# Link prediction for partially observed networks

arXiv: https://arxiv.org/abs/1301.7047 · HTML: https://mlsanity.com/paper/1301.7047/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Jan 2013.

## Built on by (most cited first, 7 of 7)

- [Latent Space Model for Higher-order Networks and Generalized Tensor Decomposition](https://mlsanity.com/paper/2106.16042/) (2021)
- [Conformal link prediction for false discovery rate control](https://mlsanity.com/paper/2306.14693/) (2023)
- [Distribution-Free Matrix Prediction Under Arbitrary Missing Pattern](https://mlsanity.com/paper/2305.11640/) (2023)
- [High-order joint embedding for multi-level link prediction](https://mlsanity.com/paper/2111.05265/) (2021)
- [Influence Maximization (IM) in Complex Networks with Limited Visibility Using Statistical Methods](https://mlsanity.com/paper/2208.13166/) (2022)
- [A Bayesian Approach for the Network Reconstruction of Interdependent Critical Infrastructure Systems from Cascading Failures](https://mlsanity.com/paper/2211.15590/) (2022)
- [Fitting Low-rank Models on Egocentrically Sampled Partial Networks](https://mlsanity.com/paper/2303.11230/) (2023)

## Abstract (arXiv)

Link prediction is one of the fundamental problems in network analysis. In many applications, notably in genetics, a partially observed network may not contain any negative examples of absent edges, which creates a difficulty for many existing supervised learning approaches. We develop a new method which treats the observed network as a sample of the true network with different sampling rates for positive and negative examples. We obtain a relative ranking of potential links by their probabilities, utilizing information on node covariates as well as on network topology. Empirically, the method performs well under many settings, including when the observed network is sparse. We apply the method to a protein-protein interaction network and a school friendship network.