A Survey of Link Mining Tasks for Analyzing Noisy and Incomplete Networks

  • Galileo Mark Namata
  • Hossam Sharara
  • Lise Getoor


Many data sets of interest today are best described as networks or graphs of interlinked entities. Examples include Web and text collections, social networks and social media sites, information, transaction and communication networks, and all manner of scientific networks, including biological networks. Unfortunately, often the data collection and extraction process for gathering these network data sets is imprecise, noisy, and/or incomplete. In this chapter, we review a collection of link mining algorithms that are well suited to analyzing and making inferences about networks, especially in the case where the data is noisy or missing.


Betweenness Centrality Community Detection Link Prediction Reference Node Entity Resolution 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The work was supported by NSF Grant #0746930.


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Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  • Galileo Mark Namata
    • 1
  • Hossam Sharara
    • 1
  • Lise Getoor
    • 1
  1. 1.Department of Computer ScienceUniversity of MarylandCollege ParkUSA

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