  The particle-flow (PF) algorithm is used in general-purpose particle
detectors to reconstruct a comprehensive particle-level view of the collision
by combining information from different subdetectors. A graph neural network
(GNN) model, known as the machine-learned particle-flow (MLPF) algorithm, has
been developed to substitute the rule-based PF algorithm. However,
understanding the model's decision making is not straightforward, especially
given the complexity of the set-to-set prediction task, dynamic graph building,
and message-passing steps. In this paper, we adapt the layerwise-relevance
propagation technique for GNNs and apply it to the MLPF algorithm to gauge the
relevant nodes and features for its predictions. Through this process, we gain
insight into the model's decision-making.
