从时态网络中挖掘（最大）跨度核
Mining (maximal) span-cores from temporal networks
摘要：
当分析时间网络时，基本任务是识别密集结构（即，表现出大量链接的顶点组），连同它们的时间跨度（即，高密度保持的时间段）。我们解决这个任务，通过引入一个概念的时间核心分解，每个核心与其跨度：我们称之为这样的核心跨度核心。由于时间间隔的总数是所分析的时间域$T$的大小的二次方，因此跨度核的总数在$|T|$中也是二次方。我们的第一个贡献是一个算法，利用跨核心之间的包容性，有效地计算所有的跨核心。然后，我们专注于只找到最大跨度核心的问题，即，跨芯不受任何其他跨芯的核心性能和跨度的支配。我们设计了一个非常有效的算法，利用理论研究结果的极大性条件，直接计算最大的，而不计算所有的跨度核心。几个真实世界的时间网络上的实验证实了我们的方法的效率和可扩展性。时间网络上的应用程序，收集了一个接近传感基础设施记录面对面的互动在学校，突出的相关性的概念（最大）跨度核心在分析社会动态和检测/纠正异常的数据。
英文摘要：
When analyzing temporal networks, a fundamental task is the identification of dense structures (i.e., groups of vertices that exhibit a large number of links), together with their temporal span (i.e., the period of time for which the high density holds). We tackle this task by introducing a notion of temporal core decomposition where each core is associated with its span: we call such cores span-cores. As the total number of time intervals is quadratic in the size of the temporal domain $T$ under analysis, the total number of span-cores is quadratic in $|T|$ as well. Our first contribution is an algorithm that, by exploiting containment properties among span-cores, computes all the span-cores efficiently. Then, we focus on the problem of finding only the maximal span-cores, i.e., span-cores that are not dominated by any other span-core by both the coreness property and the span. We devise a very efficient algorithm that exploits theoretical findings on the maximality condition to directly compute the maximal ones without computing all span-cores. Experimentation on several real-world temporal networks confirms the efficiency and scalability of our methods. Applications on temporal networks, gathered by a proximity-sensing infrastructure recording face-to-face interactions in schools, highlight the relevance of the notion of (maximal) span-core in analyzing social dynamics and detecting/correcting anomalies in the data.
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。
