Collective Relational Inference for learning heterogeneous interactions
- URL: http://arxiv.org/abs/2305.00557v3
- Date: Tue, 23 Jan 2024 21:32:30 GMT
- Title: Collective Relational Inference for learning heterogeneous interactions
- Authors: Zhichao Han, Olga Fink, David S. Kammer
- Abstract summary: We propose a novel probabilistic method for relational inference, which possesses two distinctive characteristics compared to existing methods.
We evaluate the proposed methodology across several benchmark datasets and demonstrate that it outperforms existing methods in accurately inferring interaction types.
Overall the proposed model is data-efficient and generalizable to large systems when trained on smaller ones.
- Score: 8.215734914005845
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Interacting systems are ubiquitous in nature and engineering, ranging from
particle dynamics in physics to functionally connected brain regions. These
interacting systems can be modeled by graphs where edges correspond to the
interactions between interactive entities. Revealing interaction laws is of
fundamental importance but also particularly challenging due to underlying
configurational complexities. The associated challenges become exacerbated for
heterogeneous systems that are prevalent in reality, where multiple interaction
types coexist simultaneously and relational inference is required. Here, we
propose a novel probabilistic method for relational inference, which possesses
two distinctive characteristics compared to existing methods. First, it infers
the interaction types of different edges collectively by explicitly encoding
the correlation among incoming interactions with a joint distribution, and
second, it allows handling systems with variable topological structure over
time. We evaluate the proposed methodology across several benchmark datasets
and demonstrate that it outperforms existing methods in accurately inferring
interaction types. We further show that when combined with known constraints,
it allows us, for example, to discover physics-consistent interaction laws of
particle systems. Overall the proposed model is data-efficient and
generalizable to large systems when trained on smaller ones. The developed
methodology constitutes a key element for understanding interacting systems and
may find application in graph structure learning.
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