The complex topology of real networks allows its actors to change their functional behavior. Network models provide better understanding of the evolutionary mechanisms being accountable for the growth of such networks by capturing the dynamics in the ways network agents interact and change their behavior. Considerable amount of research efforts is required for developing novel network modeling techniques to understand the structural properties such networks, reproducing similar properties based on empirical evidence, and designing such networks efficiently. First, we demonstrate how to construct social interaction networks using social media data and then present the key findings obtained from the network analytics. We analyze the characteristics and growth of such interaction networks, examine the network properties and derive important insights based on the theories of network science literature. We also discuss the application of such networks as a useful tool to effectively disseminate targeted information during planned special events. We observed that the degree-distributions of such networks follow power-law that is indicative of the existence of fewer nodes in the network with higher levels of interactions, and many other nodes with less interactions. While the network elements and average user degree grow linearly each day, densities of such networks tend to become zero. Largest connected components exhibit higher connectivity (density) when compared with the whole graph. Network radius and diameter become stable over time evidencing the small-world property. We also observe increased transitivity and higher stability of the power-law exponents as the networks grow. Data is specific to the Purdue University community and two large events, namely Purdue Day of Giving and Senator Bernie Sanders' visit to Purdue University as part of Indiana Primary Election 2016.
翻译:网络模型通过捕捉网络代理人互动和改变其行为的方式的动态,更好地了解对网络增长负责的进化机制; 需要开展大量研究工作,以开发新型网络模型技术,了解网络结构属性,根据经验证据复制类似属性,并高效设计网络; 首先,我们展示如何利用社交媒体数据建立社交互动网络,然后介绍网络分析的主要结果; 我们分析这些互动网络的特点和增长,审查网络性质,并根据网络科学文献理论获得重要见解; 我们还讨论如何应用这些网络,以此作为在计划特别活动期间有效传播有针对性的信息的有用工具; 我们观察到,这些网络的等级分布遵循权力法,这表明网络中存在较少的节点,互动程度较高,而许多其他节点互动较少。 虽然网络要素和平均用户比例每天都在线性上增长,但此类网络的密度往往变成零。 连接度最大的部分是选举周期性网络的高度连通性(密度),在与整个图表相比,我们发现,稳定度和稳定度的网络也比稳定度更稳定。