Structural Inference; Benchmark; AI4Science; Interaction Graphs; Dynamical Systems
Résumé :
[en] Understanding complex dynamical systems begins with identifying their topological structures, which expose the organization of the systems. This requires robust structural inference methods that can deduce structure from observed behavior. However, existing methods are often domain-specific and lack a standardized, objective comparison framework. We address this gap by benchmarking 13 structural inference methods from various disciplines on simulations representing two types of dynamics and 11 interaction graph models, supplemented by a biological experimental dataset to mirror real-world application. We evaluated the methods for accuracy, scalability, robustness, and sensitivity to graph properties. Our findings indicate that deep learning methods excel with multi-dimensional data, while classical statistics and information theory based approaches are notably accurate and robust. Additionally, performance correlates positively with the graph's average shortest path length. This benchmark should aid researchers in selecting suitable methods for their specific needs and stimulate further methodological innovation.
Disciplines :
Sciences informatiques
Auteur, co-auteur :
WANG, Aoran ✱; University of Luxembourg > Faculty of Science, Technology and Medicine > Department of Computer Science > Team Jun PANG
TONG, Tsz Pan ✱; University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Computer Science (DCS)
MIZERA, Andrzej; IDEAS-NCBR > IDEAS-NCBR ; Institute of Informatics > Faculty of Mathematics, Informatics and Mechanics > University of Warsaw
PANG, Jun ; University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Computer Science (DCS)
✱ Ces auteurs ont contribué de façon équivalente à la publication.
Co-auteurs externes :
yes
Langue du document :
Anglais
Titre :
Benchmarking Structural Inference Methods for Interacting Dynamical Systems with Synthetic Data
Date de publication/diffusion :
26 septembre 2024
Nom de la manifestation :
38th Conference on Neural Information Processing Systems
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