Theoretical and computational characterizations of interaction mechanisms on Facebook dynamics using a common knowledge model
Theoretical and computational characterizations of interaction mechanisms on Facebook dynamics using a common knowledge model
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DOI：
10.1007/s13278-021-00791-7
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发表时间：
2021-11
影响因子：
2.8
通讯作者：
C. Kuhlman;Gizem Korkmaz;Sujith Ravi;F. Vega-Redondo
中科院分区：
文献类型：
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作者：
C. Kuhlman;Gizem Korkmaz;Sujith Ravi;F. Vega-Redondo
基于网络的交互使代理能够协调并产生集体行动。协调可以促进传染病向网络人群中的大群体传播。在博弈论背景下，协调要求代理彼此共享共同知识。当每个成员都知道其他成员的状态和阈值（偏好）时，群体内就会出现常识，而且至关重要的是，每个成员都知道其他人都拥有这些信息。因此，这些通信网络上的常识和协调模型与基于影响力的单边传染模型（例如 Granovetter 和 Centola 设计的模型）有着根本的不同。此外，这些模型利用不同的机制来驱动传染。我们使用九个社交（媒体）网络评估了通用知识模型的三种机制，该模型可以代表 Facebook 上人群之间基于网络的通信。我们提供的理论结果表明识别网络中所有节点最大双系的棘手性，这是产生共同知识的特征网络结构。模型执行需要 Bicliques。我们还表明，其中一种机制（名为 PD2）主导另一种机制（名为 ND2）。通过模拟，我们在 Facebook 模型中计算了这些网络上的传染传播情况，并证明了不同的机制可以在传播程度和传染传播速度方面产生广泛不同的行为。我们还通过获得传染的节点比例来量化 ND2 和 PD2 机制效果的差异，这取决于网络结构和其他模拟输入。
Web-based interactions enable agents to coordinate and generate collective action. Coordination can facilitate the spread of contagion to large groups within networked populations. In game theoretic contexts, coordination requires that agents share common knowledge about each other. Common knowledge emerges within a group when each member knows the states and the thresholds (preferences) of the other members, and critically, each member knows that everyone else has this information. Hence, these models of common knowledge and coordination on communication networks are fundamentally different from influence-based unilateral contagion models, such as those devised by Granovetter and Centola. Moreover, these models utilize different mechanisms for driving contagion. We evaluate three mechanisms of a common knowledge model that can represent web-based communication among groups of people on Facebook, using nine social (media) networks. We provide theoretical results indicating the intractability in identifying all node-maximal bicliques in a network, which is the characterizing network structure that produces common knowledge. Bicliques are required for model execution. We also show that one of the mechanisms (named PD2) dominates another mechanism (named ND2). Using simulations, we compute the spread of contagion on these networks in the Facebook model and demonstrate that different mechanisms can produce widely varying behaviors in terms of the extent of the spread and the speed of contagion transmission. We also quantify, through the fraction of nodes acquiring contagion, differences in the effects of the ND2 and PD2 mechanisms, which depend on network structure and other simulation inputs.