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: In this section, we describe the embedding generation, or forward propagation algorithm (Algorithm 1), which assumes that the model has already been trained and that the parameters are fixed.In particular, we assume that we have learned the parameters of K aggregator functions (denoted \textsc{aggregate}_{k},\forall k\in\{1,...,K\}), which aggregate information from node neighbors, as well as a set of weight matrices \mathbf{W}^{k},\forall k\in\{1,...,K\}, which are used to propagate information between different layers of the model or “search depths”.Section 3.2 describes how we train these parameters. Instead of training a distinct embedding vector for each node, we train a set of aggregator functions that learn to aggregate feature information from a node’s local neighborhood (Figure 1).Each aggregator function aggregates information from a different number of hops, or search depth, away from a given node.At test, or inference time, we use our trained system to generate embeddings for entirely unseen nodes by applying the learned aggregation functions.Following previous work on generating node embeddings, we design an unsupervised loss function that allows GraphSAGE to be trained without task-specific supervision.We also show that GraphSAGE can be trained in a fully supervised manner. Paragraph 10 :

Question: In what ways can it be said that the concatenation acts as a skip connection?
Skip connection is to consider information from different search depths or layers simultaneously.
GraphSAGE use a set of weight matrices and concatenation to consider information from diverse search depths. 
It can be interpreted as a skip connection.
The reason is that a set of weight matrices are used to propagate information 
 between different layers of the model or search depths, while considering different search depth is a kind of skip-connection.