Leveraging Label-Independent Features for Classification in Sparsely Labeled Networks: An Empirical Study

@inproceedings{Gallagher2008LeveragingLF,
  title={Leveraging Label-Independent Features for Classification in Sparsely Labeled Networks: An Empirical Study},
  author={Brian Gallagher and Tina Eliassi-Rad},
  booktitle={SNAKDD},
  year={2008}
}
We address the problem of within-network classification in sparsely labeled networks. Recent work has demonstr ated success with statistical relational learning (SRL) and semi-supervised learning (SSL) on such problems. However, both approaches r ely on the availability of labeled nodes to infer the v alues of missing labels. When few labels are available, the performa nce of these approaches can degrade. In addition, many such appr o ches are sensitive to the specific set of nodes labeled. So… CONTINUE READING
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