Mining outliers in heterogeneous networks is crucial to many applications, but challenges abound. In this paper, we focus on identifying meta-path-based outliers in heterogeneous information network (HIN), and calculate the similarity between different types of objects. We propose a meta-path-based outlier detection method (MPOutliers) in heterogeneous information network to deal with problems in one go under a unified framework. MPOutliers calculates the heterogeneous reachable probability by combining different types of objects and their relationships. It discovers the semantic information among nodes in heterogeneous networks, instead of only considering the network structure. It also computes the closeness degree between nodes with the same type, which extends the whole heterogeneous network. Moreover, each node is assigned with a reliable weighting to measure its authority degree. Substantial experiments on two real datasets (AMiner and Movies dataset) show that our proposed method is very effective and efficient for outlier detection.

中文翻译：

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异构信息网络中基于元路径的离群值检测

异构网络中的异常值挖掘对于许多应用程序至关重要，但是挑战很多。在本文中，我们专注于识别异构信息网络（HIN）中基于元路径的离群值，并计算不同类型对象之间的相似度。我们提出了一种在异构信息网络中基于元路径的离群值检测方法（MPOutliers），以在统一框架下一次性解决问题。MPOutliers通过组合不同类型的对象及其关系来计算异构可达概率。它发现异构网络中节点之间的语义信息，而不仅仅是考虑网络结构。它还可以计算相同类型节点之间的紧密度，从而扩展了整个异构网络。此外，每个节点都分配有可靠的权重以衡量其权限度。在两个真实数据集（AMiner和Movies数据集）上的大量实验表明，我们提出的方法对于异常值检测非常有效。