Use the following pieces of context to answer the question at the end. If you don't know the answer, just say that you don't know, don't try to make up an answer.

However, the direct implementation of HRGCN is not scalable for large datasets, and it can take over 8 hours to train on a full batch. To address this, we have implemented a mini-batch version of HRGCN that can run on GPUs, reducing the training time to around 2.5 hours while maintaining relatively better performance than full-batch training. The loss function used is focus loss, which is discussed in section 4.5. The mini-batch HRGCN model consists of several layers of HRGCNs. Each HRGCN layer takes as input a batch of nodes and edges and performs the above indicated operation (1). The input to the first layer is the feature matrix of the nodes in the batch. The output of the final layer is used to predict the labels of the nodes in the batch. Overall, the mini-batch HRGCN algorithm allows us to perform the HRGCN algorithm efficiently on large graphs by processing the graph in small, randomly selected batches. This reduces the computational complexity and memory requirements of the algorithm, while still maintaining its effectiveness in capturing the complex relationships between nodes in the graph. 4.3 Mini Batch HRGCN architecture In this section, we describe the mini-batch HRGCN algorithm implementation. 

Question: why is mini-batch HRGCN more efficient?