网络分类与归类
Network Classification and Categorization
摘要：
据我们所知，本文提出了第一个大规模的研究，测试是否网络类别（例如，社交网络与网络图）是彼此可区分的（使用真实世界网络和合成图这两种类别）。使用真实网络和合成网络的随机森林分类器实现了$94.2\%$的分类精度。这项工作有两个重要的发现。首先，来自不同领域的真实网络具有不同的结构属性，使我们能够高精度地预测任意网络的类别。其次，对合成网络进行分类是很简单的，因为我们的模型可以很容易地区分合成图和它们应该建模的真实网络。
英文摘要：
To the best of our knowledge, this paper presents the first large-scale study that tests whether network categories (e.g., social networks vs. web graphs) are distinguishable from one another (using both categories of real-world networks and synthetic graphs). A classification accuracy of $94.2\%$ was achieved using a random forest classifier with both real and synthetic networks. This work makes two important findings. First, real-world networks from various domains have distinct structural properties that allow us to predict with high accuracy the category of an arbitrary network. Second, classifying synthetic networks is trivial as our models can easily distinguish between synthetic graphs and the real-world networks they are supposed to model.
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。
