Comments (3)
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This model only works when You have a single string feature. However, You can generalize it to multi feature cases (for string/categorical features). If You extract WL node features for each node feature separately (You can rewrite it easily) -- each string feature must get a "feature_x_" prefix. The feature extraction and optimization runtime will be exactly multiplied by the number of features (assuming no down sampling). May I ask what is the exact use case?
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Yes you can make it work for an inductive setting if all of the node features have been seen up to that point.
from graph2vec.
Thanks for your reply.
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I have no idea how to modify for now but will try. My use case is programming language source code structures. Each node is a parsed token.
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Got it, will try this as well.
from graph2vec.
- Are you writing a paper?
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.