We implemented an iterative approach to overcome this hardware limitation that involved constructing the graphs in batches. We began by building the multigraphs using the first batch of data. As we progressed to subsequent batches, we updated the one-hot encoded features of the previously processed 𝑛 -1 batches whenever we encountered new features. This ensured that we maintained the up-to-date encoding of the previous batches while also generating new encoding for the current batch. Specifically, if the feature set of the new batch 𝑛 (denoted as 𝐿) was an improper subset of the feature set from the previous 𝑛 -1 batches (denoted as 𝑀), the node's feature vector is not expanded because it already includes the required features.