  Liquid Argon Time Projection Chambers (LArTPCs) are a class of detectors that
produce high resolution images of charged particles within their sensitive
volume. In these images, the clustering of distinct particles into
superstructures is of central importance to the current and future neutrino
physics program. Electromagnetic (EM) activity typically exhibits spatially
detached fragments of varying morphology and orientation that are challenging
to efficiently assemble using traditional algorithms. Similarly, particles that
are spatially removed from each other in the detector may originate from a
common interaction. Graph Neural Networks (GNNs) were developed in recent years
to find correlations between objects embedded in an arbitrary space. The Graph
Particle Aggregator (GrapPA) first leverages GNNs to predict the adjacency
matrix of EM shower fragments and to identify the origin of showers, i.e.
primary fragments. On the PILArNet public LArTPC simulation dataset, the
algorithm achieves achieves a shower clustering accuracy characterized by a
mean adjusted Rand index (ARI) of 97.8 % and a primary identification accuracy
of 99.8 %. It yields a relative shower energy resolution of $(4.1+1.4/\sqrt{E
(\text{GeV})})\,\%$ and a shower direction resolution of
$(2.1/\sqrt{E(\text{GeV})})^{\circ}$. The optimized algorithm is then applied
to the related task of clustering particle instances into interactions and
yields a mean ARI of 99.2 % for an interaction density of
$\sim\mathcal{O}(1)\,m^{-3}$.
