Densification events in time-evolving networks refer to instants in which the network density, that is, the number of edges, is substantially larger than in the remaining. These events can occur at a global level, involving the majority of the nodes in the network, or at a local level involving only a subset of nodes.While global densification events affect the overall structure of the network, the same does not hold in local densification events, which may remain undetectable by the existing detection methods. In order to address this issue, we propose WINdowed TENsor decomposition for Densification Event Detection (WINTENDED) for the detection and characterization of both global and local densification events. Our method combines a sliding window decomposition with statistical tools to capture the local dynamics of the network and automatically find the irregular behaviours. According to our experimental evaluation, WINTENDED is able to spot global densification events at least as accurately as its competitors, while also being able to find local densification events, on the contrary to its competitors.

随时间变化的网络中的致密化事件是指网络密度（即边数）显着大于其余部分的瞬间。这些事件可以发生在涉及网络中大多数节点的全局级别上，也可以发生在仅涉及节点子集的本地级别上。致密化事件，这可能是现有检测方法无法检测到的。为了解决此问题，我们建议使用WINdowed TENsor分解进行致密化事件检测（WINTENDED），以检测和表征全局和局部致密化事件。我们的方法将滑动窗口分解与统计工具结合在一起，以捕获网络的局部动态并自动查找不规则行为。根据我们的实验评估，与竞争对手相比，WINTENDED能够至少像其竞争对手一样准确地发现全球致密化事件，同时还能找到本地致密化事件。