# Data-driven Link Prediction over Graphical Models

arXiv: https://arxiv.org/abs/2004.14187 · HTML: https://mlsanity.com/paper/2004.14187/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Apr 2020.

## Built on by (most cited first, 2 of 2)

- [Learning Sparse High-Dimensional Matrix-Valued Graphical Models From Dependent Data](https://mlsanity.com/paper/2404.19073/) (2024)
- [Identification of Non-causal Graphical Models](https://mlsanity.com/paper/2410.09480/) (2024)

## Abstract (arXiv)

The positive link prediction (PLP) problem is formulated in a system identification framework: we consider dynamic graphical models for auto-regressive moving-average (ARMA) Gaussian random processes. For the identification of the parameters, we model our network on two different time scales: a quicker one, over which we assume that the process representing the dynamics of the agents can be considered to be stationary, and a slower one in which the model parameters may vary. The latter accounts for the possible appearance of new edges. The identification problem is cast into an optimization framework which can be seen as a generalization of the existing methods for the identification of ARMA graphical models. We prove the existence and uniqueness of the solution of such an optimization problem and we propose a procedure to compute numerically this solution. Simulations testing the performances of our method are provided.