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Context: I was reading Chapter 3 in the following book (here) about graph representation learning. Before I get to node embeddings, I wanted to make sure that I do understand what is meant by the phrase 'node features' used numerous times throughout the book. Examples are as follows:

Chapter 5, page 50:

Node Features: note that unlike the shallow embedding methods discussed in Part I of this book, the GNN framework requires that we node features $\mathbf{x}_{u}$, $\forall u \in \mathcal{V}$ as input to the model. In many graphs we will have rich node features to use (e.g. gene expression features in biological networks or text features in social networks)....

Question: What is a simple, concrete example of different node features? I have read the paragraph above, but I am not sure whether I have interpreted it correctly. For example, if we imagine a social network of some friends, would some example node features be: address, age, height, weight, etc.? Would it be as simple as that? What are some more advanced/subtle bits of information which could be counted as node features. Perhaps one could be 'number of friends' (i.e. the degree of the node), but what about others.

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