Nonparametric Network Models for Link Prediction

JMLR 2016 pp. 1-21

/jmlr/2016/williamson2016jmlr-nonparametric/

Abstract

Many data sets can be represented as a sequence of interactions between entities---for example communications between individuals in a social network, protein-protein interactions or DNA-protein interactions in a biological context, or vehicles' journeys between cities. In these contexts, there is often interest in making predictions about future interactions, such as who will message whom.

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Text

Williamson. "Nonparametric Network Models for Link Prediction." Journal of Machine Learning Research, 2016.

Markdown

[Williamson. "Nonparametric Network Models for Link Prediction." Journal of Machine Learning Research, 2016.](https://mlanthology.org/jmlr/2016/williamson2016jmlr-nonparametric/)

BibTeX

@article{williamson2016jmlr-nonparametric,
  title     = {{Nonparametric Network Models for Link Prediction}},
  author    = {Williamson, Sinead A.},
  journal   = {Journal of Machine Learning Research},
  year      = {2016},
  pages     = {1-21},
  volume    = {17},
  url       = {https://mlanthology.org/jmlr/2016/williamson2016jmlr-nonparametric/}
}