  Topological analysis of the magnetic field in simulated plasmas allows the
study of various physical phenomena in a wide range of settings. One such
application is magnetic reconnection, a phenomenon related to the dynamics of
the magnetic field topology, which is difficult to detect and characterize in
three dimensions. We propose a scalable pipeline for topological data analysis
and spatiotemporal graph representation of three-dimensional magnetic vector
fields. We demonstrate our methods on simulations of the Earth's magnetosphere
produced by Vlasiator, a supercomputer-scale Vlasov theory-based simulation for
near-Earth space. The purpose of this work is to challenge the machine learning
community to explore graph-based machine learning approaches to address a
largely open scientific problem with wide-ranging potential impact.
