保持边缘连接的合成网络

Lahari Anne, The-Anh Vu-Le, Minhyuk Park, Tandy Warnow, George Chacko
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引用次数: 0

摘要

由于真实世界网络中的真实群落很少为人所知,因此具有基本事实的合成网络对于评估群落检测方法的性能非常有价值。在现有的合成网络生成工具中,随机块模型(SBM)能生成具有地面实况聚类的网络,这些聚类能很好地近似真实世界网络和聚类的输入参数。然而,我们发现,即使给定的参数来自所有簇都相互连接的聚类,随机块模型也能生成断开的地面实况簇。在这里,我们介绍了现实簇连接模拟器(RECCS),它是一种修改 SBM 合成网络的技术,可以在簇间边缘连接性方面提高与给定聚类真实世界网络的拟合度,同时保持与其他网络和簇统计数据的良好拟合度。通过使用规模高达 1,390 万节点的真实世界网络,我们发现 RECCS 应用于随机块模型后,合成网络与未修改的 SBM 相比,能更好地拟合聚类边缘连通性,同时在其他网络和聚类参数方面提供与未修改 SBM 大致相同的拟合质量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Synthetic Networks That Preserve Edge Connectivity
Since true communities within real-world networks are rarely known, synthetic networks with planted ground truths are valuable for evaluating the performance of community detection methods. Of the synthetic network generation tools available, Stochastic Block Models (SBMs) produce networks with ground truth clusters that well approximate input parameters from real-world networks and clusterings. However, we show that SBMs can produce disconnected ground truth clusters, even when given parameters from clusterings where all clusters are connected. Here we describe the REalistic Cluster Connectivity Simulator (RECCS), a technique that modifies an SBM synthetic network to improve the fit to a given clustered real-world network with respect to edge connectivity within clusters, while maintaining the good fit with respect to other network and cluster statistics. Using real-world networks up to 13.9 million nodes in size, we show that RECCS, applied to stochastic block models, results in synthetic networks that have a better fit to cluster edge connectivity than unmodified SBMs, while providing roughly the same quality fit for other network and clustering parameters as unmodified SBMs.
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