In this big data era, more and more social activities are digitized thereby becoming traceable, and thus the studies of social networks attract increasing attention from academia. It is widely believed that social networks play important role in the process of information diffusion. However, the opposite question, i.e., how does information diffusion process rebuild social networks, has been largely ignored. In this paper, we propose a new framework for understanding this reversing effect. Specifically, we first introduce a novel information diffusion model on social networks, by considering two types of individuals, i.e., smart and normal individuals, and two kinds of messages, true and false messages. Since social networks consist of human individuals, who have self-learning ability, in such a way that the trust of an individual to one of its neighbors increases (or decreases) if this individual received a true (or false) message from that neighbor. Based on such a simple self-learning mechanism, we prove that a social network can indeed become smarter, in terms of better distinguishing the true message from the false one. Moreover, we observe the emergence of social stratification based on the new model, i.e., the true messages initially posted by an individual closer to the smart one can be forwarded by more others, which is enhanced by the self-learning mechanism. We also find the crossover advantage, i.e., interconnection between two chain networks can make the related individuals possessing higher social influences, i.e., their messages can be forwarded by relatively more others. We obtained these results theoretically and validated them by simulations, which help better understand the reciprocity between social networks and information diffusion.

中文翻译：

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智能社交网络的自学习信息扩散模型

在这个大数据时代，越来越多的社交活动被数字化从而变得可追溯，因此社交网络的研究越来越受到学术界的关注。人们普遍认为，社交网络在信息传播过程中起着重要作用。然而，相反的问题，即信息传播过程如何重建社交网络，却在很大程度上被忽视了。在本文中，我们提出了一个新的框架来理解这种逆转效应。具体来说，我们首先通过考虑两种类型的个体，即聪明和正常个体，以及两种消息，真实和虚假消息，在社交网络上引入一种新的信息传播模型。由于社交网络由具有自我学习能力的人类个体组成，以这样的方式，如果该个人从该邻居收到真（或假）消息，则该个人对其邻居之一的信任会增加（或减少）。基于这样一个简单的自学习机制，我们证明了社交网络确实可以变得更智能，在更好地区分真实信息和虚假信息方面。此外，我们观察到基于新模型的社会分层的出现，即最初由更接近聪明的个人发布的真实消息可以被更多的其他人转发，这是通过自学习机制增强的。我们还发现了交叉优势，即两个链网络之间的互连可以使相关个人具有更高的社会影响力，即他们的消息可以被相对更多的人转发。