论文标题
快速生成具有相互边缘和高群的简单定向网络图
Fast generation of simple directed social network graphs with reciprocal edges and high clustering
论文作者
论文摘要
在线社交网络已成为每天传达或共享信息和新闻的有用工具。最受欢迎的网络之一是Twitter，用户通过定向的追随者关系相互连接。研究人员研究了Twitter追随者图，并具有各种拓扑特征。收集Twitter数据，尤其是用户的追随者爬行的数据是一个乏味且耗时的过程，由于其敏感性，还需要仔细处理数据，其中包含个人用户信息。因此，我们旨在快速生成具有相互边缘和高聚类的合成定向网络图。我们提出的方法基于先前开发的模型，但依赖于较少的超参数，并且运行时的运行时间明显较低。结果表明，该方法不仅复制了爬行的有向Twitter图W.R.T.几个拓扑特征和流行病扩散过程的应用，但是它也是高度可扩展的，它允许快速创建较大的图形，这些图形表现出与现实世界网络相似的属性。
Online social networks have emerged as useful tools to communicate or share information and news on a daily basis. One of the most popular networks is Twitter, where users connect to each other via directed follower relationships. Researchers have studied Twitter follower graphs and described them with various topological features. Collecting Twitter data, especially crawling the followers of users, is a tedious and time-consuming process and the data needs to be treated carefully due to its sensitive nature, containing personal user information. We therefore aim at the fast generation of synthetic directed social network graphs with reciprocal edges and high clustering. Our proposed method is based on a previously developed model, but relies on less hyperparameters and has a significantly lower runtime. Results show that the method does not only replicate the crawled directed Twitter graphs well w.r.t. several topological features and the application of an epidemics spreading process, but that it is also highly scalable which allows the fast creation of bigger graphs that exhibit similar properties as real-world networks.
