A mixed graph model for community detection. (1st January 2012)

• Record Type: Journal Article
• Authors: Keszler, Anita; Szirányi, Tamás
• Is Part Of: International journal of intelligent information and database systems. Volume 6:Number 5(2012)
• Page Start: 479
• Page End: 494
• Publication Date: 2012-01-01
• Subjects: community detection -- clustering -- noise tolerance -- dense subgraph mining -- social networks -- incomplete data; Database management -- Computer programs -- Periodicals; Information retrieval -- Computer programs -- Periodicals; Information storage and retrieval systems -- Computer programs -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Intelligent agents (Computer software) -- Periodicals; 006.33
• Languages: English
• ISSNs: 1751-5858

Abstract

A mixed graph theoretic model is proposed for finding communities in a social network. Information on the habits (shopping habits, free time activities) is considered to be known at least for part of the society. The presented model is based on applying parallelly a standard and a bipartite graph. Compared to previous methods, the introduced algorithm has the advantage of noise-tolerance and is suitable independently of the size of the clusters in the graph. Clusters in the dataset tend to form dense subgraphs in both graph models. The idea is to speed up cluster core mining by a modified MST algorithm. Noise in the dataset is defined as missing information on a person's habits. Clustering noisy data is done by using a bipartite graph and fuzzy membership functions. The proposed algorithm can be used for predicting the missing data estimated on the available information patterns. The presented mixed graph model might also be used for image processing tasks.