Abstract
Community detection is an efficient tool to analyze large complex networks offering new insights about their structures and functioning. A community is a significant organizational unity formed by nodes with more connections between them. Ant colony algorithms have been used to detect communities on a fast and efficient way. In this work, changes are performed on an ant colony algorithm for community detection by means of modularity optimization. The changes rely on the way an ant moves and on the adopted stopping criteria. To assess the proposed strategy, benchmark networks are studied and preliminary results indicate that the suggested changes make the original algorithm more robust, reaching higher values of modularity of the detected communities.
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de Andrade, L.P., Espíndola, R.P., Ebecken, N.F.F. (2014). Community Detection by an Efficient Ant Colony Approach. In: Andreasen, T., Christiansen, H., Cubero, JC., Raś, Z.W. (eds) Foundations of Intelligent Systems. ISMIS 2014. Lecture Notes in Computer Science(), vol 8502. Springer, Cham. https://doi.org/10.1007/978-3-319-08326-1_1
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DOI: https://doi.org/10.1007/978-3-319-08326-1_1
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