标题： 从无标签数据中学习图结构用于事件检测 显示英文标题

标题： Graph Structure Learning from Unlabeled Data for Event Detection

摘要： 诸如疾病传播和信息扩散之类的进程通常会通过某些潜在的网络结构进行传播，而该网络结构必须从观测数据中学习得到。 给定一组表示感兴趣事件类型（例如，疾病暴发）出现情况的未标记训练样本，我们的目标是学习一种图结构，以便能够准确地检测出未来此类事件的发生。 受关于约束和非约束子集扫描一致性新理论结果的启发，我们提出了一种新颖的框架，通过比较带有和不带图约束条件时检测到的最异常子集，从无标签数据中学习图结构。 我们的框架使用平均归一化对数似然比分数来衡量图结构的质量，并高效搜索得分最高的图结构。 利用注入到真实世界Allegheny县急诊科数据中的模拟疾病暴发数据，我们表明我们的方法学习到的结构与真实的潜在图结构相似，但可以实现更快且更准确的检测。 显示英文摘要

摘要： Processes such as disease propagation and information diffusion often spread over some latent network structure which must be learned from observation. Given a set of unlabeled training examples representing occurrences of an event type of interest (e.g., a disease outbreak), our goal is to learn a graph structure that can be used to accurately detect future events of that type. Motivated by new theoretical results on the consistency of constrained and unconstrained subset scans, we propose a novel framework for learning graph structure from unlabeled data by comparing the most anomalous subsets detected with and without the graph constraints. Our framework uses the mean normalized log-likelihood ratio score to measure the quality of a graph structure, and efficiently searches for the highest-scoring graph structure. Using simulated disease outbreaks injected into real-world Emergency Department data from Allegheny County, we show that our method learns a structure similar to the true underlying graph, but enables faster and more accurate detection.