Adaptive K-means algorithm for overlapped graph clustering
EntityUAM. Departamento de Ingeniería Informática
PublisherWorld Scientific Publishing Co. Pte. Ltd.
10.1142/S0129065712500189International Journal of Neural Systems 22.5 (2012): 1250018
ISSN0129-0657 (print); 1793-6462 (online)
Funded byThis work has been partly supported by: Spanish Ministry of Science and Education under project TIN2010-19872 and the grant BES-2011-049875 from the same Ministry.
SubjectsClustering coefficient; Community finding; Genetic algorithms; Graph clustering; Overlapped clustering; Social networks; Informática
NoteElectronic version of an article published as International Journal of Neural Systems 2, 5, 2012, DOI: 10.1142/S0129065712500189 © 2012 copyright World Scientific Publishing Company
Rights© World Scientific Publishing Company 2012
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.
Google Scholar:Bello Orgaz, Gema - Menéndez, Héctor D. - Camacho, David
This item appears in the following Collection(s)
Showing items related by title, author, creator and subject.
Adaptive K-means algorithm for overlapping graph clustering
Using the clustering coefficient to guide a genetic-based communities finding algorithm
A multi-objective genetic graph-based clustering algorithm with memory optimization Menéndez, Héctor D.; Barrero, David F.; Camacho, David