具有置信阈值的意见动态：Axelrod模型的替代
Opinion dynamics with confidence threshold: an alternative to the Axelrod model
摘要：
选举模型和阿克塞尔罗德模型是描述网络上意见传播的两个主要随机过程。前者包括社会影响，即个体在互动时变得更相似的趋势，而后者也解释了同质性，即与更相似的个体更频繁地互动的趋势。Axelrod模型在过去的十年中基于数值模拟得到了广泛的研究。相比之下，我们给出了严格的分析结果的概括的选举模型，是密切相关的阿克塞尔罗德模型，因为它结合了社会影响力和信心阈值，这是仿照有点类似于同质性。网络的每一个顶点，用一个有限连通图表示，都由一个观点来表征，并且可以与它的邻顶点相互作用。与选举模型类似，交互作用导致两个交互顶点之间的一致性--社会影响力--但与选举模型不同的是，当且仅当顶点的观点在一定距离内时，交互作用才会发生--置信阈值。在一个确定性的静态方法，我们首先给出下限和上界的最大数量的意见，可以支持的网络作为一个函数的置信度阈值和各种特性的图。在平衡共存的意见的数量，然后调查的概率动态方法的随机过程从随机配置.
英文摘要：
The voter model and the Axelrod model are two of the main stochastic processes that describe the spread of opinions on networks. The former includes social influence, the tendency of individuals to become more similar when they interact, while the latter also accounts for homophily, the tendency to interact more frequently with individuals which are more similar. The Axelrod model has been extensively studied during the past ten years based on numerical simulations. In contrast, we give rigorous analytical results for a generalization of the voter model that is closely related to the Axelrod model as it combines social influence and confidence threshold, which is modeled somewhat similarly to homophily. Each vertex of the network, represented by a finite connected graph, is characterized by an opinion and may interact with its adjacent vertices. Like the voter model, an interaction results in an agreement between both interacting vertices -- social influence -- but unlike the voter model, an interaction takes place if and only if the vertices' opinions are within a certain distance -- confidence threshold. In a deterministic static approach, we first give lower and upper bounds for the maximum number of opinions that can be supported by the network as a function of the confidence threshold and various characteristics of the graph. The number of opinions coexisting at equilibrium is then investigated in a probabilistic dynamic approach for the stochastic process starting from a random configuration ...
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
