Abstract
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.
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Breve, F., Zhao, L., Quiles, M., Pedrycz, W., Liu, J. (2011). Particle Competition and Cooperation for Uncovering Network Overlap Community Structure. In: Liu, D., Zhang, H., Polycarpou, M., Alippi, C., He, H. (eds) Advances in Neural Networks – ISNN 2011. ISNN 2011. Lecture Notes in Computer Science, vol 6677. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21111-9_48
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DOI: https://doi.org/10.1007/978-3-642-21111-9_48
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21110-2
Online ISBN: 978-3-642-21111-9
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