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,其变化会对其他方面有重大影响的实体被称为摇动器。近年来,提出了一些模型,以发现来自不断演变的实体的摇动器。然而,有限的工作侧重于短期的摇动器探测,这具有许多现实世界应用。例如,在金融市场,它可以使投资者和董事对迅速变化作出迅速反应。在短期环境下,传统方法可能受到数据抽样有限问题的影响,并容易受到玩世不恭的操纵,从而导致不可靠的结果。幸运的是,存在多种归宿进化记录,可以提供兼容和互补的信息。在本文中,我们研究如何从短期多归宿的演变记录中学习可靠的影响结果。我们呼吁短期内不同观点具有持续影响力的实体作为多视角摇动器,并研究多视图摇晃动器检测的新问题。我们确定以下挑战:(1)如何共同发现短期摇动器和模式对不同观点的影响产生矛盾效应。(2)如何过滤个人观点中的虚假影响关系,以便产生强有力的影响?在本文中,一种新的解决办法,我们称之为冲动式的调动器动图像关系中,我们称之为“更趋动式的震动式”网络,具体地从模拟的震动式图像中,从模拟的震动式变动关系,从模拟变压动式的图像中,从结构中,从模拟的变动式变动式的图像中,从结构中,从模拟的变动式的变动式的变动式的变动式的变动式的变动式关系,从结构,从一种变动式关系,具体地看,从一种不同的变动式的变动式的变动式的变动式的变动式的变动式的变动式的变动式的变动式的变动式的变动式的变动式的变动式的变动式关系,从一种变动式的变动式的变动式的变动式的变动式关系从一种变动式的变动式网络,从一种变动式的变动式的变动式的变动式的变动式的变动式的变动式的变动式的变动式的变动式的变动式的变动式的变动式的变。</s>