Comments (6)
from gae.
Thans for your reply and guidance. I still have some questions and would like to trouble you for advice.
objective:
The graph in my experiment is a fully connected graph, each edge has a non-zero weight, and each node has two attributes (values greater than 1), I want to get the node representation(embeddings) based on the GAE model,and further do node clustering.
questions:
At the moment, I have finished loading the data, but I am having trouble selecting the edge as a negative sample. I don't know how to select the edge or its weight as a negative sample.
As you suggested above, Is it necessary to set a threshold for the weight of the edge? An edge larger than the threshold is used as a positive sample, instead, an edge smaller than the threshold is used as a negative sample. Could I make out in this way?
I hope that you can give some advice and guidance and look forward to your reply.
The text file in the attachment stores the adjacency matrix used in my experiment.
adj.txt
from gae.
from gae.
That would really help me a lot. I'm very grateful.
from gae.
Hello, I am also using the GAE model on adjacency matrix data with continuous positive weights. I ran into a problem while training the network to create reconstructions of the original adjacency matrix. For some reason, despite changing the activation function of the InnerProductDecoder from sigmoid to relu, I get reconstructions that contain only binary values of 0 or 1 instead of continuous weights.
Does the current framework support reconstructions of continuously weighted adjacency matrix? Thank you so much for your time.
from gae.
from gae.
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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from gae.