A model for efficient dynamical ranking in networks
Andrea Della Vecchia Kibidi Neocosmos Daniel B. Larremore Cristopher Moore Caterina De Bacco
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.