CN104462318A  Identity recognition method and device of identical names in multiple networks  Google Patents
Identity recognition method and device of identical names in multiple networks Download PDFInfo
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 CN104462318A CN104462318A CN201410719649.9A CN201410719649A CN104462318A CN 104462318 A CN104462318 A CN 104462318A CN 201410719649 A CN201410719649 A CN 201410719649A CN 104462318 A CN104462318 A CN 104462318A
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Abstract
Description
Technical field
The present invention relates to technical field of information processing, refer to personal identification method and the device of identical name in a kind of Multi net voting especially.
Background technology
Generally, same user registers different identity informations in heterogeneous networks, such as, and Email address, phone etc. information.Such as, at field of scientific study, often have a large amount of scientific worker collaborative work in multiple Research Team simultaneously, cause the personal information that same person uses when delivering academy's successes thus, as Email, unit, address etc., may not be identical, namely identical name has different identity information.When gathering academy's successes information relevant in field, owing to being difficult to judge whether these identical names are same person, such redundant information directly can affect the accuracy of statistics.Such as, scientific worker works in different team, the personal information of same scientific worker may occur in multiple network, such as, the website, paper net, technological achievements transfer net, patent transaction net etc. of certain university, and the personal information of this scientific worker in multiple network is not necessarily identical.
Traditional methods of social network only consider the behavioural characteristic of user in single network (as held a post in certain colleges and universities) usually, have ignored user and may be in association situation in multiple network, such as a user can be active in colleges and universities, scientific research institution of stateowned enterprise and social research institution simultaneously, and have different identity, interpersonal circle and research contents in each community network, the behavior analysis method for single network cannot be applied to this multitiered network environment.In multiple network, the node in each network may have distinct attribute, and there is incidence relations such as interdepending and cooperate between network and the node of network, therefore, needs a kind of method to the establishing identity of individuality of the same name in Multi net voting.
Summary of the invention
In view of this, the object of the invention is to the personal identification method and the device that propose identical name in a kind of Multi net voting, can will have different identity information in multiple network but the identical information of name is carried out homogeneity and determined.
The invention provides the personal identification method of identical name in a kind of Multi net voting based on abovementioned purpose, comprising: obtain the subscriber identity information in multiple network and user identity corresponding relation; Using the subscriber identity information set of known users identity corresponding relation as training set; Build the minimum energy model based on user behavior similarity according to the described subscriber identity information in described training set, obtain energy factors and matching relationship sorter; According to described matching relationship sorter, any two subscriber identity informations are mated, and adopt energy factors to carry out energy filling forming energy matrix, solve the matching result that this energy matrix obtains single prediction; Carry out integrated to the matching result repeatedly solved, obtain user identity corresponding relation and determine to have the identity homogeneity of identical name user.
According to one embodiment of present invention, further, described using the set of the described subscriber identity information of known users identity corresponding relation as training set, according to the described subscriber identity information in described training set build based on user behavior similarity minimum energy model, obtain energy factors and matching relationship sorter comprises: for node V (i) any given in 2 networks P, Q, its network topology structure proper vector is: f (i)={ f
_{1}, f
_{2}... f
_{d}, wherein, node on behalf subscriber identity information, f
_{[1d]}for the basic attributive character of node, comprising: node outdegree, indegree, cluster coefficients, neighbor node, average degree, common neighbours; Set up node to proper vector vector, the node for 2 networks P, Q to proper vector vector is:
According to one embodiment of present invention, further, described according to described matching relationship sorter any two subscriber identity informations are carried out mating and adopt energy factors to carry out energy fill forming energy matrix, solve the matching result that this energy matrix obtains single prediction and comprise: its topological features is extracted respectively to the node of identity corresponding relation unknown in network P, Q: F
_{p}(i)={ f
_{p}(1), f
_{p}(2) ..., f
_{p}(m) } and F
_{q}(i)={ f
_{q}(1), f
_{q}(2) ..., f
_{q}(m) }; For the node i ∈ P of any unknown identity corresponding relation, j ∈ Q, build the matched node of n × n all unknown node to proper vector:
According to one embodiment of present invention, further, the algorithm calculating the optimum matching of this energy matrix is:
λ _{ij}∈{0,1}；
Wherein, λ
_{ij}represent whether the node i in network P and the node j in network G exist onetoone relationship, if
corresponding relation be established, be labeled as 1, otherwise be labeled as 0, matching result is expressed as
According to one embodiment of present invention, further, the described matching result to repeatedly solving carries out integrated, obtain the corresponding relation of subscriber identity information and determine that the identity homogeneity with identical name comprises: obtaining ξ and predict the outcome, to vote predicting the outcome at every turn in node is to coupling matrix, obtain ballot matrix VMatrix=(V _{ij}); Solve the Optimum Matching problem of this ballot matrix VMatrix, the formula of employing is:
λ _{ij}∈{0，1}；
Wherein, v _{ij}represent the voting results of the ith row jth row in ballot matrix, λ _{ij}represent whether the node i in network P and the node j in network G exist onetoone relationship, namely represent the final matching results that node is right.
The invention provides the identity recognition device of identical name in a kind of Multi net voting based on abovementioned purpose, comprising: information acquisition unit, for obtaining subscriber identity information in multiple network and user identity corresponding relation; Training set generation unit, for using the subscriber identity information set of known users identity corresponding relation as training set; Build the minimum energy model based on user behavior similarity according to the described subscriber identity information in described training set, obtain energy factors and matching relationship sorter; Matching unit, for being mated by any two subscriber identity informations according to described matching relationship sorter, and adopting energy factors to carry out energy filling forming energy matrix, solving the matching result that this energy matrix obtains single prediction; Integrated unit, for carrying out integrated to the matching result repeatedly solved, obtaining user identity corresponding relation and determining to have the identity homogeneity of identical name user.
According to one embodiment of present invention, further, described training set generation unit, comprising: node sets up submodule to feature, for for node V (i) any given in 2 networks P, Q, setting up its network topology structure proper vector is: f (i)={ f
_{1}, f
_{2}... f
_{d}, wherein, node on behalf subscriber identity information, f
_{[1d]}for the basic attributive character of node, comprising: node outdegree, indegree, cluster coefficients, neighbor node, average degree, common neighbours; Set up node to proper vector vector, the node for 2 networks P, Q to proper vector vector is:
According to one embodiment of present invention, further, described training set generation unit, also comprises: node sets up submodule to classification, for extracting its topological features respectively to the node of identity corresponding relation unknown in network P, Q: F
_{p}(i)={ f
_{p}(1), f
_{p}(2) ..., f
_{p}(m) } and F
_{q}(i)={ f
_{q}(1), f
_{q}(2) ..., f
_{q}(m) }; For the node i ∈ P of any unknown identity corresponding relation, j ∈ Q, build the matched node of n × n all unknown node to proper vector:
According to one embodiment of present invention, further, described matching unit calculates the algorithm of the optimum matching of this energy matrix and is:
λ _{ij}∈{0,1}；
Wherein, λ
_{ij}represent whether the node i in network P and the node j in network G exist onetoone relationship, if
corresponding relation be established, be labeled as 1, otherwise be labeled as 0, matching result is expressed as
According to one embodiment of present invention, further, described integrated unit, also to predict the outcome with obtaining ξ, to vote, obtain ballot matrix VMatrix=(V by predicting the outcome at every turn in node is to coupling matrix _{ij}); Solve the Optimum Matching problem of this ballot matrix VMatrix, the formula of employing is:
λ _{ij}∈{0,1}；
Wherein, v _{ij}represent the voting results of the ith row jth row in ballot matrix, λ _{ij}represent whether the node i in network P and the node j in network G exist onetoone relationship, namely represent the final matching results that node is right.
As can be seen from above, the personal identification method of identical name and device in Multi net voting of the present invention, different identity information can will be had but the identical information of name carries out homogeneity confirmation in multiple network, different identity information can be confirmed but whether the identical people of name is same person, the accuracy of statistics can be improved, and, the algorithm adopted is efficient, computation process is very fast, and along with the increase of Sample Storehouse, result of calculation accuracy rate also can improve constantly.
Accompanying drawing explanation
Fig. 1 is the process flow diagram of an embodiment of the personal identification method of identical name in Multi net voting of the present invention;
Fig. 2 is the process flow diagram of another embodiment of the personal identification method of identical name in Multi net voting of the present invention;
In Fig. 3 Multi net voting of the present invention, the twotier network arbitrary node of the personal identification method of identical name is to classification matrix schematic diagram;
Fig. 4 is that in the energy matrix of the personal identification method of identical name in Multi net voting of the present invention, energy factors fills schematic diagram;
The schematic diagram of ballot matrix when Fig. 5 is ξ=2 of the personal identification method of identical name in Multi net voting of the present invention;
Fig. 6 is the ballot of the personal identification method of identical name in Multi net voting of the present invention and the schematic diagram of Integrated Algorithm process;
Fig. 7 is the schematic diagram of an embodiment of the identity recognition device of identical name in Multi net voting of the present invention.
Embodiment
For making the object, technical solutions and advantages of the present invention clearly understand, below in conjunction with specific embodiment, and with reference to accompanying drawing, the present invention is described in more detail.
Fig. 1 is the process flow diagram of an embodiment of the personal identification method of identical name in Multi net voting of the present invention; As shown in Figure 1:
Step 101, obtains the subscriber identity information in multiple network and user identity corresponding relation.
Step 102, using the subscriber identity information set of known users identity corresponding relation as training set.
Step 103, builds the minimum energy model based on user behavior similarity according to the described subscriber identity information in described training set, obtains energy factors and matching relationship sorter.
Any two subscriber identity informations are mated according to described matching relationship sorter by step 104, and adopt energy factors to carry out energy filling forming energy matrix, solve the matching result that this energy matrix obtains single prediction.
Step 105, carries out integrated to the matching result repeatedly solved, and obtains user identity corresponding relation and determines to have the identity homogeneity of identical name user.
Determine that the key of the Problems of Identity of individuality of the same name in Multi net voting is to find out the individual node corresponding relation in heterogeneous networks of multiple identities, i.e. network intermediate node matching problem.And mutual interactive information etc. between a large amount of individuality in the internet information records such as the topology information of heterogeneous networks, social network sites, possibility is provided for solving the internetwork coupled problem of different layers, such as, the node with same identity can be identified in heterogeneous networks to a certain extent by degree, bunch coefficient, neighbours' structure, common friends etc.
Fig. 2 is the process flow diagram of another embodiment of the personal identification method of identical name in Multi net voting of the present invention; As shown in Figure 2:
Step 201208 is model formulation, using the user of known identities corresponding relation set as training set, according to known part of nodes to the minimum energy model of information one side structure based on user behavior similarity, thus obtain node to corresponding energy factors, training obtains node to matching relationship sorter on the other hand, is used to guide the coupling that unknown matching relationship node is right.
Step 210216 is node matching, carries out the coupling of any two nodes according to the sorter in model formulation process, and adopts energy factors to carry out energy filling, obtains the node of single prediction to matching result in the energy minimization process after solving filling.
Step 217219 for ballot integrated, on the basis of repeatedly node matching process, carrying out predicting the outcome integrated, obtaining the corresponding relation of user identity in final multitiered network, and judge the homogeneity of identical name with this.
Ising model is a kind of model describing material phase transformation.Through phase transformation, new structure and physical property be there is in material.The system undergone phase transition is generally the system had between molecule compared with strong interaction, also known as partner systems.In model formulation process, Yi Xin theoretical model principle is applied to, in the node matching process of two networks, by extracting the topological features f of nodes, set up matched node to proper vector F _{pQ}.
According to this proper vector to matched node to carrying out cluster, obtain the number with similar features node comprised in each cluster classification C, it can be used as particle characteristics.The user group with similar network behavioural characteristic as magnetic probability (energy factors), is given equivalent energy factors to the distribution situation of feature by node.According to the maximum energy criterion of spin model, suppose that system total energy value is minimum when nodes all in doublelayer network are to during by entirely true coupling, and build matched node according to this to energy model:
Wherein, β _{i}for the matched node of similar features carries out the node after cluster to number, ε to set according to feature _{i}for the energy factors that this classification is corresponding.
By nonlinear optimization method, the energy factors corresponding to each cluster classification will be obtained: ε={ ε _{1}, ε _{2}..., ε _{k}, and it can be used as forecasting process interior joint to the energy factors of generic.Set up Ksorter (CLASSIFIER) according to abovementioned cluster category result, and give each node to class number
In one embodiment, the prerequisite setting up energy model is that the vectorization of network node represents, for any given node V (i), defining its network topology structure proper vector is: f (i)={ f _{1}, f _{2}... f _{d}, wherein f _{[1d]}may be the basic attributive character of node, such as node outdegree, indegree, cluster coefficients, neighbor node, average degree etc., also may be point spread attributive character, such as common neighbours, Jaccard coefficient etc. between two nodes.
On this basis, the right structural eigenvector of node is then the set of base attribute characteristic sum extended attribute feature in multitiered network, and for two networks or twotier network, then node can be expressed as vector:
In one embodiment, in node matching process, for two or twotier network, node contains the institute of twotier network interior joint coupling likely to classification matrix, may correct matching result one to one be found then identity forecasting problem right for node to be converted to bipartite graph Optimum Matching problem situation from numerous, namely make only there is a matching result in any row and column in matrix by optimized algorithm.
And according to the minimum model of energy value, in order to make matching result global energy value minimum, first need to carry out energy factors filling to classification matrix, replace with the energy factors corresponding to this classification by class label in matrix, and build energy matrix as shown in Figure 3.In this n × n energy matrix, this method target, for finding n best matching result, meets system capacity value minimum.Available algorithm is a lot, such as Hungary Algorithm.
Hungary Algorithm is one of numerous algorithm for solving linear Task Allocation Problem, is used to the classic algorithm solving bipartite graph maximum matching problem.If G=(V, E) is a nondirected graph.As vertex set V can subregion be two mutually disjoint subset V1, V2 also, and two summits that in figure, every bar limit depends on all belong to these two different subsets, then title figure G is bipartite graph.Bipartite graph also can be designated as G=(V1, V2, E).A given bipartite graph G, in a subgraph M of G, { any two limits in E} do not depend on same summit to the limit collection of M, then claim M to be a coupling.Subset that in such subset, limit number is maximum is selected to be called the maximum matching problem (maximal matching problem) of figure.If figure's is all summita limit in all mating with certain is associated, then claim this coupling for mate completely, also referred to as complete, and perfect matching.
For 2 networks, first, to the node of identity corresponding relation unknown in network, its topological features is extracted respectively: F _{p}(i)={ f _{p}(1), f _{p}(2) ..., f _{p}(m) } and F _{q}(i)={ f _{q}(1), f _{q}(2) ..., f _{q}(m) }.
For arbitrary node i ∈ P, j ∈ Q, build n × n all possible matched node to proper vector:
Classified by K sorter, obtain each node to class label, thus the node built as shown in Figure 3 is to classification matrix.
Node contains the institute of twotier network interior joint coupling likely to classification matrix, may correct matching result one to one be found then identity forecasting problem right for node to be converted to bipartite graph Optimum Matching problem situation from numerous, namely make only there is a matching result in any row and column in matrix by optimized algorithm.And according to the minimum model of energy value, in order to make matching result global energy value minimum, first need to carry out energy factors filling to classification matrix, replace with the energy factors ε corresponding to this classification by class label in matrix _{i=catogory}, and build energy matrix as shown in Figure 4 further.
In energy matrix, target, for finding n best matching result, meets system capacity value minimum.Adopt Hungary Algorithm calculate this optimum matching, its mathematical model or algorithm as follows:
λ _{ij}∈{0,1}
Wherein, λ _{ij}represent whether the node i in network P and the node j in network G exist onetoone relationship, if corresponding relation be established, be labeled as 1, otherwise be labeled as 0.Without loss of generality, matching result is expressed as
In one embodiment, in ballot integrating process, due to the randomness of clustering algorithm when choosing cluster centre, the global optimum of cluster result might not be ensured, the uncertainty that this characteristic can cause single to predict the outcome.In order to more be stablized and result accurately, the present invention introduces Integrated Algorithm, is finally predicted the outcome by ballot and Secondary Match optimization.
For given data to be predicted, the identity that first reruns corresponding relation prediction algorithm ξ time, obtains ξ and to predict the outcome, then to vote predicting the outcome at every turn in node is to coupling matrix, obtains ballot matrix VMatrix=(V _{ij}), as shown in Figure 5.Such as, given ξ=2, then will obtain twice matching result, if the node corresponding relation of matching result is for the first time then V is set _{11}, V _{22}, V _{33}and V _{nn}value be 1, if second time matching result node corresponding relation be then V is set _{11}, V _{23}, V _{32}and V _{nn}value be 1, and by V _{11}and V _{nn}value add 1.
Again adopt the Optimum Matching problem of Hungarian Method this bipartite graph, to make system capacity value reach minimum process in forecasting process different, in Voting Algorithm, made by following for employing algorithm voting results reach overall maximum:
λ _{ij}∈{0，1}
Wherein, v _{ij}represent the voting results of the ith row jth row in ballot matrix, λ _{ij}represent whether the node i in network P and the node j in network G exist onetoone relationship, namely represent the final matching results that node is right.After this bipartite graph Optimum Matching problem solving, obtain node as shown in Figure 6 to final matching results.
In one embodiment, adopt the True Data collection of disclosed 2 networks, be respectively Twitter and Friendfeed network, this data set comprises 155,804 users being simultaneously registered in Twitter and Friendfeed, and comprises its user identity corresponding relation.Wherein, Twitter data set comprises 13, effectively pay close attention to relation record for 142,341, and Friendfeed data set comprises 5,939,687 effective friends records.
In experimentation, be training set and test set by Data Placement, and the user's ratio arranging unknown identity corresponding relation is α, and α ∈ (0,1), such as α=5% item represents under the prerequisite of known 95% identity corresponding relation, the identity corresponding relation of prediction residue 5% user.
Under the prerequisite of fixing cluster number K and unknown data collection ratio α, the accuracy rate of testing algorithm, and on the data set of different size the extensibility of algorithm.In the test of carried out ten examples, parameter is set to K=6 respectively, α=5% and ξ=20,100}, as shown in table 1 below:
Table 1test figure table
Can be seen by upper table, multitiered network node identities Forecasting Methodology proposed by the invention Average Accuracy on this True Data collection, more than 90%, and has consistance result for the data set of different size, shows that the method has good extensibility.
As shown in Figure 7, the invention provides the identity recognition device 4 of identical name in a kind of Multi net voting, comprising: information acquisition unit 41, training set generation unit 42, matching unit 43, integrated unit 44.Information acquisition unit 41 obtains subscriber identity information in multiple network and user identity corresponding relation.Training set generation unit 42 using the subscriber identity information set of known users identity corresponding relation as training set; Build the minimum energy model based on user behavior similarity according to the described subscriber identity information in described training set, obtain energy factors and matching relationship sorter.
Any two subscriber identity informations mate according to described matching relationship sorter by matching unit 43, and adopt energy factors to carry out energy filling forming energy matrix, solve the matching result that this energy matrix obtains single prediction.Integrated unit 44 carries out integrated to the matching result repeatedly solved, and obtains user identity corresponding relation and determines to have the identity homogeneity of identical name user.
In one embodiment, training set generation unit 42, comprising: node sets up submodule to feature, and for node V (i) any given in 2 networks P, Q, setting up its network topology structure proper vector is: f (i)={ f
_{1}, f
_{2}... f
_{d}, wherein, node on behalf subscriber identity information, f
_{[1d]}for the basic attributive character of node, comprising: node outdegree, indegree, cluster coefficients, neighbor node, average degree, common neighbours; Set up node to proper vector vector, the node for 2 networks P, Q to proper vector vector is:
Training set generation unit 42 comprises: sorter generates submodule, builds matched node to energy model: β _{i}for the matched node of similar features carries out the node after cluster to number, ε to set according to feature _{i}for the energy factors that this classification is corresponding; The energy factors corresponding to each cluster classification is obtained: ε={ ε according to described energy model _{1}, ε _{2}..., ε _{k}, and it can be used as forecasting process interior joint to the energy factors of generic; Set up K sorter according to cluster category result, and give each node to class number.
Described training set generation unit 42 also comprises: node sets up submodule to classification, extracts its topological features respectively: F to the node of identity corresponding relation unknown in network P, Q
_{p}(i)={ f
_{p}(1), f
_{p}(2) ..., f
_{p}(m) } and F
_{q}(i)={ f
_{q}(1), f
_{q}(2) ..., f
_{q}(m) }; For the node i ∈ P of any unknown identity corresponding relation, j ∈ Q, build the matched node of n × n all unknown node to proper vector:
Described matching unit 43 pairs of classification matrixes carry out energy factors filling, and class label in classification matrix is replaced with the energy factors ε corresponding to this classification _{i=catogory}, build energy matrix, calculate the optimum matching of energy matrix.
The algorithm that described matching unit 43 calculates the optimum matching of this energy matrix is:
λ _{ij}∈{0,1}；
Wherein, λ
_{ij}represent whether the node i in network P and the node j in network G exist onetoone relationship, if
corresponding relation be established, be labeled as 1, otherwise be labeled as 0, matching result is expressed as
Described integrated unit 44 obtains ξ and to predict the outcome, and to vote, obtain ballot matrix VMatrix=(V by predicting the outcome at every turn in node is to coupling matrix _{ij}); Solve the Optimum Matching problem of this ballot matrix VMatrix, the formula of employing is:
λ _{ij}∈{0,1}；
Wherein, v _{ij}represent the voting results of the ith row jth row in ballot matrix, λ _{ij}represent whether the node i in network P and the node j in network G exist onetoone relationship, namely represent the final matching results that node is right.
The personal identification method of identical name and device in Multi net voting of the present invention, different identity information can will be had but the identical information of name carries out homogeneity confirmation in multiple network, different identity information can be confirmed but whether the identical people of name is same person, the accuracy of statistics can be improved.The algorithm adopted is efficient, and computation process is very fast, and along with the increase of Sample Storehouse, result of calculation accuracy rate also can improve constantly.
The memory management method in the intelligent meter storehouse that abovedescribed embodiment provides and system, solve according to the suitable storage policy of different warehouse Foreground selection by the storage policy optimized, not only effectively can utilize storage space, improve warehouse execution efficiency, reduce operating cost, also can bring a lot of benefit for whole intelligent meter storehouse in management simultaneously.
Those of ordinary skill in the field are to be understood that: the foregoing is only specific embodiments of the invention; be not limited to the present invention; within the spirit and principles in the present invention all, any amendment made, equivalent replacement, improvement etc., all should be included within protection scope of the present invention.
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CN105227352A (en) *  20150902  20160106  新浪网技术(中国)有限公司  A kind of update method of user ID collection and device 
CN106529110A (en) *  20150909  20170322  阿里巴巴集团控股有限公司  Classification method and equipment of user data 
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Cited By (4)
Publication number  Priority date  Publication date  Assignee  Title 

CN105227352A (en) *  20150902  20160106  新浪网技术(中国)有限公司  A kind of update method of user ID collection and device 
CN105227352B (en) *  20150902  20190319  新浪网技术(中国)有限公司  A kind of update method and device of user identifier collection 
CN106529110A (en) *  20150909  20170322  阿里巴巴集团控股有限公司  Classification method and equipment of user data 
CN107330459A (en) *  20170628  20171107  联想(北京)有限公司  A kind of data processing method, device and electronic equipment 
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