Comments (2)
A Doc2Vec type model factorizes a matrix with non zero elements implicitly. This matrix is a document-word (document-term) matrix where rows are documents and columns denote how many times a word appeared in a document. Doc2Vec does this decomposition by doing a form of noise-contrastive estimation called negative sampling. This technique requires zero elements of the target matrix that you can sample from each row as negative samples. Graph2Vec factorizes a matrix where the documents are graphs and the words in the documents ar WL features. Your document-term matrix has identical rows with positive elements because of your experimental design. Because of how the gradients interact - similar features pull graphs closer but the negative sampling pushes them away (in this case these are not good negative samples, because of your experimental design you sample factual features) and the graphs would be laid out uniformly in the embedding space around the origin.
Originally I ran diagnostics on this tool with simulated Watts-Strogatz graphs with heterogeneous rewiring probability. Based on the learned representations I was able to predict the rewiring probability for the nodes. In your case, I would generate a few more classes of graphs and add some randomness within the classes. If this is a real world project I am happy to do consulting.
from graph2vec.
from graph2vec.
Related Issues (20)
- node and edge attributes HOT 2
- ValueError while using the default Dataset HOT 2
- Graph2Vec datasets HOT 1
- Visualisation of graph2vec embeddings in a network HOT 5
- Question about embeddings HOT 5
- Input JSON HOT 1
- Getting This Error When Running on a graph with 1304 nodes HOT 1
- [Question] Add PyPi package HOT 2
- worse results with latest version
- Error on executing graph2vec.py HOT 1
- how to get the graph dataset? HOT 1
- Using one example HOT 1
- RuntimeError: you must first build vocabulary before training the model HOT 1
- Graph2vec for graph similarity learning HOT 3
- graph encoding HOT 6
- model save and load HOT 1
- Can I use multiple features of a particular node? HOT 1
- how to generate embeddings of graphml or graphson files as input using your library?
- Graph2vec infer HOT 1
- What does the output file contain HOT 1
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from graph2vec.