Comments (3)
I will add a warning that, if the graph does not have the necessary self loops, it will be cloned and properly adapted.
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The algorithm necessarily adds the diagonal identity matrix to the adiacendy matrix. That is why you get self loops and 24 more edges.
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True, I think it may be also better to do this to a copy and avoid changing the input graph itself. Thanks a lot for the implementation and the quick reply!
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Related Issues (20)
- Estimator _check_graph method
- g2vec_model.infer([test_graph]) returns different embeddings each time I call it HOT 2
- IGE Laplacian Issue for small graphs HOT 1
- Dependency update HOT 16
- gl2vec doc’s may be inconsistent with the code HOT 4
- How are ego-nets created
- cannot import name '_rename_parameter' from 'scipy._lib._util' HOT 6
- Inductive Node Embedding Algorithms in KarateClub HOT 2
- numpy version error HOT 10
- How to retrieve the mapping between nodes and embeddings with get_embedding() ? HOT 1
- Seed and random results for embeddings HOT 1
- Add set_params to Estimator base class
- Pandas version
- AssertionError: The node indexing is wrong. HOT 4
- [Graph2Vec] the right way to build the attributed graph... HOT 1
- Graph2Vec—— how the attributed nodes are transformed to subgraph and word?
- GLEE - the sign of embedding values changes for repeat fits
- Feather-Graph with attributed graphs
- Cut new release 1.3.4 [or 1.4.0?] HOT 1
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