DELTACON：一个原理性的大数相似函数
DELTACON: A Principled Massive-Graph Similarity Function
摘要：
从昨天开始网络变化了多少？鲍勃的大脑（男性左撇子）和爱丽丝的大脑（女性右撇子）之间的线路有什么不同？图与已知节点对应的相似性，即检测图的连通性的变化，出现在许多设置中。在这项工作中，我们正式声明的公理和所需的图形相似性函数的属性，并评估时，国家的最先进的方法无法检测到关键的连通性变化的图形。我们提出了DeltaCon，一个原则性的，直观的，可扩展的算法，评估两个图形之间的相似性在相同的节点（例如，公司的员工，移动运营商的客户）。在各种合成图和真实图上的实验显示了我们的方法比现有的相似性度量的优势。最后，我们采用DeltaCon的实际应用程序：（a）我们分类的人组的高和低的创造力的基础上，他们的大脑连接图，和（b）做时间异常检测谁-电子邮件-谁安然图。
英文摘要：
How much did a network change since yesterday? How different is the wiring between Bob's brain (a left-handed male) and Alice's brain (a right-handed female)? Graph similarity with known node correspondence, i.e. the detection of changes in the connectivity of graphs, arises in numerous settings. In this work, we formally state the axioms and desired properties of the graph similarity functions, and evaluate when state-of-the-art methods fail to detect crucial connectivity changes in graphs. We propose DeltaCon, a principled, intuitive, and scalable algorithm that assesses the similarity between two graphs on the same nodes (e.g. employees of a company, customers of a mobile carrier). Experiments on various synthetic and real graphs showcase the advantages of our method over existing similarity measures. Finally, we employ DeltaCon to real applications: (a) we classify people to groups of high and low creativity based on their brain connectivity graphs, and (b) do temporal anomaly detection in the who-emails-whom Enron graph.
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。
