  The identification of jets and their constituents is one of the key problems
and challenging task in heavy ion experiments such as experiments at RHIC and
LHC. The presence of huge background of soft particles pose a curse for jet
finding techniques. The inabilities or lack of efficient techniques to filter
out the background lead to a fake or combinatorial jet formation which may have
an errorneous interpretation. In this article, we present Graph Reduction
technique (GraphRed), a novel class of physics-aware and topology-based
attention graph neural network built upon jet physics in heavy ion collisions.
This approach directly works with the physical observables of variable-length
set of final state particles on an event-by-event basis to find most likely
jet-induced particles in an event. This technique demonstrate the robustness
and applicability of this method for finding jet-induced particles and show
that graph architectures are more efficient than previous frameworks. This
technique exhibit foremost time a classifier working on particle-level in each
heavy ion event produced at the LHC. We present the applicability and
integration of the model with current jet finding algorithms such as FastJet.
