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

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...

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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 discovery algorithms are single-classification algorithms, each member can only belong to one certain social interest circle, and the multi-classification algorithm that can find overlapping and intersecting social interest circles is not perfect; 3) most of the existing algorithms only consider The connection structure of the network, without analyzing the user's network behavior and purpose

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