Casiraghi Giona, Andres Georges
Chair of Systems Design, ETH Zurich, Weinbergstrasse 56/58, Zurich 8092, Switzerland.
PNAS Nexus. 2025 Jan 9;4(1):pgaf001. doi: 10.1093/pnasnexus/pgaf001. eCollection 2025 Jan.
Real-world networks are sparse. As we show in this article, even when a large number of interactions is observed, most node pairs remain disconnected. We demonstrate that classical multiedge network models, such as the , configuration models, and stochastic block models, fail to accurately capture this phenomenon. To mitigate this issue, zero-inflation must be integrated into these traditional models. Through zero-inflation, we incorporate a mechanism that accounts for the excess number of zeroes (disconnected pairs) observed in empirical data. By performing an analysis on all the datasets from the Sociopatterns repository, we illustrate how zero-inflated models more accurately reflect the sparsity and heavy-tailed edge count distributions observed in empirical data. Our findings underscore that failing to account for these ubiquitous properties in real-world networks inadvertently leads to biased models that do not accurately represent complex systems and their dynamics.
现实世界中的网络是稀疏的。正如我们在本文中所展示的，即使观察到大量的交互，大多数节点对仍然是不相连的。我们证明，经典的多边缘网络模型，如 、配置模型和随机块模型，无法准确捕捉这一现象。为了缓解这个问题，必须将零膨胀纳入这些传统模型。通过零膨胀，我们纳入了一种机制，该机制解释了在经验数据中观察到的过多零值（不相连的对）。通过对来自Sociopatterns存储库的所有数据集进行分析，我们说明了零膨胀模型如何更准确地反映经验数据中观察到的稀疏性和重尾边计数分布。我们的研究结果强调，未能考虑现实世界网络中这些普遍存在的属性会无意中导致有偏差的模型，这些模型无法准确表示复杂系统及其动态。
