Online social networks (OSN) have today reached a remarkable capillary diffusion. There are numerous examples of very large platforms people use to communicate and maintain relationships. People also subscribe to several OSNs, e.g., people create accounts on Facebook, Twitter, and so on. This phenomenon leads to online social internetworking (OSI) scenarios where users who subscribe to multiple OSNs are termed as bridges . Unfortunately, several important features make the study of information propagation in an OSI scenario a difficult task, e.g., correlations in both the structural characteristics of OSNs and the bridge interconnections among them, heterogeneity and size of OSNs, activity factors, cross-posting propensity, and so on. In this article, we propose a directed random graph-based model that is amenable to efficient numerical solution to analyze the phenomenon of information propagation in an OSI scenario; in the model development, we take into account heterogeneity and correlations introduced by both topological (correlations among nodes degrees and among bridge distributions) and user-related factors (activity index, cross-posting propensity). We first validate the model predictions against simulations on snapshots of interconnected OSNs in a reference scenario. Subsequently, we exploit the model to show the impact on the information propagation of several characteristics of the reference scenario, i.e., size and complexity of the OSI scenario, degree distribution and overall number of bridges, growth and decline of OSNs in time, and time-varying cross-posting users propensity.

中文翻译：

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互联在线社交网络中的信息扩散模型

在线社交网络 (OSN) 今天已经达到了显着的毛细扩散。人们使用非常大的平台来沟通和维护关系的例子不胜枚举。人们还订阅了多个 OSN，例如，人们在 Facebook、Twitter 等上创建帐户。这种现象导致了在线社交网络互联 (OSI) 场景，其中订阅多个 OSN 的用户被称为桥梁. 不幸的是，一些重要的特性使得在 OSI 场景中的信息传播研究成为一项艰巨的任务，例如，OSN 的结构特征和它们之间的桥梁互连、OSN 的异质性和大小、活动因素、交叉发布倾向、等等。在本文中，我们提出了一种基于有向随机图的模型，该模型可以通过有效的数值解来分析 OSI 场景中的信息传播现象；在模型开发中，我们考虑了拓扑（节点度和桥分布之间的相关性）和用户相关因素（活动指数、交叉发布倾向）引入的异质性和相关性。我们首先根据参考场景中互连 OSN 快照的模拟验证模型预测。随后，我们利用该模型展示了参考场景的几个特征对信息传播的影响，即 OSI 场景的规模和复杂性、桥梁的度分布和总数、OSN 在时间上的增长和下降以及时间-改变交叉发布用户的倾向。