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Identifying graphs from noisy and incomplete data

Identifying Graphs From Noisy and Incomplete Data Galileo Mark S. Namata Jr. Department of Computer Science University of Maryland College Park, MD 20742 USA Lise Getoor Department of Computer Science University of Maryland College Park, MD 20742 USA namatag@cs.umd.edu getoor@cs.umd.edu ABSTRACT There is a growing wealth of data describing networks of various types, including social networks, physical networks such as transportation or communication networks, and biological networks. At the same time, there is a growing interest in analyzing these networks, in order to uncover general laws that govern their structure and evolution, and patterns and predictive models to develop better policies and practices. However, a fundamental challenge in dealing with this newly available observational data describing networks is that the data is often of dubious quality “ it is noisy and incomplete “ and before any analysis method can be applied, the data must be cleaned, and missing information inferred. In this paper, we introduce the notion of graph identi cation, which explicitly models the inference of a œcleaned  output network from a noisy input graph. It is this output network that is appropriate for further analysis. We present an illustrative example and use the example to http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM SIGKDD Explorations Newsletter Association for Computing Machinery

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