对称性是微博社交网络中最直观也是最重要的性质之一，研究微博社交网络的对称性变化规律，可以揭示微博社交网络用户节点之间的联系紧密程度。然而，目前已有的社交网络对称性研究方法存在局限性。一方面，微博社交网络的对称程度没有具体准确的定义，容易忽略微博的特殊性；另一方面，现有的对称程度算法不适用于大数据类型，而社交网络研究属于大数据挖掘范畴。为了解决这两个方面的问题，本文基于微博的特性，给出微博社交网络对称程度的定义，并对已有算法进行优化，使得对大数据类社交网络对称性的研究更加可行。

本文主要分为三个部分，第一部分主要阐述微博社交网络对称性的相关背景，并概述了现有的Reciprocity算法，给出了改进的对称程度算法。第二部分使用改进算法研究微博社交网络的对称性，具体工作如下：根据收集的100万条新浪用户之间的关注关系和236个Twitter用户及其之间的关注关系来构建初始微博社交网络，选取其中具有明显对称性的连通子网络作为研究的主要对象；通过去除法研究影响其最大连通子网络对称性的主要因素，并分析了产生此现象的原因；用比较分析法，得到了Sina微博和Twitter的大V用户构成的社交子网络的对称性强弱关系；从功能定位方面给出了两种微博的对称程度实证分析；通过对初始网络的所有连通子网络的对称性研究，总结得出社交网络的对称性变化规律。第三部分讲述了社交网络的对称性变化规律的现实意义，总结了相关的结论，并对微博社交网络的对称性的进一步研究提出了延伸和展望。

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