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Overlapping community detection has already become an interesting problem in data mining and also a useful technique in applications. This underlines the importance of following the lifetime of communities in real graphs. Palla et al. developed a promising method, and analyzed community evolution on two large databases [23]. We have followed their footsteps(More)
The study of infection processes is an important field of science both from the theoretical and the practical point of view, and has many applications. In this paper we focus on the popular Independent Cascade model and its generalization. Unfortunately the exact computation of infection probabilities is a #P-complete problem [8], so one cannot expect fast(More)
In this paper we present preliminary results for a fast parallel adaptation of the well-known k-means clustering algorithm to graphs. We are going to use our method to detect communities in complex networks. For testing purposes we will use the graph generator of Lancichinetti et al., and we are going to compare our method with the OSLOM, CPM, and hub(More)
The applications of infection models like the Linear Threshold or the Domingos-Richardson model requires a graph weighted with infection probabilities. In many real-life applications these probabilities are unknown; therefore a systematic method for the estimation of these probabilities is required. One of the methods proposed to solve this problem, the(More)
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