Abstract
Label propagation is one of the fastest methods for community detection, with a near linear time complexity. It acts locally. Each node interacts with neighbours to change its own label by a majority vote. But this method has three major drawbacks: (i) it can lead to huge communities without sense called also monster communities, (ii) it is unstable, and (iii) it is unable to detect overlapping communities.
In this paper, we suggest new techniques that improve considerably the basic technique by using an existing core detection label propagation technique. It is then possible to detect overlapping communities through a belonging function which qualifies the belonging degree of nodes to several communities.
Nodes are assigned and replicated by the function a number of times to communities which are found automatically. User may also interact with the technique by imposing and freezing the number of communities a node may belong to. A comparative analysis will be done.
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Attal, JP., Malek, M., Zolghadri, M. (2016). Overlapping Community Detection Using Core Label Propagation and Belonging Function. In: Hirose, A., Ozawa, S., Doya, K., Ikeda, K., Lee, M., Liu, D. (eds) Neural Information Processing. ICONIP 2016. Lecture Notes in Computer Science(), vol 9949. Springer, Cham. https://doi.org/10.1007/978-3-319-46675-0_19
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DOI: https://doi.org/10.1007/978-3-319-46675-0_19
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