3 votes
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What is difference between transductive and inductive in GNN?

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 ...
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  • 561
2 votes

What are latent representations?

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 ...
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  • 691
2 votes
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How to perform inductive train/test split for GraphSAGE classification

If I understood you correctly, you are looking to do a kind of stratified sampling, and would like to create the strata or clusters to perform train/ test splits over the identified "highly ...
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2 votes

How to perform inductive train/test split for GraphSAGE classification

I don't have a tone of experience in graph based ML applications, so correct me if I'm oversimplifying, but to restate the problem: You have a graph of weakly connected nodes You want to use ...
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  • 420
2 votes

How to perform inductive train/test split for GraphSAGE classification

One option would be using an existing package that is designed to train/test split graphs while maintaining class rates. For example, the PyG (PyTorch Geometric) package has RandomNodeSplit class ...
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1 vote
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Normalizing softmax by dividing by its maximum?

You are not missing, the text seems to miss explanation. The important takeaway is how do use Fuzzy logic where embedding values are nearly same for two classes. The example discussed: h1 =[-1.2,2.3]...
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  • 141
1 vote

How to perform inductive train/test split for GraphSAGE classification

If you are using Pytorch geometric library, checkout https://pytorch-geometric.readthedocs.io/en/latest/modules/loader.html?highlight=ClusterData#torch_geometric.loader.ClusterData, it cluster data in ...
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1 vote
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How train - test split works for Graph Neural Networks

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 ...
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  • 126
1 vote

Understanding Node Embeddings

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 ...
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1 vote

From Labels to Graph: what machine learning approaches to use?

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 ...
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  • 11
1 vote

Knowledge Graph as an input to a neural network

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 ...
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  • 2,349

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