# Network interpolation

arXiv: https://arxiv.org/abs/1905.01253 · HTML: https://mlsanity.com/paper/1905.01253/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 May 2019.

## Built on by (most cited first, 1 of 1)

- [Fast Learning of MNL Model from General Partial Rankings with Application to Network Formation Modeling](https://mlsanity.com/paper/2112.15575/) (2021)

## Abstract (arXiv)

Given a set of snapshots from a temporal network we develop, analyze, and experimentally validate a so-called network interpolation scheme. Our method allows us to build a plausible, albeit random, sequence of graphs that transition between any two given graphs. Importantly, our model is well characterized by a Markov chain, and we leverage this representation to analytically estimate the hitting time (to a predefined distance to the target graph) and long term behavior of our model. These observations also serve to provide interpretation and justification for a rate parameter in our model. Lastly, through a mix of synthetic and real-world data experiments we demonstrate that our model builds reasonable graph trajectories between snapshots, as measured through various graph statistics. In these experiments, we find that our interpolation scheme compares favorably to common network growth models, such as preferential attachment and triadic closure.