Entities whose changes will significantly affect others in a networked system are called shakers. In recent years, some models have been proposed to detect such shaker from evolving entities. However, limited work has focused on shaker detection in very short term, which has many real-world applications. For example, in financial market, it can enable both investors and governors to quickly respond to rapid changes. Under the short-term setting, conventional methods may suffer from limited data sample problems and are sensitive to cynical manipulations, leading to unreliable results. Fortunately, there are multi-attribute evolution records available, which can provide compatible and complementary information. In this paper, we investigate how to learn reliable influence results from the short-term multi-attribute evolution records. We call entities with consistent influence among different views in short term as multi-view shakers and study the new problem of multi-view shaker detection. We identify the challenges as follows: (1) how to jointly detect short-term shakers and model conflicting influence results among different views? (2) how to filter spurious influence relation in each individual view for robust influence inference? In response, a novel solution, called Robust Influence Network from a noise-immune influence analysis perspective is proposed, where the possible outliers are well modelled jointly with multi-view shaker detection task. More specifically, we learn the influence relation from each view and transform influence relation from different views into an intermediate representation. In the meantime, we uncover both the inconsistent and spurious outliers.

翻译：在网络化系统中，其变化会显著影响其他实体的实体被称为震荡源。近年来，已有一些模型被提出用于从演化实体中检测此类震荡源。然而，针对极短期震荡源检测的研究尚属有限，而该问题具有诸多现实应用场景。例如，在金融市场中，它可使投资者与监管者快速应对市场突变。在短期设定下，传统方法可能面临数据样本不足的问题，且容易受到恶意操纵的影响，从而导致不可靠的结果。幸运的是，可获取的多属性演化记录能够提供兼容且互补的信息。本文探究了如何从短期多属性演化记录中学习可靠的影响结果。我们将不同视角下短期影响力一致的实体称为多视角震荡源，并研究了多视角震荡源检测这一新问题。我们识别出以下挑战：（1）如何联合检测短期震荡源并对不同视角间的冲突性影响结果进行建模？（2）如何过滤每个独立视角中的虚假影响关系以实现鲁棒的影响推理？为此，我们提出了一种创新解决方案——基于噪声免疫影响分析视角的鲁棒影响网络，该方案将可能的异常点与多视角震荡源检测任务进行联合良好建模。具体而言，我们从每个视角学习影响关系，并将不同视角的影响关系转化为中间表征，同时揭示出不一致性与虚假异常点。