Heterogeneous networks are ubiquitous. For example, bibliographic data, social data, medical records, movie data and many more can be modeled as heterogeneous networks. Rich information associated with multi-typed nodes in heterogeneous networks motivates us to propose a new de\ufb01nition of outliers, which is different from those de\ufb01ned for homogeneous networks. In this paper, we propose the novel concept of Community Distribution Outliers (CDOutliers) for heterogeneous information networks, which are de\ufb01ned as objects whose community distribution does not follow any of the popular community distribution patterns. We extract such outliers using a type-aware joint analysis of multiple types of objects. Given community membership matrices for all types of objects, we follow an iterative two-stage approach which performs pattern discovery and outlier detection in a tightly integrated manner. We \ufb01rst propose a novel outlier-aware approach based on joint non-negative matrix factorization to discover popular community distribution patterns for all the object types in a holistic manner, and then detect outliers based on such patterns. Experimental results on both synthetic and real datasets show that the proposed approach is highly effective in discovering interesting community distribution outliers.<\/p>\n","protected":false},"excerpt":{"rendered":"
Heterogeneous networks are ubiquitous. For example, bibliographic data, social data, medical records, movie data and many more can be modeled as heterogeneous networks. Rich information associated with multi-typed nodes in heterogeneous networks motivates us to propose a new de\ufb01nition of outliers, which is different from those de\ufb01ned for homogeneous networks. 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