大型有向网络中高度实体的快速检测
Quick Detection of High-degree Entities in Large Directed Networks
摘要：
在本文中，我们解决了在大型在线社交网络的高度实体的快速检测问题。这个问题的实际重要性得到了大量公司的证明，这些公司不断收集和更新关于流行实体的统计数据，通常使用实体的程度作为其流行程度的近似值。我们提出了一个简单，高效，易于实现的两阶段随机算法，提供了高度准确的解决方案，这个问题。例如，我们的算法只需要1000个API请求就可以找到Twitter中最受关注的前100名用户，Twitter是一个拥有大约10亿注册用户的网络，准确率超过90%。我们的算法明显优于现有的方法，并服务于许多不同的目的，如找到最受欢迎的用户或最受欢迎的兴趣群体在社交网络。这项工作的一个重要贡献是使用极值理论-概率的一个分支，研究极端事件和随机样本中最大顺序统计量的属性的算法的分析。使用这一理论，我们得出了一个准确的预测算法的性能，并表明，API请求的数量找到前k个最流行的实体是次线性的实体的数量。此外，我们正式表明，实体之间的高变异性，通过重尾分布表示，是算法的效率的原因。我们用严格的数学方法来量化这种现象。
英文摘要：
In this paper, we address the problem of quick detection of high-degree entities in large online social networks. Practical importance of this problem is attested by a large number of companies that continuously collect and update statistics about popular entities, usually using the degree of an entity as an approximation of its popularity. We suggest a simple, efficient, and easy to implement two-stage randomized algorithm that provides highly accurate solutions for this problem. For instance, our algorithm needs only one thousand API requests in order to find the top-100 most followed users in Twitter, a network with approximately a billion of registered users, with more than 90% precision. Our algorithm significantly outperforms existing methods and serves many different purposes, such as finding the most popular users or the most popular interest groups in social networks. An important contribution of this work is the analysis of the proposed algorithm using Extreme Value Theory -- a branch of probability that studies extreme events and properties of largest order statistics in random samples. Using this theory, we derive an accurate prediction for the algorithm's performance and show that the number of API requests for finding the top-k most popular entities is sublinear in the number of entities. Moreover, we formally show that the high variability among the entities, expressed through heavy-tailed distributions, is the reason for the algorithm's efficiency. We quantify this phenomenon in a rigorous mathematical way.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
