Int J Neural Syst - Adaptive k-means algorithm for overlapped graph clustering.

Tópicos

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Resumo

The graph clustering problem has become highly relevant due to the growing interest of several research communities in social networks and their possible applications. Overlapped graph clustering algorithms try to find subsets of nodes that can belong to different clusters. In social network-based applications it is quite usual for a node of the network to belong to different groups, or communities, in the graph. Therefore, algorithms trying to discover, or analyze, the behavior of these networks needed to handle this feature, detecting and identifying the overlapped nodes. This paper shows a soft clustering approach based on a genetic algorithm where a new encoding is designed to achieve two main goals: first, the automatic adaptation of the number of communities that can be detected and second, the definition of several fitness functions that guide the searching process using some measures extracted from graph theory. Finally, our approach has been experimentally tested using the Eurovision contest dataset, a well-known social-based data network, to show how overlapped communities can be found using our method.

Resumo Limpo

graph cluster problem becom high relev due grow interest sever research communiti social network possibl applic overlap graph cluster algorithm tri find subset node can belong differ cluster social networkbas applic quit usual node network belong differ group communiti graph therefor algorithm tri discov analyz behavior network need handl featur detect identifi overlap node paper show soft cluster approach base genet algorithm new encod design achiev two main goal first automat adapt number communiti can detect second definit sever fit function guid search process use measur extract graph theori final approach experiment test use eurovis contest dataset wellknown socialbas data network show overlap communiti can found use method

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