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• W2556160597 abstract "Community and cluster detection is a popular field of social network analysis. Most algorithms focus on static graphs or series of snapshots. In this paper we present an hierarchical algorithm, which detects communities in dynamic graphs. The method is based on the shortest paths to high-connected nodes, so called hubs. Due to local message passing, we can update the clustering results with low computational effort. The used hierarchy allows to process the community detection without setting any parameters before. After processing it provides different cluster levels based on the selected threshold. The presented algorithm is compared with the Louvain method on large-scale real-world datasets with given community structure. The detected community structure is compared to the given with NMI scores. The advantage of the algorithm is the good performance in dynamic scenarios." @default.
• W2556160597 created "2016-11-30" @default.
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• W2556160597 date "2016-08-18" @default.
• W2556160597 modified "2023-10-16" @default.
• W2556160597 title "Detecting overlapping community hierarchies in dynamic graphs" @default.
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• W2556160597 doi "https://doi.org/10.5555/3192424.3192626" @default.
• W2556160597 hasPublicationYear "2016" @default.
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