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: We now consider the task of generalizing across graphs, which requires learning about node roles rather than community structure.We classify protein roles—in terms of their cellular functions from gene ontology—in variousprotein-protein interaction (PPI) graphs, with each graph corresponding to a different human tissue [41].We use positional gene sets, motif gene sets and immunological signatures as features and geneontology sets as labels (121 in total), collected from the Molecular Signatures Database [34].The average graph contains 2373 nodes, with an average degree of 28.8.We train all algorithms on 20 graphs and then average prediction F1 scores on two test graphs (with two other graphs used for validation). We evaluate our algorithm on three node-classification benchmarks, which test GraphSAGE’s ability to generate useful embeddings on unseen data.We use two evolving document graphs based on citation data and Reddit post data (predicting paper and post categories, respectively), and a multi-graph generalization experiment based on a dataset of protein-protein interactions (predicting protein functions).Using these benchmarks, we show that our approach is able to effectively generate representations for unseen nodes and outperform relevant baselines by a significant margin: across domains, our supervised approach improves classification F1-scores by an average of 51% compared to using node features alone and GraphSAGE consistently outperforms a strong, transductive baseline [28], despite this baseline taking ∼100×{\sim}100\times∼ 100 × longer to run on unseen nodes.We also show that the new aggregator architectures we propose provide significant gains (7.4% on average) compared to an aggregator inspired by graph convolutional networks [17].Lastly, we probe the expressive capability of our approach and show, through theoretical analysis, that GraphSAGE is capable of learning structural information about a node’s role in a graph, despite the fact that it is inherently based on features (Section 5).

Question: What are some examples of roles of node?
The roles of nodes can be their protein function or categories.
The reason is this paper classifies the function of proteins for PPI network, while classifies categories of nodes in Reddit network and Citation network.