LinkIn is that the webpage quotes other webpages
Number of links；Q is a constant related to damped coefficient d；
b：With reference to user's practical operation situation, go to judge that webpage is important according to the text message of link display and subjective purpose
With meet the demand of oneself so that utilize 2S-PageRank algorithms carry out calculate user's next step browsing pages probability, 2S-
PageRank algorithms comprise the following steps that, as shown in Figure 3：(1) data prediction；(2) by the data of processing according to node chain
Connect relation generation adjacency matrix；(3) the first level of child nodes is calculated according to the second level of child nodes out-degree of each node to weigh in distribution
Shared probability right during value；(4) node weight weight values substitution 2S-PageRank formula are iterated calculating；(5) the final row of generation
Really, its 2S-PageRank calculation formula is name：Wherein C (vk) represent current page
In connection number in the page that is linked to for k-th；
c：In website, with reference to the probability of a technorati authorities drawn and the b browsing pages drawn, user is set to surf the Net again
When more see a step, so as to draw Au-2S-PageRank algorithms, Au-2S-PageRank algorithms are comprised the following steps that, such as Fig. 4
It is shown：(1) data prediction；(2) data of processing are generated into adjacency matrix according to node link relation；(3) according to each section
Point go out in-degree situation calculate node technorati authority and according to the second level of child nodes out-degree of each node calculate the first straton section
Point shared probability right when distributing weights；(4) probability right during by the technorati authority of node with node distribution weights is contemporary
Enter Au-2S-PageRank formula and be iterated calculating；(5) it is final to produce ranking fruit, its Au-2S-PageRank calculation formula
For：
Embodiment：
First, experimental situation and data
Experimental data pushes away special data set using what SNAP (Stanford Network Analysis Platform) was provided.
SNAP is a system that is general, efficiently can analyzing and handle catenet, and it supports figure and two kinds of data structures of net.Its
In, figure describes topological structure, i.e., each node has the side between a unique integer id, node to be oriented
Or it is undirected, and can have multiple summits between two nodes；Net can be regarded as and assigned on a kind of node or side
There is the figure of data.The data type of these data can be transmitted easily as template parameter, and this is just to realize those at it
The various networks for having abundant data on node and side provide a kind of quickly and easily method.
In order to verify the effect of innovatory algorithm, entered using the SNAP digraphs between special ID digital simulations webpage that push away provided
Row experiment.Used data set basic condition is as shown in table 1.
Table 1：Experimental data counts basic condition
2nd, algorithm execution time
By the pretreatment to data, the representative node of selected part is respectively to classical PageRank algorithms, Au-
PageRank algorithms, 2S-PageRank algorithms and Au-2S-PageRank algorithms are tested.Test four kinds of calculations respectively first
The run time of method, it is 1000,2000,3000,4000, as a result as shown in Figure 5, the operation of four kinds of algorithms that nodes are chosen respectively
Time can all increase with the increase of interstitial content, but its respective run time increasing degree is not quite similar.Wherein
Tri- kinds of algorithms of PageRank, 2S-PageRank and Au-2S-PageRank are with the increase of nodes, the growth of its run time
Situation is closer to, and Au-PageRank run time is greatly prolonged with the increase of nodes；In addition, the Au- after improving
2S-PageRank algorithms are in node increase, and run time is substantially better than classical PageRank algorithms, but is due to wherein to contain
The thought of Au-PageRank algorithms, causes postorder to be calculated as the continuation of node increases run time higher than 2S-PageRank
Method.
3rd, algorithm performs result
Classics PageRank, Au-PageRank, 2S-PageRank and Au-2S-PageRank algorithm is performed respectively, is drawn
Top20 rankings it is as shown in table 2：
Table 2：The contrast of 4 kinds of sort algorithm results
For the data in table it can be found that because classical PageRank algorithms mean allocation PR is worth shortcoming so that Au-
The result that PageRank, StepPageRank and Au-StepPageRank are slightly different.
(1)PageRank：The algorithm is mean allocation weight for the link that a webpage chain goes out, and is not accounted for existing
Noise link in reality, and weights are allocated to the link of these noises, so that the accuracy reduction of meeting guide sequence.Such as table 1
In ID：813286 and ID：Although 1183041 rankings are in first and the 6th, be probably because it is the public No. ID,
Largely quoted containing substantial amounts of advertisement or information, therefore by other nodes, thus cause it is in the top but its own
Significance level is not obvious.
(2)Au-PageRank：The algorithm considers the defect of the mean allocation weights of classics PageRank algorithms, for net
Each node in network assigns a technorati authority, and the distribution of weights is carried out according to technorati authority size so that final ranking results are accurate
True property is improved.But be ranked up according only to linked network, not in view of the subjective factor of user in reality, therefore result is also
It is the presence of certain deviation.In table 1 compared with the result of classical PageRank algorithms, ID：22462180、ID：34428380
Ranking rise to first, second, due to consideration that the TOP V in node technorati authority factor, PageRank arithmetic results all goes out
Different degrees of retrogressing is showed.
(3)2S-PageRank：The algorithm is also the defect that take into account classical PageRank algorithms mean allocation weights,
Accounted for from the angle of user's subjectivity selection, user is inferred the link feelings of next node according to the literal meaning of link
Condition, so as to reach the effect of unequal distribution weights.It can be seen according to the comparison of table 1 and classics PageRank algorithm ranking results
Go out, 2S-PageRank TOP V is all replaced, and the ID of first, second of PageRank algorithms：813286、ID：
7861312 ranking seven, the 8th in the algorithm.Algorithm show consider user's subjective factor after, arithmetic result have compared with
Big change.
(4)Au-2S-PageRank：The first three methods considered, in order to make up being averaged for classical PageRank algorithms
Weights defect, Au-PageRank algorithms are combined with the thought of 2S-PageRank algorithms, are that the node in network assigns difference
Weights, while in view of the subjective purpose of user so that faster, ranking results are more accurate for convergence rate.By the result of table 1
ID can be drawn：783214 are number two ten in classical PageRank, are number two ten in Au-PageRank, and at this
Ranked the first in algorithm, understand that the technorati authority of the node is larger by analysis, and user can clearly obtain the link of node
Situation, same ID：90420314 node rankings also increase；And ID：40981798 and ID：43003845 nodes are in first three
Ranking is all earlier in algorithm, and ranking has a certain degree of decline in this algorithm.
Using the above-mentioned desirable embodiment according to the present invention as enlightenment, by above-mentioned description, relevant staff is complete
Various changes and amendments can be carried out without departing from the scope of the technological thought of the present invention' entirely.The technology of this invention
Property scope is not limited to the content on specification, it is necessary to its technical scope is determined according to right.
Claims (5)
1. a kind of method for digging of social networks key user, it is characterized in that, specific method is as follows：
a：When carrying out weights distribution, carry out calculating the technorati authority for introducing website, its Au- using Au-PageRank algorithms
PageRank calculation formula is：Wherein p (vi) it is technorati authority；
b：Reference user's practical operation situation, goes to judge that webpage is important and accords with according to the text message of link display and subjective purpose
The demand of oneself is closed, so as to using 2S-PageRank algorithms calculate the probability of user's next step browsing pages, its 2S-
PageRank calculation formula is：Wherein C (vk) represent k-th of link in current page
To the page in connection number；
c：In website, with reference to the probability of a technorati authorities drawn and the b browsing pages drawn, allow user many when surfing the Net again
A step is seen, so as to draw Au-2S-PageRank algorithms, ranking fruit, its Au-2S- are produced by Au-2S-PageRank algorithms
PageRank calculation formula is：
2. the method for digging of social networks key user according to claim 1 a kind of, it is characterized in that：Described technorati authority
p(vi) determined by webpage is pointed at link with the ratio that links of sensing：Wherein, LinkOut is reference
Webpage i number of links；LinkIn is the number of links that the webpage quotes other webpages；Q is one related to damped coefficient d
Constant.
3. the method for digging of social networks key user according to claim 1 a kind of, it is characterized in that：Described Au-
PageRank algorithms are comprised the following steps that：(1) data prediction；(2) data of processing are generated according to node link relation
Adjacency matrix；(3) according to the technorati authority for going out in-degree situation calculate node of each node；(4) node technorati authority is substituted into Au-
PageRank formula are iterated calculating；(5) it is final to produce ranking fruit.
4. the method for digging of social networks key user according to claim 1 a kind of, it is characterized in that：Described 2S-
PageRank algorithms are comprised the following steps that：(1) data prediction；(2) data of processing are generated according to node link relation
Adjacency matrix；(3) according to the second level of child nodes out-degree of each node calculate the first level of child nodes when distributing weights it is shared general
Rate weight；(4) node weight weight values substitution 2S-PageRank formula are iterated calculating；(5) it is final to produce ranking fruit.
5. the method for digging of social networks key user according to claim 1 a kind of, it is characterized in that：Described Au-2S-
PageRank algorithms are comprised the following steps that：(1) data prediction；(2) data of processing are generated according to node link relation
Adjacency matrix；(3) according to the technorati authority for going out in-degree situation calculate node and the second straton according to each node of each node
Node out-degree calculates the first level of child nodes shared probability right when distributing weights；(4) by the technorati authority and node distribution of node
Probability right during weights substitutes into Au-2S-PageRank formula and is iterated calculating simultaneously；(5) it is final to produce ranking fruit.
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| CN110134877A (en) * | 2019-05-15 | 2019-08-16 | 天津大学 | Move down the line the method and apparatus that seed user is excavated in social networks |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN108536866A (en) * | 2018-04-24 | 2018-09-14 | 中国人民解放军战略支援部队信息工程大学 | The hidden key user's analysis method of microblogging based on topic entropy of transition |
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Application publication date: 20171103 |