基于环族模的网络聚类和社区发现
Network clustering and community detection using modulus of families of loops
摘要：
我们研究的结构，在网络中的循环使用的概念，模的循环家庭。我们引入了一个新的网络聚类措施，通过量化（简单）循环的家庭的丰富性。Modulus试图通过最佳地扩展预期的链路使用来最小化环路之间的预期重叠。我们提出加权网络使用这些预期的链接使用，以改善经典的社区发现算法。我们表明，所提出的方法提高了某些算法的性能，如频谱划分和模块化最大化算法，在标准的基准测试。
英文摘要：
We study the structure of loops in networks using the notion of modulus of loop families. We introduce a new measure of network clustering by quantifying the richness of families of (simple) loops. Modulus tries to minimize the expected overlap among loops by spreading the expected link-usage optimally. We propose weighting networks using these expected link-usages to improve classical community detection algorithms. We show that the proposed method enhances the performance of certain algorithms, such as spectral partitioning and modularity maximization heuristics, on standard benchmarks.
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