Macromolecules, such as naturally occurring and synthetic proteins and glycans, have diverse chemical structures, varying in monomer composition, connecting bonds and topology. In addition to the chemical diversity, macromolecules usually have opaque structure-activity relationships, making activity prediction and model attribution hard tasks. Recently, we proposed macromolecule graph representation learning, achieving state-of-the-art results in the immunogenicity classification of glycans. Here, we extend this framework to include attribution methods for graph neural networks. We evaluated the performance of 2 attribution methods over 3 model architectures, and an attention attribution for the attention-based model, and demonstrated it for an immunogenic glycan. Our work has two-fold implications - (1) provides attribution-backed chemical insights at the monomer and chemical substructure level, and (2) informs further in silico and wet-lab experiments.