Improved density peak clustering-based social network community discovery method

Abstract

The invention discloses an improved density peak clustering-based social network community discovery method. The method comprises the following steps of: firstly calculating two indexes for each userin a network, wherein the two indexes comprise local densities and relative distances, the local densities are calculated by adoption of Gaussian kernel density estimation, and the relative distancesrepresent a distances between users and points which are greater than the users in the aspect of density and which are close to the users; selecting a point which has a large local density and relatively large relative distance as a community center on the basis of Gaussian distribution, and distributing the residual non-center points to communities of points which are greater than the non-centerpoints in the aspect of density and which are closest to the non-center points; and finally, measuring distance between every two communities on the basis of combination factors, wherein the communities, the combination factors of which are greater than a given threshold value, are combined into one community. Compared with the prior art, the method is capable of discovering spherical and non-spherical community structures in social networks at the same time, so that fewer parameters are needed under the premise of obtaining relatively high correctness and then the problem of clustering communities with any shapes is solved.

Description

technical field [0001] The invention belongs to the technical field of data mining, and more specifically relates to a method for discovering social network communities based on improved density peak clustering. Background technique [0002] In the era of Web2.0, the network space positioning technology tends to be mature, which leads to the rapid development of location-based social networks (LBSNs), such as Foursqure, Dianping, etc. The communication between people is more convenient and fast, and the community of social networks is becoming more and more obvious. For a given set of data objects, the goal of cluster analysis is to divide it into several non-empty subsets, each of which will be regarded as a community, so that the objects in the community are very similar to each other, and the objects in different communities are similar to each other. objects are quite different from each other. The cluster analysis of community structure in social networks has become a ...

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Problems solved by technology

[0005] In view of the defects of the prior art, the purpose of the present invention is to solve the technical problems of poor clustering effect of non-spherical communities in the prior art and the need to manually select the community center

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