Abstract
To detect groups in networks is an interesting problem with applications in social and security analysis. Many large networks lack a global community organization. In these cases, traditional partitioning algorithms fail to detect a hidden modular structure, assuming a global modular organization. We define a prototype for a simple local-first approach to community discovery, namely the democratic vote of each node for the communities in its ego neighborhood. We create a preliminary test of this intuition against the state-of-the-art community discovery methods, and find that our new method outperforms them in the quality of the obtained groups, evaluated using metadata of two real world networks. We give also the intuition of the incremental nature and the limited time complexity of the proposed algorithm.
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Coscia, M., Giannotti, F., Pedreschi, D. (2012). Towards Democratic Group Detection in Complex Networks. In: Yang, S.J., Greenberg, A.M., Endsley, M. (eds) Social Computing, Behavioral - Cultural Modeling and Prediction. SBP 2012. Lecture Notes in Computer Science, vol 7227. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29047-3_13
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DOI: https://doi.org/10.1007/978-3-642-29047-3_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-29046-6
Online ISBN: 978-3-642-29047-3
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