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Network Embedding Exploration Tool (NEExT)

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Modelling and Mining Networks (WAW 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14671))

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Abstract

In this paper, we introduce NEExT(Network Embedding Exploration Tool) for embedding collections of graphs via user-defined node features. The advantages of the framework are twofold: (i) the ability to easily define your own interpretable node-based features in view of the task at hand, and (ii) fast embedding of graphs provided by the Vectorizers library. In this exploratory work, we demonstrate the usefulness of NEExT on collections of synthetic and real-world graphs.

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Notes

  1. 1.

    https://pypi.org/project/NEExT/.

  2. 2.

    https://github.com/ashdehghan/LSME.

  3. 3.

    https://vectorizers.readthedocs.io.

  4. 4.

    https://pypi.org/project/vectorizers/.

  5. 5.

    https://github.com/bkamins/ABCDGraphGenerator.jl.

  6. 6.

    https://github.com/tolcz/ABCDeGraphGenerator.jl/.

  7. 7.

    https://xgboost.readthedocs.io/en/stable/.

  8. 8.

    https://umap-learn.readthedocs.io/en/latest/.

  9. 9.

    https://ls11-www.cs.tu-dortmund.de/staff/morris/graphkerneldatasets.

  10. 10.

    https://paperswithcode.com/task/graph-classification.

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Correspondence to Ashkan Dehghan .

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Dehghan, A., Prałat, P., Théberge, F. (2024). Network Embedding Exploration Tool (NEExT). In: Dewar, M., et al. Modelling and Mining Networks. WAW 2024. Lecture Notes in Computer Science, vol 14671. Springer, Cham. https://doi.org/10.1007/978-3-031-59205-8_5

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  • DOI: https://doi.org/10.1007/978-3-031-59205-8_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-59204-1

  • Online ISBN: 978-3-031-59205-8

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