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On a Triadic Approach to Connect Microstructural Properties to Social Macrostructural Patterns

机译:关于将微观结构特性与社会宏观结构模式联系起来的三元论方法

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Social macrostructures, such as structural balance, ranked clusters and transitivity, are of great importance on account of their abilities to reflect the underlying social psychological processes about the formation and evolution of relationships among people. Here we present a detailed study on examining the existence and evolution of social macrostructures in an empirical online social network, and exploring how they can be explained by network micro structural properties, i.e. nodal in degree and out degree and dyadic feature. We establish the micro-macro linkage by analyzing the network triadic patterns. Based on a novel clustering coefficient based network sampling approach, we show that the distribution of observed triad census in our data is low dimensional and can be greatly explained by network dyadic properties. In a time series analysis, we observe that our network exhibits strong tendencies towards balanced, transitive and clustered social macrostructure given the nodal and dyadic characteristics. Our findings supplement the studies on structural properties of online social network by providing more insights on the relation between network macrostructures and the micro-level social processes that result in them. And they form the basis to understand better how online social media systems change the information and communication fabric of our society.
机译:社会宏观结构,例如结构平衡,排名集群和及物性,因其能够反映有关人际关系形成和演变的潜在社会心理过程而具有重要意义。在这里,我们提供详细的研究,以检查经验性在线社交网络中社会宏观结构的存在和演变,并探索如何用网络微观结构特性(即程度和程度以及结点特征的节点)来解释它们。我们通过分析网络三重模式来建立微宏链接。基于一种新颖的基于聚类系数的网络采样方法,我们表明,在我们的数据中观察到的三合会人口普查的分布是低维的,并且可以通过网络二进位性质得到很大的解释。在时间序列分析中,我们观察到,鉴于节点和二元特征,我们的网络呈现出向平衡,可传递和聚集的社会宏观结构发展的强烈趋势。我们的发现通过提供对网络宏观结构与导致其的微观社会过程之间的关系的更多见解,补充了对在线社会网络的结构属性的研究。它们是更好地了解在线社交媒体系统如何改变我们社会的信息和沟通结构的基础。

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