监测动态网络中未知参与者团队之间的通信爆发
Monitoring communication outbreaks among an unknown team of actors in dynamic networks
摘要：
本文研究了时变网络中一小组参与者之间的通信爆发检测问题。我们提出了监测计划，已知和未知的团队的基础上推广的指数加权移动平均（EWMA）统计。对于未知的团队，我们提出了一个有效的基于邻域的搜索来估计候选团队的集合。这个过程大大降低了穷举搜索的计算复杂性。我们的程序包括两个步骤：首先使用多变量EWMA策略平滑演员之间的通信计数。使用邻域搜索方法将密集连接的团队确定为候选人。这些候选团队，然后使用来自广义EWMA统计的监督计划进行监测。为协作小组、领导人占主导地位的小组以及全球疫情制定了监测计划。我们考虑加权异构动态网络，其中每对演员之间的预期通信计数在对和时间上可能不同，以及同构网络，其中预期通信计数在时间和演员上是恒定的。我们的监测计划在模拟网络的测试床上进行评估，以及在美国参议院共同投票网络上进行评估，该网络模拟了1857年至2015年的参议院投票模式。我们的分析表明，我们的监视策略可以有效地检测动态网络中的相关和重大变化。
英文摘要：
This paper investigates the detection of communication outbreaks among a small team of actors in time-varying networks. We propose monitoring plans for known and unknown teams based on generalizations of the exponentially weighted moving average (EWMA) statistic. For unknown teams, we propose an efficient neighborhood-based search to estimate a collection of candidate teams. This procedure dramatically reduces the computational complexity of an exhaustive search. Our procedure consists of two steps: communication counts between actors are first smoothed using a multivariate EWMA strategy. Densely connected teams are identified as candidates using a neighborhood search approach. These candidate teams are then monitored using a surveillance plan derived from a generalized EWMA statistic. Monitoring plans are established for collaborative teams, teams with a dominant leader, as well as for global outbreaks. We consider weighted heterogeneous dynamic networks, where the expected communication count between each pair of actors is potentially different across pairs and time, as well as homogeneous networks, where the expected communication count is constant across time and actors. Our monitoring plans are evaluated on a test bed of simulated networks as well as on the U.S. Senate co-voting network, which models the Senate voting patterns from 1857 to 2015. Our analysis suggests that our surveillance strategies can efficiently detect relevant and significant changes in dynamic networks.
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