Identification and classification of overlap nodes in communities is an important topic in data mining. In this paper, a new graph-based (network-based) semi-supervised learning method is proposed. It is based on competition and cooperation among walking particles in the network to uncover overlap nodes, i.e., the algorithm can output continuous-valued output (soft labels), which corresponds to the levels of membership from the nodes to each of the communities. Computer simulations carried out for synthetic and real-world data sets provide a numeric quantification of the performance of the method. © 2011 Springer-Verlag.
CITATION STYLE
Breve, F., Zhao, L., Quiles, M., Pedrycz, W., & Liu, J. (2011). Particle competition and cooperation for uncovering network overlap community structure. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6677 LNCS, pp. 426–433). https://doi.org/10.1007/978-3-642-21111-9_48
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