# A model for efficient dynamical ranking in networks

arXiv: https://arxiv.org/abs/2307.13544 · HTML: https://mlsanity.com/paper/2307.13544/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 35 references that are in the map.
- First posted on arXiv 25 Jul 2023.

## Builds on (most cited first, 4 of 4)

- [A physical model for efficient ranking in networks](https://mlsanity.com/paper/1709.09002/) (2017)
- [Dynamic Ranking and Translation Synchronization](https://mlsanity.com/paper/2207.01455/) (2022)
- [The interplay between ranking and communities in networks](https://mlsanity.com/paper/2112.12670/) (2021)
- [Resolution of ranking hierarchies in directed networks](https://mlsanity.com/paper/1608.06135/) (2016)

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

- [Estimation of partial rankings from sparse, noisy comparisons](https://mlsanity.com/paper/2501.02505/) (2025)

## Abstract (arXiv)

We present a physics-inspired method for inferring dynamic rankings in directed temporal networks - networks in which each directed and timestamped edge reflects the outcome and timing of a pairwise interaction. The inferred ranking of each node is real-valued and varies in time as each new edge, encoding an outcome like a win or loss, raises or lowers the node's estimated strength or prestige, as is often observed in real scenarios including sequences of games, tournaments, or interactions in animal hierarchies. Our method works by solving a linear system of equations and requires only one parameter to be tuned. As a result, the corresponding algorithm is scalable and efficient. We test our method by evaluating its ability to predict interactions (edges' existence) and their outcomes (edges' directions) in a variety of applications, including both synthetic and real data. Our analysis shows that in many cases our method's performance is better than existing methods for predicting dynamic rankings and interaction outcomes.