Patent Publication Number: US-9424527-B2

Title: Information processing system, network structure learning device, link strength prediction device, link strength prediction method and program

Description:
CROSS-REFERENCE TO RELATED PATENT APPLICATIONS 
     This application is a National Stage Entry of International Application No. PCT/JP2012/065949, filed on Jun. 22, 2012, which is based upon and claims the benefit of priority from Japanese Patent Application No. 2011-140112 filed on Jun. 24, 2011. The entire contents of the above-referenced applications are expressly incorporated herein by reference. 
     TECHNICAL FIELD 
     The present invention relates to an information processing system, network structure learning device, link strength prediction device, link strength prediction method and program, and particularly to an information processing system, network structure learning device, link strength prediction device, link strength prediction method and program that deal with matters of network structure learning and link prediction in the data mining field. 
     BACKGROUND 
     Methods that analyze the strength of connections between users in social network services or the prediction of users&#39; preferences regarding products in online shopping as matters of the structure of a network and the link strength thereof have been proposed. Non-Patent Literature 1 proposes an analytical method that simultaneously performs link prediction on a plurality of types of links between nodes in a network. 
     Further, Non-Patent Literature 2 proposes a flexible analytical method that expresses to what extent structural differences described above exist between networks as a probability model. As an application of this analytical method, data of different product categories available online are simultaneously analyzed, and the results indicate that the method is capable of predicting users&#39; preferences more accurately than when each data of individual product categories are separately analyzed. 
     Non-Patent Literatures 3 and 4 are reference documents regarding hierarchical models. 
     CITATION LIST 
     [Non-Patent Literature] (NPL) 
     [Non-Patent Literature 1 
     
         
         Hisashi Kashima, Tsuyoshi Kato, Yoshihiro Yamanish, Masashi Sugiyama, and Koji Tsuda, “Link Propagation: A Fast Semi-supervised Learning Algorithm for Link Prediction,” Proceedings of the 9 th  SIAM International Conference on Data Mining (SDM 2009), pp. 1099-1110.
 
[Non-Patent Literature 2]
 
         Yu Zhang, Bin Cao, and Dit-Yan Yeung, “Multi-Domain Collaborative Filtering,” Proceedings of the 26th Conference on Uncertainly in Artificial Intelligence (UAI 2010).
 
[Non-Patent Literature 3]
 
       
    
     Peter D. Hoff, “Bilinear Mixed-Effects Models for Dyadic Data,” Journal of the American Statistical Association, 100, 469, pp. 286-295. 
     [Non-Patent Literature 4] 
     
         
         Peter D. Hoff, “Modeling Homophily and Stochastic Equivalence in Symmetric Relational Data,” Advances in Neural Information Processing Systems, 20, pp. 657-664. 
       
    
     SUMMARY 
     The following analysis is given by the present invention. The analytical method of Non-Patent Literature 1 simultaneously predicts a plurality of links between nodes in a network and this is thought to be equivalent to simultaneously treating a plurality of networks. This analytical method, however, has a problem that differences in properties that may exist among different types of links (i.e., to what extent the structures of links between nodes are different between different types of links) are not taken into account. Therefore, when a predetermined amount of structural differences or more exists between different link types, the prediction accuracy may decrease due to the fact that a plurality of link types are simultaneously predicted, compared to a case where each link is individually predicted. 
     Further, the method of Non-Patent Literature 2 has a problem that the data formats to which the method can be applied are restricted since purchasing users must be the same between product categories. 
     Therefore, in order to predict link strengths based on data obtained from a plurality of networks, similarities in the structure of each network and cases where the number of data obtained from each network varies greatly must be taken into consideration. 
     It is an object of the present invention to provide an information processing system, network structure learning device, link strength prediction device, link strength prediction method, and program capable of accurately learning the structures of a plurality of networks and of predicting link strengths assuming that each network has a different structure. 
     According to a first aspect, there is provided an information processing system comprising a first input unit that receives learning data and hyperparameters relating to a plurality of networks; a network structure learning unit that outputs a sample sequence generated from a posterior distribution of each random variable in a probability model representing the structure of a template network that serves as a template for a plurality of networks whose network structures are to be learned, and a sample sequence generated from a posterior distribution of each random variable in a probability model representing the structures of the plurality of networks, using the learning data and the hyperparameters; a learning result output unit that outputs sample sequences from a posterior distribution of each random variable in probability models representing the structures of each of the networks and of the template network; a second input unit that receives an external variable relating to a pair of nodes in a network in which link strengths are to be predicted, and sample sequences outputted from the learning result output unit and generated from a posterior distribution of each random variable in probability models representing the structures of the template network and of each of the networks; a link strength prediction unit that derives a predictive value of the strength of a link specified by the external variable based on the external variable and on sample sequences generated from a posterior probability distribution of each random variable in probability models representing the structures of the template network and of each of the networks; and a prediction result output unit that outputs the strength of a link specified by the external variable. 
     According to a second aspect, there are provided a network structure learning device and link strength prediction device that constitute the information processing system described above. 
     According to a third aspect, there is provided a link strength prediction method comprising separately generating sample sequences from a posterior distribution of each random variable in a probability model representing the structure of a template network that serves as a template for a plurality of networks whose network structures are to be learned, and from a posterior distribution of each random variable in a probability model representing the structures of the plurality of networks, using learning data and hyperparameters relating to the plurality of networks; and deriving a predictive value of the strength of a link specified by an external variable, relating to a pair of nodes in a network in which link strengths are to be predicted, based on the external variable and on sample sequences generated from a posterior probability distribution of each random variable in probability models representing the structures of the template network and of each of the networks. The present method is tied to a particular machine, which is an information processing system that learns the structures of a plurality of networks and that predicts the strength of any link thereof. 
     According to a fourth aspect, there are provided computer programs realizing the network structure learning device and the link strength prediction device that constitute the information processing system described above. Further, these programs can be stored in a computer-readable storage medium. In other words, the present invention can be realized as a computer program product. 
     The meritorious effects of the present invention are summarized as follows. 
     According to the present disclosure, it becomes possible to accurately learn the structures of a plurality of networks and predict link strengths even when the structure of each of these networks is different from each other. 
    
    
     
       BRIEF DESCRIPTION OF DRAWINGS 
         FIG. 1  is a block diagram showing the configuration of an information processing system of a first exemplary embodiment of the present disclosure. 
         FIG. 2  is a flowchart showing the operation of the information processing system of the first exemplary embodiment of the present disclosure. 
         FIG. 3  is a continuation diagram of  FIG. 2 . 
     
    
    
     MODES 
     Exemplary Embodiment 1 
     Next, a first exemplary embodiment of the present disclosure will be described in detail with reference to the drawings. In the present exemplary embodiment below, a link strength is represented by a real number. Further, for each pair of nodes, some external variables representing information such as properties and characteristics between the nodes are observed. 
     In the present exemplary embodiment below, it is assumed that a template network, a template for individual network data, exists and that the template network is generated from a probability distribution. It is further assumed that data relating to each network are generated from a probability distribution based on the template network. With these assumptions above, a hierarchical model in the present exemplary embodiment is assumed.
 
 p ( y,w,s   2   ,b,t|X,κ,λ,β,σ,μ,ν )= p ( y|X,w,s   2 ) p ( w|b,t ) p ( s   2 |κ,λ) p ( b|β,σ ) p ( t|μ,ν )  [Math. 1]
 
 y =( y   1   , . . . , y   G ) y   g =( y   g1   , . . . , y   gN     R   ) t   ,g= 1 , . . . , G   [Math. 2]
 
 X =( X   1   , . . . , X   G ),  X   g =( x   g1   , . . . , x   gN     R   ) t =( x   g(1)   , . . . , x   g(D) ), g= 1 , . . . , G   [Math. 3]
 
 w =( w   1   , . . . , w   G ), w   g =( w   g1   , . . . , w   gD ) t   ,g= 1 , . . . , G   [Math. 4]
 
 b =( b   1   , . . . , b   D ) t   ,t =( t   1   2   , . . . , t   D   2 ) t   [Math. 5]
 
β=(β 1 , . . . , β D ) t , σ=(σ 1   2 , . . . , σ D   2 ) t   [Math 6]
 
     Note that p (A|B, C) denotes the conditional probability distribution of a random variable A with a random variable B given, and C denotes a parameter of this conditional probability distribution. Further, y denotes the strength of a link. X denotes an external  190  variable. N g  denotes the number of links (i.e., pairs of nodes) observed in a gth network. G denotes the number of targeted network data. D denotes the number of external variables. Further, κ, λ, β, σ, μ, and ν are hyperparameters. 
     As the probability distribution on the right side of [Math. 1], the following is assumed. 
     
       
         
           
             
               
                 
                   
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     Note that N (*, λ, Σ) denotes the multidimensional normal distribution of the mean vector n and a covariance matrix Σ. IG(*, φ, ψ) denotes the inverse-gamma distribution of a shape parameter ip and a scale parameter ψ. 
     Here, [Math. 10] and [Math. 11] express probability models of the template network. Further, [Math. 8] and [Math. 9] indicate that the probability model of each of the networks  1  to G is generated from the template network. Further, [Math. 7] indicates that network data for each network is generated from the probability model of each network. 
     With the assumptions above, the conditional probability  215  distribution of each random variable on the right side of [Math. 1] becomes a conjugate distribution, and a conditional posterior probability distribution is derived analytically. Therefore, it becomes possible to predict the posterior distribution of a random variable using a Gibbs sampler. 
     Next, the configuration of an information processing system of the first exemplary embodiment of the present disclosure that realizes link prediction using the hierarchical model above will be described in detail with reference to the drawing.  FIG. 1  is a block diagram illustrating the configuration of the information processing system of the first exemplary embodiment of the present disclosure. 
     With reference to  FIG. 1 , the information processing system that includes a network structure learning device  10  and a link strength prediction device  20  is shown. The network structure learning device  10  is constituted by including a first input unit  101 , a network structure learning unit  102 , and a learning result output unit  103 . Further, a template network structure learning unit  102 - 0  that learns the structure of the template network, and first to Gth network structure learning units  102 - 1  to  102 -G that learn the structure of each network are connected to the network structure learning unit  102 . 
     The link strength prediction device  20  is constituted by including a second input unit  104 , a link strength prediction unit  105 , and a prediction result output unit  106 . Further, first to Gth network link strength prediction units  105 - 1  to  105 -G that predict link strengths for each network are connected to the link strength prediction unit  105 . 
     The first input unit  101  receives the input of learning data (y, X)  245  for learning a network structure and of the hyperparameters η, λ, β, σ, μ, and ν, stores them, and outputs them to the network structure learning unit  102 . 
     The network structure learning unit  102  feeds the entered learning data (y, X) and hyperparameters κ, λ, β, σ, μ, and μ to the template network structure learning unit  102 - 0  and the first to the Gth network structure learning units  102 - 1  to  102 -G. For instance, the number of email exchanges among employees at a corporation or the number of times that employees attended the same meeting can be used as the learning data, and in this case, as a link strength prediction, connections/relationships between employees, which are not obvious (but latent), can be analyzed. Further, examples of external variables described later include whether or not the sex, the position, the department, and the specialized field/skills matches between employees. 
     The template network structure learning unit  102 - 0  and the first to the Gth network structure learning units  102 - 1  to  102 -G respectively generate and store sample sequences based on the posterior probability distribution of each random variable using a Gibbs sampler. Further, the generated sample sequences are outputted to the learning result output unit  103  via the network structure learning unit  102 . 
     The learning result output unit  103  outputs the sample sequence based on the posterior probability distribution of each random variable entered by the network structure learning unit  102  to the outside of the device. 
     The second input unit  104  receives the input of an external  275  variable X* relating to a pair of nodes between which one wants to predict the strength of the link and of the sample sequence from the posterior probability distribution of each random variable, and outputs these to the link strength prediction unit  105 . 
     The link strength prediction unit  105  feeds the entered external variable X* and the sample sequence from the posterior probability distribution of each random variable to the first to the Gth network link strength prediction units  105 - 1  to  105 -G. 
     A gth network link strength prediction unit  105 - g  (g=1, . . . , G) out of the first to the Gth network link strength prediction units  105 - 1  to  105 -G predicts a link strength according to the following [Math. 12] using an average value AVE (w g ) of the sample sequence from the posterior probability distribution of w g . Note that x* gng  on the right  290  side of [Math. 12] denotes an external variable for a pair of nodes between which one wants to predict the strength of the link regarding a gth network. 
     
       
         
           
             
               
                 
                   
                     
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     Note that the example above is merely an example, and as an alternative, a method in which the posterior prediction probability distribution of a link strength is derived from a sample sequence from the posterior probability distribution of each random variable can be used. 
     The link strength predictive value on the left side of [Math. 12] predicted as described above is outputted to the prediction result output unit  106  via the link strength prediction unit  105 . 
     The prediction result output unit  106  outputs the link strength predictive value supplied by the link strength prediction unit  105  to the outside of the device. 
     Further, each unit (processing means) of the network structure learning device  10  and the link strength prediction device  20  shown  FIG. 1  can be realized by a computer that constitutes the network structure learning device  10  and the link strength prediction device  20  using the hardware thereof with a computer program having each processing described above executed. 
     Next, the operation of the present exemplary embodiment will be described in detail with reference to the drawing.  FIGS. 2 and 3  are flowcharts showing the operation of the information processing system of the first exemplary embodiment of the present disclosure. 
     With reference to  FIG. 2 , first the first input unit  101  receives the input of the learning data (y, X) for learning a network structure and of the hyperparameters κ, λ, β, β, σ, μ, and ν supplied from the outside of the device, stores them, and outputs them to the network structure learning unit  102  (step S 101  in  FIG. 2 ). 
     The network structure learning unit  102  generates and stores sample sequences for the entered learning data (y, X) and hyperparameters κ, λ, β, σ, μ, and ν based on the posterior probability distribution of each random variable using a Gibbs sampler (step S 102  in  FIG. 2 ). At this time, the template network structure learning unit  102 - 0  generates and stores sample sequences for b, t (refer to [Math. 10] and [Math. 11]), and the first to the Gth network structure learning units  102 - 1  to  102 -G generate and store sample sequences for w g , s 2  (refer to [Math. 8] and [Math. 9]). 
     The learning result output unit  103  outputs these sample sequences to an external device (step S 103  in  FIG. 2 ). 
     Next, with reference to  FIG. 3 , the second input unit  104  stores the external variable X* (prediction data), supplied from the outside of the device, relating to a pair of nodes between which one wants to predict the strength of the link and the sample sequence from the posterior probability distribution of each random variable, and then outputs these to the link strength prediction unit  105  (step S 104  in  FIG. 3 ). 
     The link strength prediction unit  105  calculates a predictive value of the link strength based on the supplied external variable X* (prediction data) and sample sequence from the posterior probability distribution of each random variable, and stores the predictive value (step S 105  in  FIG. 3 ). 
     The prediction result output unit  106  outputs the stored link strength predictive value to the outside of the device (step S 106  in  FIG. 3 ). 
     As described, according to the present exemplary embodiment, when data of a plurality of networks can be obtained, the accuracy of network structure learning represented by link prediction can be improved by simultaneously learning the data of the plurality of networks. (Here the accuracy improvement means that prediction can be performed with higher accuracy than when the network structure of each network is separately learned.) The reason is that the concept of hierarchical model, a type of statistical model, is utilized. By doing this, when links are predicted in a network, it becomes possible to utilize data information on other networks properly while taking into account similarities between networks. For instance, when the amount of data obtained is greatly different between networks, it becomes possible to compensate the instability of the learning accuracy for a network with a small amount of data by utilizing information on a network with a large amount of data. 
     Further, according to the present exemplary embodiment, parameter values of a hierarchical model can be learned, and these parameter values further provide additional information, such as to what extent the structures are different between networks. 
     Further, as evident from the explanations above, unlike the method of Non-Patent Literature 1, the present exemplary embodiment does not require all nodes to be the same between networks. In other words, since there is the advantage that a node included in a network does not have to be included in another, targeted data formats are not restricted. 
     An exemplary embodiment of the present disclosure has been described above, however, the present disclosure is not limited to the exemplary embodiment described and further modifications, substitutions, and adjustments can be added within the scope of the basic technological concept of the present disclosure. For instance, in the exemplary embodiment above, link strengths are expressed in real numbers and some external variables representing the properties of each node are assumed to have been observed, but it is possible to perform the same learning/prediction by assuming an appropriate linear model in a case where link strengths are expressed in binary numbers indicating the presence of a link, or link strengths are expressed in positive integers, instead real numbers. 
     Further, even in a case where external variables are not observed, it is possible to perform the same learning/prediction by using, for instance, hierarchical models described in Non-Patent Literatures 3 and 4. Note that, in this cases, learning/prediction is performed using the Metropolis-Hastings algorithm after an appropriate transition probability distribution is given, instead of Gibbs Sampling used in the present exemplary embodiment. 
     INDUSTRIAL APPLICABILITY 
     In addition to an analysis of connections between employees, when a new social network (referred to as “A” hereinafter) is launched, by utilizing information on an existing social network (referred to as “B” hereinafter), the present disclosure makes it possible to accurately predict the strengths of connections between users in A from the initial  415  phase of the implementation of A and recommend each user to other users with strong connections. Further, when the strengths of connections between users in A are predicted, the similarities between A and B are automatically determined and the information of B is utilized based on the degree of the similarities. 
     Further, the disclosure of Patent Literature listed above is incorporated herein by reference thereto. 
     It should be noted that other objects, features and aspects of the present invention will become apparent in the entire disclosure and that modifications may be done without departing the gist and scope of the present invention as disclosed herein and claimed as appended herewith. 
     Also it should be noted that any combination of the disclosed and/or claimed elements, matters and/or items may fall under the modifications aforementioned. 
     REFERENCE SIGNS LIST 
     
         
           10 : network structure learning device 
           20 : link strength prediction device 
           101 : first input unit 
           102 : network structure learning unit 
           102 - 0 : template network structure learning unit 
           102 - 1  to  102 -G: first to Gth network structure learning units 
           103 : learning result output unit 
           104 : second input unit 
           105 : link strength prediction unit 
           105 - 1  to  105 -G: first to Gth network link strength prediction units 
           106 : prediction result output unit