You will be shown an excerpt from a computer science scientific research paper, followed by a question about the paper. Please answer the question. Do not include any text in your response other than the answer.

Context: Our entity classification model, similarly to ? (?), uses softmax classifiers at each node in the graph. The classifiers take node representations supplied by a relational graph convolutional network (R-GCN) and predict the labels. The model, including R-GCN parameters, is learned by optimizing the cross-entropy loss. Our link prediction model can be regarded as an autoencoder consisting of (1) an encoder: an R-GCN producing latent feature representations of entities, and (2) a decoder: a tensor factorization model exploiting these representations to predict labeled edges. Though in principle the decoder can rely on any type of factorization (or generally any scoring function), we use one of the simplest and most effective factorization methods: DistMult (?). We observe that our method achieves competitive results on standard benchmarks, outperforming, among other baselines, direct optimization of the factorization (i.e. vanilla DistMult). This improvement is especially large when we consider the more challenging FB15k-237 dataset (?). This result demonstrates that explicit modeling of neighborhoods in R-GCNs is beneficial for recovering missing facts in knowledge bases.

Question: What is the structural difference when applying RGCN to two tasks(entity classification, link prediction)?
There is a structural difference between RGCN according to its tasks.
The reason is that RGCN use softmax classifier to classify entities, supported by P3, whereas it requires a tensor factorization decoder when it comes to predict links as P7.