Abstract
Outlier detection is an important problem that has been researched and applied in a myriad of domains ranging from fraudulent transactions to intrusion detection. Most existing methods have been specially developed for detecting global and (or) local outliers by using either content information or structure information. Unfortunately, these conventional algorithms have been facing with unprecedented challenges in social networks, where data and link information are tightly integrated.
In this paper, a novel measurement named Community Outlying Factor is put forward for community outlier, besides its descriptive definition. A scalable community outliers detection algorithm (SCODA), which fully considers both content and structure information of social networks, is proposed. Furthermore, SCODA takes effective measures to minimize the number of input parameters down to only one, the number of outliers. Experimental results demonstrate that the time complexity of SCODA is linear to the number of nodes, which means that our algorithm can easily deal with very large data sets.
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Ji, T., Gao, J., Yang, D. (2012). A Scalable Algorithm for Detecting Community Outliers in Social Networks. In: Gao, H., Lim, L., Wang, W., Li, C., Chen, L. (eds) Web-Age Information Management. WAIM 2012. Lecture Notes in Computer Science, vol 7418. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32281-5_42
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DOI: https://doi.org/10.1007/978-3-642-32281-5_42
Publisher Name: Springer, Berlin, Heidelberg
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