BibSonomy publications for /user/lee_peck/networkThu Jan 14 19:59:52 CET 2010New York, NY, USAKDD '09: Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining169--178Connections between the lines: augmenting social networks with text200909 Chang extraction network social text Network data is ubiquitous, encoding collections of relationships between entities such as people, places, genes, or corporations. While many resources for networks of interesting entities are emerging, most of these can only annotate connections in a limited fashion. Although relationships between entities are rich, it is impractical to manually devise complete characterizations of these relationships for every pair of entities on large, real-world corpora. In this paper we present a novel probabilistic topic model to analyze text corpora and infer descriptions of its entities and of relationships between those entities. We develop variational methods for performing approximate inference on our model and demonstrate that our model can be practically deployed on large corpora such as Wikipedia. We show qualitatively and quantitatively that our model can construct and annotate graphs of relationships and make useful predictions.Wed Nov 12 20:41:32 CET 2008New York, NY, USAACM-SE 42: Proceedings of the 42nd annual Southeast regional conference402--103Network flow for outlier detection200404 Liu community discovery flow network outlier toread Detecting outliers is an important topic in data mining. Sometimes the outliers are more interesting than the rest of the data. Outlier identification has lots of applications, such as intrusion detection, and unusual usage of credit cards or telecommunication services. In this paper, we propose a novel method for outlier identification which is based on network flow. We use the well known Maximum Flow Minimum Cut theorem from graph theory to find the outliers and strong outlier groups. Especially, it works on high dimensional data. This outlier detection occurs in a novel setting: to repair poor quality clusters generated by a clustering algorithm.