标题： 亲和路径和社会网络中的信息扩散 显示英文标题

标题： Affinity Paths and Information Diffusion in Social Networks

摘要： 在社交网络中信息扩散的广泛兴趣已经产生了大量社会动力学模型。 其中大多数模型使用理论假设来解释它们的扩散机制，而少数基于实证的模型则对其测量值在不同内容的许多消息上进行平均。 我们的实证研究跟踪了一个不变的病毒式营销消息的逐步电子邮件传播，深入研究了内容影响，并发现了新的显著特征。 传播级联的拓扑结构和动态表现出不同于承载该消息的电子邮件网络的模式。 它们的不相连、低传递性、类似树状的级联显示出节点转发消息的概率与其目标邻居的平均数量之间的正相关性，并且随着传播路径长度的增长，参与者的参与度增加。 这些之前未描述过且任何现有的信息扩散模型都无法复制的模式，如果参与者仅根据其网络邻居对消息内容的亲和力的局部知识做出传递决策，则可以得到解释。 我们通过一个简化的基于代理的模型证明了这种机制的合理性，该模型能够复制真实信息扩散级联中观察到的\emph{亲和路径}。 显示英文摘要

摘要： Widespread interest in the diffusion of information through social networks has produced a large number of Social Dynamics models. A majority of them use theoretical hypothesis to explain their diffusion mechanisms while the few empirically based ones average out their measures over many messages of different content. Our empirical research tracking the step-by-step email propagation of an invariable viral marketing message delves into the content impact and has discovered new and striking features. The topology and dynamics of the propagation cascades display patterns not inherited from the email networks carrying the message. Their disconnected, low transitivity, tree-like cascades present positive correlation between their nodes probability to forward the message and the average number of neighbors they target and show increased participants' involvement as the propagation paths length grows. Such patterns not described before, nor replicated by any of the existing models of information diffusion, can be explained if participants make their pass-along decisions based uniquely on local knowledge of their network neighbors affinity with the message content. We prove the plausibility of such mechanism through a stylized, agent-based model that replicates the \emph{Affinity Paths} observed in real information diffusion cascades.