Abstract
In this paper, we demonstrate the utility of dynamic network sequences to provide insight into geometric data; moreover, we construct a natural syntactic and semantic understanding of these network sequences for useful downstream applications. As a proof-of-concept, we study the trajectory data of basketball players and construct “interaction networks” to express an essential game mechanic: the ability for the offensive team to pass the ball to each other. These networks give rise to a library of player configurations that can in turn be modeled by a jump Markov model. This model provides a highly compressed representation of a game, while capturing important latent structures. By leveraging this structure, we use a Transformer to predict trajectories with increased accuracy.
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Dabke, D.V., Chazelle, B. (2022). Extracting Semantic Information from Dynamic Graphs of Geometric Data. In: Benito, R.M., Cherifi, C., Cherifi, H., Moro, E., Rocha, L.M., Sales-Pardo, M. (eds) Complex Networks & Their Applications X. COMPLEX NETWORKS 2021. Studies in Computational Intelligence, vol 1073. Springer, Cham. https://doi.org/10.1007/978-3-030-93413-2_40
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DOI: https://doi.org/10.1007/978-3-030-93413-2_40
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