Comments (6)
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Will it be possible to predict the link based on the node embedding Z_i, Z_j
using the metric learning?
Where in the case that we have learned the node embedding from the subgraph, and learn a metric to predict the existence of a link, say f(Z_i, Z_j) = Z_i' W Z_j
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from gae.
Got it, thanks for your information.
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Why can't we just try to generate the graph from scratch via simulation.
I would like to use these methods to create an ensemble of different graphs that are close to the original graph rather than complete missing edges.
Mainly to compare the statistical properties and solve degeneracy problems with ERGMs.
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Related Issues (20)
- How could I get the output graph? HOT 1
- invoice or id card structure prediction
- Can GAE work in inductive setting? HOT 1
- Graph embedding vs Node embedding
- Does it work with Tensorflow 2 and Python 3? HOT 3
- Enforcing Sparsity
- Use continuous feature values HOT 1
- A question about negative samples generation in preprocessing.py HOT 4
- How can I reproduce the experiment using only the adjacency matrix without node features? HOT 2
- Potential Problem about the KL term? HOT 1
- About reduce links of the graph.
- KL divergence loss
- Descriptions of x, tx, allx?
- About Dataset Splits
- No such file or directory: 'data/ind.cora.x'
- Experiment with the weighted adjacency matrix
- Issues in preprocessing.py (not 100% sure)
- How to visualize the latent space?
- How to use GAE to generate nodes embeddings
- Problems I encountered in the train.py program HOT 1
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