Questions tagged [gnn]

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What is effect of having more edges on a GCN helps learning?

I am using a Graph2Seq GNN. For my case, I am using GCN as my encoder. For my graph, on the average: I have around 500 nodes in the graph per data point. In the current graph, on average, a node is ...
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Why is ROC-AUC usually shown in GNN papers

In various graph neural network (GNNs) papers, the ROC-AUC metric is usually shown alone without considering F1 or Accuracy. Is there a reason for that? What does it say about two models 1 and 2 with ...
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Graph Neural Network | How node embeddings are learned from several graphs?

I am reading paper on MEGnet which is a GNN. The objective is that we have several molecules that share same elements such as molecules $C0_2$ and $COOH$ share $C$ and $O$. Now if we learn the node ...
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When an author says Features are the input to Machine Learning Model what does it mean?

I am reading an article about graph neural network and it is mentioned: In this step, we extract all newly update hidden states and create a final feature vector describing the whole graph. This ...
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How to define similarity between nodes in original graph?

While there has been a lot of talk in how to define the similarity between nodes in the embedding space, but I don't seem to come across any talking about defining the similarity between nodes in the ...
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What is the model architectural difference between transductive GCN and inductive GraphSAGE?

Difference of the model design. It seems the difference is that GraphSAGE sample the data. But what is the difference in model architecture.
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What is difference between transductive and inductive in GNN?

It seems in GNN(graph neural network), in transductive situation, we input the whole graph and we mask the label of valid data and predict the label for the valid data. But is seems in inductive ...
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