Directed contact networks (DCNs) are temporal networks that are useful for analyzing and modeling phenomena in transportation, communications, epidemiology and social networking. Specific sequences of contacts can underlie higher-level behaviors such as flows that aggregate contacts based on some notion of semantic and temporal proximity. We describe a simple inhomogeneous Markov model to infer flows and taint bounds associated with such higher-level behaviors, and also discuss how to aggregate contacts within DCNs and/or dynamically cluster their vertices. We provide examples of these constructions in the contexts of information transfers within computer and air transportation networks, thereby indicating how they can be used for data reduction and anomaly detection.

中文翻译：

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定向联系网络分析

定向联系网络（DCN）是时间网络，可用于分析和建模运输，通信，流行病学和社交网络中的现象。联系人的特定顺序可以成为更高级别行为的基础，例如基于语义和时间接近度的概念聚合联系人的流程。我们描述了一个简单的非均匀马尔可夫模型来推断与此类更高级别行为相关的流和污点边界，并讨论了如何在DCN中聚合联系人和/或动态地聚类其顶点。我们在计算机和航空运输网络内的信息传输环境中提供了这些构造的示例，从而说明了如何将它们用于数据缩减和异常检测。