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Entities whose changes will significantly affect others in a networked systemare called shakers. In recent years, some models have been proposed to detectsuch shaker from evolving entities. However, limited work has focused on shakerdetection in very short term, which has many real-world applications. Forexample, in financial market, it can enable both investors and governors toquickly respond to rapid changes. Under the short-term setting, conventionalmethods may suffer from limited data sample problems and are sensitive tocynical manipulations, leading to unreliable results. Fortunately, there aremulti-attribute evolution records available, which can provide compatible andcomplementary information. In this paper, we investigate how to learn reliableinfluence results from the short-term multi-attribute evolution records. Wecall entities with consistent influence among different views in short term asmulti-view shakers and study the new problem of multi-view shaker detection. Weidentify the challenges as follows: (1) how to jointly detect short-termshakers and model conflicting influence results among different views? (2) howto filter spurious influence relation in each individual view for robustinfluence inference? In response, a novel solution, called Robust InfluenceNetwork from a noise-immune influence analysis perspective is proposed, wherethe possible outliers are well modelled jointly with multi-view shakerdetection task. More specifically, we learn the influence relation from eachview and transform influence relation from different views into an intermediaterepresentation. In the meantime, we uncover both the inconsistent and spuriousoutliers.

Multi-view shaker detection: Insights from a noise-immune influence analysis Perspective