Graph Neural Networks for Astrophysics and Cosmology

Graph Neural Networks (GNNs) are presented as a natural framework for modeling the relational structure inherent in astrophysical and cosmological data, such as galaxies in their environments or photons in detectors. The review argues that GNNs align with the native geometry of these domains, incorporate existing symmetries and prior knowledge, and remain flexible enough to capture unknown phenomena, offering greater interpretability than other deep‑learning models. It surveys applications across cosmology, galaxy formation, dynamics, and instrumentation, noting that only about one in six non‑instrumentation papers apply GNNs to real data rather than simulations, and provides best‑practice guidelines for when to use or avoid graph approaches.

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