Semi-supervised community discovery method based on maximum clique

Abstract

The invention discloses a semi-supervised community discovery method based on maximum clique, comprising the following steps: selecting an initial node based on node degree, looking for each independent clique structure, and forming a clique set in a network; selecting k biggest cliques as initial core communities, and marking the nodes in the initial core communities, wherein the nodes in the same community are marked in the same way, and the nodes in different communities are marked differently; respectively adding MustLink and CannotLink relations according to whether the nodes are marked in the same way or differently, and calculating the membership degree between neighbor nodes; and finally, carrying out reasoning based on the properties of the MustLink and CannotLink relations to get more MustLink and CannotLink relations, and determining the membership of the nodes based on the maximum membership degree between adjacent nodes, thus completing community division. The method can help analyze the structure of a network and find features hidden in the network, and is of important practical significance.

Description

technical field [0001] The invention relates to the field of complex network community discovery, in particular to a semi-supervised community discovery method based on maximum clique. Background technique [0002] With the vigorous development of the Internet, network microblogging and large-scale online websites have become popular rapidly, thus forming many large-scale social networks on the Internet, and community division of these large-scale social networks can help understand the hobbies and consumption concepts of different groups of people, etc. . Community discovery is widely used in our daily life. For example, our common application examples of community discovery include: public opinion analysis and early warning, search and discovery of target groups, analysis of market consumer groups and construction of a reasonable business environment, etc. Through further analysis and research on the community, we can discover the unknown information hidden behind the soc...

AI Technical Summary

Problems solved by technology

The effect of semi-supervised clustering depends on the quality of the marked nodes. If the marked nodes provide enough information, the clustering effect is better; if less information is provided, the final clustering effect is not necessarily better than that of unsupervised learning. The clustering effect is good, and may even be worse than the clustering effect of unsupervised learning

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