2 research outputs found
Multiresolution Tensor Decomposition for Multiple Spatial Passing Networks
This article is motivated by soccer positional passing networks collected
across multiple games. We refer to these data as replicated spatial passing
networks---to accurately model such data it is necessary to take into account
the spatial positions of the passer and receiver for each passing event. This
spatial registration and replicates that occur across games represent key
differences with usual social network data. As a key step before investigating
how the passing dynamics influence team performance, we focus on developing
methods for summarizing different team's passing strategies. Our proposed
approach relies on a novel multiresolution data representation framework and
Poisson nonnegative block term decomposition model, which automatically
produces coarse-to-fine low-rank network motifs. The proposed methods are
applied to detailed passing record data collected from the 2014 FIFA World Cup.Comment: 34 pages, 15 figure
Detecting Structural Changes in Longitudinal Network Data
Dynamic modeling of longitudinal networks has been an increasingly important
topic in applied research. While longitudinal network data commonly exhibit
dramatic changes in its structures, existing methods have largely focused on
modeling smooth topological changes over time. In this paper, we develop a
hidden Markov multilinear tensor model (HMTM) that combines the multilinear
tensor regression model (Hoff 2011) with a hidden Markov model using Bayesian
inference. We model changes in network structure as shifts in discrete states
yielding particular sets of network generating parameters. Our simulation
results demonstrate that the proposed method correctly detects the number,
locations, and types of changes in latent node characteristics. We apply the
proposed method to international military alliance networks to find structural
changes in the coalition structure among nations