基于稳定性优化的多尺度社区发现贪心算法
Multi-scale Community Detection using Stability Optimisation within Greedy Algorithms
摘要：
许多真实的系统可以表示为网络，其分析可以提供关于原始系统的组织的非常有用的信息。在过去的十年里，社区发现受到了广泛的关注，现在是一个活跃的研究领域。最近引入稳定性作为划分质量的新度量。这项工作研究的稳定性作为一个优化标准，利用马尔可夫过程的网络视图，使多尺度社区发现。提出了优化稳定性的算法的几种算法和变化，以及重叠社区的应用。实验表明，该方法能够实现多尺度网络分析的准确性。
英文摘要：
Many real systems can be represented as networks whose analysis can be very informative regarding the original system's organisation. In the past decade community detection received a lot of attention and is now an active field of research. Recently stability was introduced as a new measure for partition quality. This work investigates stability as an optimisation criterion that exploits a Markov process view of networks to enable multi-scale community detection. Several heuristics and variations of an algorithm optimising stability are presented as well as an application to overlapping communities. Experiments show that the method enables accurate multi-scale network analysis.
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