document: On community outliers and their efficient detection in information networks Linked or networked data are ubiquitous in many applications . Examples include web data or hypertext documents connected via hyperlinks , social networks or user profiles connected via friend links , co-authorship and citation information , blog data , movie reviews and so on . In these datasets ( called `` information networks '' ) , closely related objects that share the same properties or interests form a community . For example , a community in blogsphere could be users mostly interested in cell phone reviews and news . Outlier detection in information networks can reveal important anomalous and interesting behaviors that are not obvious if community information is ignored . An example could be a low-income person being friends with many rich people even though his income is not anomalously low when considered over the entire population . This paper first introduces the concept of community outliers ( interesting points or rising stars for a more positive sense ) , and then shows that well-known baseline approaches without considering links or community information can not find these community outliers . We propose an efficient solution by modeling networked data as a mixture model composed of multiple normal communities and a set of randomly generated outliers . The probabilistic model characterizes both data and links simultaneously by defining their joint distribution based on hidden Markov random fields ( HMRF ) . Maximizing the data likelihood and the posterior of the model gives the solution to the outlier inference problem . We apply the model on both synthetic data and DBLP data sets , and the results demonstrate importance of this concept , as well as the effectiveness and efficiency of the proposed approach .
doc_bio_tags: O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O