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The clustering/community-based methods detect community outliers whose properties differ from other members in the community, whereas network structure- based methods detect subgraphs that are rare with respect to the structure of the attributed network.
These nodes are termed as community outliers.
For instance, a low-income person having many rich people as friends is an example of community outlier (Gao et al.
(2010) introduced the concept of community outliers, and developed a unified framework for detecting outliers and for discovering communities.
They proposed community outlier de- tection algorithm (CODA) that combines both community detec- tion and outlier detection in a probabilistic formulation based on Hidden Markov Random Fields (HMRF), by combining information
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