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The latent representation is the simplified model of your input data, for example, created by a neural network. Considering an autoencoder, the central layer of this network (after training) will contain a simplified representation of the input data (i.e. summary of key features), which can be used to reconstruct the output. If we take a dictionary ...


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In inductive learning, during training you are unaware of the nodes used for testing. For the specific inductive dataset here (PPI), the test graphs are disjoint and entirely unseen by the GNN during training.


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Does the algorithm consider as neighbourhood only the green nodes(test set) or it also considers the blue node's? It does consider both blue nodes and green nodes. Note tha GNN deals with transductive learning, where the test data(nodes here) is seen (without knowing the labels) during training. What you might have in mind is inductive learning(train set ...


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First, let me give you an intuitive explanation. When you drop a pebble in water, you see the ripples being formed. Imagine that in reverse. All those ripples comes together at the point from which they started. Node embeddings are like that. You take the information of neighbourhood nodes and combine it with the information in the original node. The art ...


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Your graph is characterized by a set of nodes and edges. It means given a start point x, show me the next node z1, then from z1 show me z2 and so on. This looks like the definition of graph-searching algorithm. However, maybe by definining a 'cost function' that you want to optimize (minimize or maximize) you can convert it to a supervised learning task. ...


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You can represent this type of knowledge graph as a binary $n \times r \times n$ tensor. (You can think of this as a 3D matrix if it helps.) The first dimension is for the node on the left side of the relationship, the second dimension is for the relation type, and the third dimension is for the node on the right side of the relationship. Then you can ...


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