A Coverable Clustering Method Applied to Community Discovery

Abstract

The invention discloses a coverable clustering algorithm applying to community discovery. The coverable clustering algorithm includes firstly, converting acquired original data into 'user-attribute graphs', primarily classifying behaviors in the 'user-attribute graphs' after initializing candidate subgraphs; secondly, computing domination attribute of each candidate subgraph while computing correlation between each user and each candidate subgraph; thirdly, establishing a probability statistical model, computing correlation between each 'user-attribute' pair and the candidate subgraph, iteratively constructing the candidate subgraphs until forming stable and effective candidate subgraph structures; and finally, reasonably classifying the different 'user-attribute' pairs in data according to the constructed candidate subgraphs in the data environment, and discovering key users having various attributes. The coverable clustering algorithm is used for processing content data and relative data simultaneously, and meets requirements on community discovery in real network environments well.

Description

technical field [0001] The invention belongs to the technical field of network information, in particular to a coverable clustering method applied to community discovery based on content data and correlation data. Background technique [0002] With the development of Internet technology, various new network applications emerge in an endless stream, increasingly enriching the virtual social behavior of network users. Furthermore, the relationship between the Internet and users is no longer as simple and direct as the information publishing end and the information receiving end. The Internet has constituted another inseparable world that provides users with production and life - "virtual social network", and Users are also more actively and proactively integrated into this new social platform. For example, users will put forward their own specific views, users will spontaneously form discussion groups, and Internet companies will carry out differentiated marketing for user gr...

AI Technical Summary

Problems solved by technology

[0004] However, the current overlayable community discovery method or clustering method has the following problems: (1) The data application environment of the current overlayable community discovery method or clustering method is single, and can only deal with content data or only relevant data

(2) Generally speaking, the traditional community discovery method only regards the users in the network as the main body in the social network, which potentially leads to the assumption that each user is treated equally, because at this time the weight value of each user all equal

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