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Label propagation is one of the fastest methods for community detection, with a near linear time complexity. It's a local method, where each node has its own label which changes by interaction with its neighbourhood. Unfortunately, this method has two major drawbacks. The first is a bad propagation which can lead to huge communities without sense (giant communities problem). The second is the instability of the method. Each trial of a label propagation algorithm gives rarely the same result. In this paper, we propose algorithms and a study on the label propagation by putting artificial dams on edges in order to avoid bad propagations. We then apply an ensemble learning clustering method based on a frequency matrix in order to stabilize the algorithm.
community detection, label propagation, dams, ensemble learning, core detection.
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