Department of Computer Science, Virginia Tech, Blacksburg, VA, 24061, US.
Sci Rep. 2023 Feb 10;13(1):2395. doi: 10.1038/s41598-023-29443-w.
The rapid growth of online social media usage in our daily lives has increased the importance of analyzing the dynamics of online social networks. However, the dynamic data of existing online social media platforms are not readily accessible. Hence, there is a necessity to synthesize networks emulating those of online social media for further study. In this work, we propose an epidemiology-inspired and community-based, time-evolving online social network generation algorithm (EpiCNet), to generate a time-evolving sequence of random networks that closely mirror the characteristics of real-world online social networks. Variants of the algorithm can produce both undirected and directed networks to accommodate different user interaction paradigms. EpiCNet utilizes compartmental models inspired by mathematical epidemiology to simulate the flow of individuals into and out of the online social network. It also employs an overlapping community structure to enable more realistic connections between individuals in the network. Furthermore, EpiCNet evolves the community structure and connections in the simulated online social network as a function of time and with an emphasis on the behavior of individuals. EpiCNet is capable of simulating a variety of online social networks by adjusting a set of tunable parameters that specify the individual behavior and the evolution of communities over time. The experimental results show that the network properties of the synthetic time-evolving online social network generated by EpiCNet, such as clustering coefficient, node degree, and diameter, match those of typical real-world online social networks such as Facebook and Twitter.
在我们的日常生活中，在线社交媒体的使用呈快速增长趋势，这使得分析在线社交网络的动态性变得尤为重要。然而，现有的在线社交媒体平台的动态数据不易获取。因此，有必要综合模拟在线社交媒体的网络，以便进一步研究。在这项工作中，我们提出了一种受传染病启发的、基于社区的、随时间演变的在线社交网络生成算法（EpiCNet），以生成与真实世界在线社交网络特征紧密匹配的随时间演变的随机网络序列。该算法的变体可以生成有向和无向网络，以适应不同的用户交互模式。EpiCNet 利用受数学传染病启发的房室模型来模拟个体在在线社交网络中的流入和流出。它还采用重叠社区结构，以实现网络中个体之间更真实的连接。此外，EpiCNet 还根据时间和个体行为来演变模拟在线社交网络中的社区结构和连接。EpiCNet 通过调整一组可调节参数来模拟各种在线社交网络，这些参数指定了个体行为和社区随时间的演变。实验结果表明，EpiCNet 生成的合成时变在线社交网络的网络属性，如聚类系数、节点度和直径，与 Facebook 和 Twitter 等典型的真实世界在线社交网络相匹配。
