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Community similarity calculation method and social network cooperation mode discovery method

A similarity calculation and social network technology, applied in the field of network pattern mining, can solve problems such as difficult to set support threshold, algorithm neglect, performance problems, etc.

Inactive Publication Date: 2012-05-16
NAT UNIV OF DEFENSE TECH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Setting the support threshold too high will often cause the algorithm to ignore some specific structures in the network, while setting the support threshold too low will cause performance problems
It is difficult for those skilled in the art to set an appropriate support threshold

Method used

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  • Community similarity calculation method and social network cooperation mode discovery method
  • Community similarity calculation method and social network cooperation mode discovery method
  • Community similarity calculation method and social network cooperation mode discovery method

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Embodiment Construction

[0037] The present invention will be described below in conjunction with the accompanying drawings and specific embodiments.

[0038] Before the method of the present invention is described in detail, the relevant concepts involved in the present invention are firstly defined.

[0039] 1. Representation method of social network

[0040] In real life, there are many types of social networks, such as online dating sites, academic cooperation networks, communication networks, biological protein interaction networks, etc. Although these networks have various forms, they can all be modeled by well-known statistical or measurement methods into a distance-weighted map. In the present invention, the following quadruple can be used to represent the graphical social network: Among them, v∈V represents an individual and an individual set, and the relationship between individuals is represented by an undirected edge e={u, v}∈E, and E represents a set of edges (links); It is used to r...

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Abstract

The invention provides a method for calculating the similarity of communities, comprising the steps of: representing a first community and a second community having similarity to be calculated by a first picture and a second picture, respectively; sequencing all the nodes in the first picture and the second picture according to node authority values in a descending order, respectively; and calculating a similar value for the first picture and the second picture, thus obtaining the degree of the similarity of the communities represented by the first picture and the second picture. The invention also provides a method for discovering a cooperation mode in a social network. The method takes not only structural characteristics, but also the authority distribution of nodes into consideration in the process of discovering the cooperation mode, so that the cooperation mode discovered is more representative.

Description

technical field [0001] The invention relates to network pattern mining, in particular to a method for discovering social network cooperation patterns. Background technique [0002] Social Networking (SN for short) is a relationship network used to represent the interconnection among individuals in society, such as academic cooperation network, online dating network, etc. The popular Facebook and Twitter in real life can be regarded as social networks. A social network can be represented by a matrix method or a graphical method. In the graphic method, a node in the graph is used to represent an individual in the society, the links between nodes are used to represent the connection between individuals, and the size of the link is used to represent the closeness of the connection between individuals. [0003] There is a difference between tight and sparse connections between individuals in a social network and other individuals in the network. The collection of individuals wi...

Claims

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Application Information

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IPC IPC(8): G06F17/30
Inventor 韩毅贾焰方滨兴韩伟红周斌杨树强李爱平
Owner NAT UNIV OF DEFENSE TECH
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