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Method and device for predicting social network links based on cooperative fusion theory

A social network and link prediction technology, applied in the field of large-scale social network analysis, can solve the problems of low accuracy and precision, lack of global consistency, etc.

Inactive Publication Date: 2015-07-08
TSINGHUA UNIV +1
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The local prediction method has low accuracy and precision and lacks global consistency; while the global prediction method needs to calculate various parameters of the correlation matrix, which is more accurate than a small network, but compared to a large-scale (million points ) social network is helpless, in response to this situation, some scholars have proposed a random block model that compromises the two processing methods, such as the local path index (Local Path Index, LP) algorithm, local random walk (Local Random Walk) , referred to as LRW) algorithm, but essentially still need to find the correlation matrix of the block, the network needs to be approximated in the process of block, changing the inherent nature of the original network

Method used

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  • Method and device for predicting social network links based on cooperative fusion theory
  • Method and device for predicting social network links based on cooperative fusion theory
  • Method and device for predicting social network links based on cooperative fusion theory

Examples

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no. 1 example

[0087] figure 1 It shows a schematic flow diagram of the social network link prediction method based on the cooperative fusion principle provided by the first embodiment of the present invention, as shown in figure 1 As shown, the social network link prediction method based on the cooperative fusion principle in this embodiment is as follows.

[0088] 101. Obtain the intersection A of the nearest neighbor sets Γ(a), Γ(b) of the node pair (a, b) in the social network.

[0089] In a specific application, the intersection A described in this embodiment is calculated by the first formula,

[0090] The first formula is:

[0091] A=Γ(a)∩Γ(b),

[0092] Among them, Γ(a) is the nearest neighbor set of node a in the social network, and Γ(b) is the nearest neighbor set of node b in the social network.

[0093] 102. Obtain the node A in the intersection A i The likelihood function Pl(A i ) and trust function Bel(A i ).

[0094] In a specific application, the likelihood function Pl...

no. 2 example

[0130] In this embodiment, link prediction is performed on a social network G(V, E) (see Table 1, without considering repeated communication) composed of 49 call information of 30 employees during the working period of a certain enterprise on July 16, 2014 and 17. instance, Figure 4 A schematic diagram showing the social network structure of the data in this embodiment. The social network link prediction method based on the cooperative fusion principle of this embodiment is described in the following steps 201-204.

[0131] Table 1

[0132]

[0133]

[0134] 201. Obtain the nearest neighbor set Γ(a), Γ(b) of the node pair (a, b) in the social network composed of 49 call information of 30 employees during the working period of an enterprise on July 16 and 17, 2014 The intersection of A.

[0135] In a specific application, it can be calculated that:

[0136] Γ(a)={n0,n2,n5,n6,n15,n18,n23,},

[0137] Γ(b)={n2,n11,n15,n18,n20,n23,n26,n27};

[0138] The nearest neighbo...

no. 3 example

[0163] Figure 8 A schematic structural diagram of a social network link prediction device based on the collaborative fusion principle provided by the third embodiment of the present invention is shown, as shown in Figure 8 As shown, the social network link prediction device based on the cooperative fusion principle in this embodiment includes: a first acquisition module 81, a second acquisition module 82, a third acquisition module 83 and a mining module 84;

[0164] The first obtaining module 81 is used to obtain the intersection A of the nearest neighbor set Γ(a), Γ(b) of the node pair (a, b) in the social network;

[0165] The second obtaining module 82 is used to obtain the node A in the intersection A i The likelihood function Pl(A i ) and trust function Bel(A i );

[0166] The third acquisition module 83 is used to use the likelihood function Pl(A i ) and trust function Bel(A i ) for collaborative fusion to obtain the connection degree p(a,b) and reliability d(a,...

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Abstract

The invention provides a method and device for predicting social network links based on cooperative fusion principles. The method comprises the steps: acquiring a set A of nearest neighbor sets T(a), T(b) of a node pair (a, b) in a social network; acquiring a likelihood function P1 (Ai) and a trusting function Be1 (Ai) of a node Ai in the set A; cooperatively fusing the likelihood function P1 (Ai) and the trusting function Be1 (Ai) and acquiring connectivity p (a, b) and reliability d (a, b) of the node pair (a, b); acquiring missing edge judging rules of a hidden link in the social network and excavating a hidden relation of the social network. The above method is applicable to the computing requirement of the large-scale social network; while the order of magnitudes of the computing complexity is not changed, accuracy of predicting results can be improved greatly; the predicting link scale can be adjusted according to error tolerance, thus precise control for predicting large-scale social network links is realized.

Description

technical field [0001] The present invention relates to the technical field of large-scale social network analysis, and in particular, to a method and device for predicting social network links based on the principle of collaborative fusion. Background technique [0002] In recent years, with the development of the application field of data analysis, social network analysis based on probability theory and communication dynamics has been developed to a certain extent, and it has been applied to some social relationship analysis and social platforms, and has gained huge application value. However, the continuous collection of social network data is limited, and there are a large number of missing and noisy data in the collected data, which seriously affects the reliability and accuracy of the analysis results. [0003] There are two link prediction methods in related fields. One is local similarity index. Scholars in this field have proposed a series of similarity measurement ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/30G06Q10/04G06Q50/00
Inventor 钟飞魏琳东黄永峰王烨张潮
Owner TSINGHUA UNIV
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