模因图聚类
Memetic Graph Clustering
摘要：
众所周知，图聚类没有单一的最佳策略，这证明了现有方法的合理性。本文提出了一种通用的模因算法VieClus来解决图聚类问题。该算法可以适用于优化不同的目标函数。我们的贡献的一个关键组成部分是自然重组运营商，采用集成聚类以及多层次的技术。最后，我们将这些技术与可扩展的通信协议相结合，产生一个能够在短时间内计算高质量解决方案的系统。我们通过模块化局部搜索实例化了我们的方案，并表明我们的算法使用少量时间成功改进或重现了正在考虑的第10个DIMACS实现~挑战的所有条目。
英文摘要：
It is common knowledge that there is no single best strategy for graph clustering, which justifies a plethora of existing approaches. In this paper, we present a general memetic algorithm, VieClus, to tackle the graph clustering problem. This algorithm can be adapted to optimize different objective functions. A key component of our contribution are natural recombine operators that employ ensemble clusterings as well as multi-level techniques. Lastly, we combine these techniques with a scalable communication protocol, producing a system that is able to compute high-quality solutions in a short amount of time. We instantiate our scheme with local search for modularity and show that our algorithm successfully improves or reproduces all entries of the 10th DIMACS implementation~challenge under consideration using a small amount of time.
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