Microblog interestingness circle mining method based on intimacy degree and influence power and microblog interestingness circle mining device based on intimacy degree and influence power

A mining device and influence technology, applied in the field of microblog social interest circle mining method and device field, can solve the problems of imperfect multi-classification algorithm, lack of user network behavior and purpose analysis, lack of social interest circle discovery algorithm, etc. The effect of wide application prospects and value

Inactive Publication Date: 2016-01-20
TIANJIN UNIV
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] Analyzing the existing social interest circle discovery algorithms, it is not difficult to find three problems: 1) the existing social interest circle discovery methods are mostly oriented to the global network, and the local network social interest circle discovery algorithm centered on a certain node is relatively lacking; 2) ) Most of the existing social interest circle discov

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  • Microblog interestingness circle mining method based on intimacy degree and influence power and microblog interestingness circle mining device based on intimacy degree and influence power
  • Microblog interestingness circle mining method based on intimacy degree and influence power and microblog interestingness circle mining device based on intimacy degree and influence power
  • Microblog interestingness circle mining method based on intimacy degree and influence power and microblog interestingness circle mining device based on intimacy degree and influence power

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Experimental program
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Embodiment 1

[0055] A microblog social interest circle mining method based on intimacy and influence, see figure 1 , the mining method includes the following steps:

[0056] 101: Based on the KCC algorithm, the social interest circle seed discovery is performed on the first-level interaction graph of the central user;

[0057] In the first-level interaction graph constructed based on the user's attention relationship, several K-clique communities are mined out using the K-clique-community algorithm, which is the KCC (K-clique-community) algorithm, as the seeds of social interest circles. The seed of a social interest circle can be interpreted as a set of smaller complete subgraphs that share nodes with each other. In the mathematical literature, these complete subgraphs are called K cliques, and the nodes in K delegations Quantity, the size of the clique. The K-clique complete subgraph in the network is called the K-clique community.

[0058] Wherein, the KCC algorithm is well known to th...

Embodiment 2

[0069] The scheme in embodiment 1 is described in detail below in conjunction with specific calculation formulas and examples, see below for details:

[0070] 201: Mining social interest circle seeds based on group theory;

[0071] In the current social interest circle discovery methods, some methods first dig out the core of the social interest circle, and then expand the core of the social interest circle to form the final division result of the social interest circle, which is easy to form the iceberg island problem, because some selected The core of social interest circles may belong to the same social interest circle. In order to avoid icebergs and isolated islands, the embodiment of the present invention uses the K group community KCC method to directly process the largest group in the first-level map of microblog users.

[0072] Before the discovery of social interest circle seeds, define two concepts: friend set and first-level interaction graph.

[0073] Definition ...

Embodiment 3

[0133] Below in conjunction with concrete test, the scheme in embodiment 1 and 2 is carried out feasibility verification, see the following description for details:

[0134] f 1 -Measure is calculated using the accuracy rate and recall rate. The accuracy rate measures the accuracy rate of the mining results of the proposed algorithm, and the recall rate measures the recall rate of the mining results of the proposed algorithm. F 1 -Measure is a comprehensive evaluation index of the two.

[0135] The MAP value is obtained by calculating the average value of the AP value of each social interest circle. The AP value can reflect the location information of the correctly classified members in the social interest circle, that is, if the correctly classified members are in the higher position in the social interest circle location, the higher the AP value of the circle. f 1 - The specific formulas of Measure and MAP are shown in formula (14) to formula (18).

[0136]

[0137] ...

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Abstract

The invention discloses a microblog interestingness circle mining method based on intimacy degree and influence power and a microblog interestingness circle mining device based on the intimacy degree and the influence power. The mining method comprises the following steps of discovering a social intercourse interestingness circle seed on a center user first-stage interaction diagram on the basis of a KCC (K-Clique-Community) algorithm; expanding the social intercourse interestingness circle seed according to the intimacy degree among nodes; expanding a PageRank algorithm through the user microblog interesting similarity degree, and calculating the user influence power; expanding the expanded social intercourse interestingness circle seed again through the user influence power; and automatically marking the discovered social intercourse interestingness circle through the re-expanded social intercourse interestingness circle. The mining device comprises a discovering module, a first expansion module, a calculation module, a second expansion module and a marking module. The social intercourse interestingness circle obtained through mining by the method and the device can be applied to various fields such as interestingness modeling, cooperated recommendation, personalized searching and ranking, precise advertisement putting and knowledge mapping; and wide application prospects and values are realized.

Description

technical field [0001] The invention relates to the fields of data mining, natural language processing and information retrieval, in particular to a method and device for mining microblog social interest circles based on intimacy and influence. Background technique [0002] Community discovery or social interest circle discovery algorithms can be roughly divided into traditional data mining clustering algorithms, segmentation-based algorithms, modularity-based optimization algorithms, dynamic model-based algorithms, spectral mapping-based algorithms, etc. For example: Kernighan-Lin algorithm, spectral dichotomy, splitting algorithm based on edge betweenness measure, Guimera-Amaral classical algorithm and agglomeration algorithm based on similarity measure. [0003] These traditional social interest circle discovery algorithms are essentially static analysis algorithms, which are difficult to adapt to the complex and changeable structure of the current real social network; T...

Claims

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Application Information

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IPC IPC(8): G06F17/30G06Q50/00
CPCG06Q50/01G06F16/9535
Inventor 喻梅侯德俊徐天一王建荣于瑞国缑小路
Owner TIANJIN UNIV
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