@inproceedings{f7f3f2cbaf8f4424a51de900632d53e8,
title = "Extracting Semantic Information from Dynamic Graphs of Geometric Data",
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.",
keywords = "Dynamic networks, Geometric data, Machine learning, Networks",
author = "Dabke, {Devavrat Vivek} and Bernard Chazelle",
note = "Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 10th International Conference on Complex Networks and Their Applications, COMPLEX NETWORKS 2021 ; Conference date: 30-11-2021 Through 02-12-2021",
year = "2022",
doi = "10.1007/978-3-030-93413-2_40",
language = "English (US)",
isbn = "9783030934125",
series = "Studies in Computational Intelligence",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "474--485",
editor = "Benito, {Rosa Maria} and Chantal Cherifi and Hocine Cherifi and Esteban Moro and Rocha, {Luis M.} and Marta Sales-Pardo",
booktitle = "Complex Networks and Their Applications X - Volume 2, Proceedings of the 10th International Conference on Complex Networks and Their Applications COMPLEX NETWORKS 2021",
address = "Germany",
}