A model for efficient dynamical ranking in networks

TL;DR

This paper introduces a physics-inspired, scalable method for inferring dynamic rankings in directed temporal networks, effectively predicting interactions and outcomes in various real and synthetic datasets.

Contribution

The authors propose a novel linear system-based approach for dynamic ranking inference that is efficient, requires minimal parameter tuning, and outperforms existing methods in prediction tasks.

Findings

Method accurately predicts interaction existence and outcomes.

Scalable and efficient algorithm suitable for large networks.

Outperforms existing methods in various datasets.

Abstract

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.