Evaluating community detection algorithms for progressively evolving graphs
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TLDR
In this paper, the authors compare six algorithms for dynamic community detection in terms of instantaneous and longitudinal similarity with the planted ground truth, smoothness of dynamic partitions, and scalability.Abstract:
Many algorithms have been proposed in the last ten years for the discovery of dynamic communities. However, these methods are seldom compared between themselves. In this article, we propose a generator of dynamic graphs with planted evolving community structure, as a benchmark to compare and evaluate such algorithms. Unlike previously proposed benchmarks, it is able to specify any desired evolving community structure through a descriptive language, and then to generate the corresponding progressively evolving network. We empirically evaluate six existing algorithms for dynamic community detection in terms of instantaneous and longitudinal similarity with the planted ground truth, smoothness of dynamic partitions, and scalability. We notably observe different types of weaknesses depending on their approach to ensure smoothness, namely Glitches, Oversimplification and Identity loss. Although no method arises as a clear winner, we observe clear differences between methods, and we identified the fastest, those yielding the most smoothed or the most accurate solutions at each step.read more
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Dynamic community detection based on the Matthew effect
TL;DR: Wang et al. as discussed by the authors proposed a dynamic community detection algorithm called, Dynamic Community Detection based on the Matthew effect (DCDME), which employs a batch processing method to reveal communities incrementally in each network snapshot.
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Data Compression to Choose a Proper Dynamic Network Representation
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Flow stability for dynamic community detection
TL;DR: In this paper , the authors derive a method based on a dynamical process evolving on the temporal network to discover two sets of communities for a given time interval that accounts for the ordering of edges in forward and backward time.
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