On the Structural Properties of Social Networks and their Measurement-calibrated Synthetic Counterparts

Marcell Nagy, Roland Molontay
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引用次数: 1

Abstract

Data-driven analysis of large social networks has attracted a great deal of research interest. In this paper, we investigate 120 real social networks and their measurement-calibrated synthetic counterparts generated by four well-known network models. We investigate the structural properties of the networks revealing the correlation profiles of graph metrics across various social domains (friendship networks, communication networks, and collaboration networks). We find that the correlation patterns differ across domains. We identify a nonredundant set of metrics to describe social networks. We study which topological characteristics of real networks the models can or cannot capture. We find that the goodness-of-fit of the network models depends on the domains. Furthermore, while 2K and stochastic block models lack the capability of generating graphs with large diameter and high clustering coefficient at the same time, they can still be used to mimic social networks relatively efficiently.
社会网络的结构性质及其测量校准的合成对应物
大型社交网络的数据驱动分析吸引了大量的研究兴趣。在本文中,我们研究了120个真实的社会网络和它们的测量校准合成对偶由四个著名的网络模型产生。我们研究了网络的结构特性,揭示了不同社会领域(友谊网络、通信网络和协作网络)中图形指标的相关概况。我们发现相关模式在不同的领域是不同的。我们确定了一组非冗余的指标来描述社交网络。我们研究模型可以或不能捕获真实网络的哪些拓扑特征。我们发现网络模型的拟合优度依赖于域。此外,虽然2K和随机块模型缺乏同时生成大直径和高聚类系数图的能力,但它们仍然可以相对有效地用于模拟社会网络。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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