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Since graph embedding often clusters similar nodes with similar centrality markers, would it be useful to just replace the whole embedding entirely in favor of something more human-comprehendable?
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From @caufieldjh:
To get graph embeddings (note this is just with grape - NEAT may be used to automate the process, but this is what runs):
Install grape: pip install grape
```
from grape.data…
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### Example Code
``` Python
# Creating Embdeddings of the sentences and storing it into Graph DB
from langchain_community.embeddings import HuggingFaceBgeEmbeddings
model_name = "BAAI/bge-ba…
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### Question Validation
- [X] I have searched both the documentation and discord for an answer.
### Question
I have gotten the following SCRIPT to work however I have a few questions about pre-proc…
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#### Expected behaviour
The bar chart should not be empty when embedding the HTML output.
#### Actual behaviour
In the browser (Highcharts Editor), we see the full graph (including the bars), but…
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### 🐛 Describe the bug
Hello, we have encountered a problem when using GCNConv and HypergraphConv for graph representation learning. We want to use GCNConv and HypergraphConv layers to process graph…
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I'm trying to test the Link prediction with Node2Vec algorithm on my graph. when I want to run a random walk on it, this error shows up:
```
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Hello there,
what is the correct way to separate training from inference?
Is this correct?
I run the training first, save the embeddings.
Then I load a new graph and do the most similar?
```
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As explained confirmation from all node2vec papers, this algorithm is designed for 1 graph. It create specific embedding for each graph, so it is not possible for training node2vec model on a graph, t…
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https://arxiv.org/pdf/1805.11273v1