naganandy/graph-based-deep-learning-literature
A curated collection of links to conference publications, workshops, surveys, and software libraries in graph-based deep learning.
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This repository aggregates academic papers and resources on graph-based deep learning, organizing publications by conference (NeurIPS, ICLR, ICML, KDD, etc.) and year. It categorizes papers by topic including graph convolutional networks, graph representation learning, and graph neural networks. The repository also links to related workshops, surveys, books, and software libraries in the field.
Frequently asked
- What is naganandy/graph-based-deep-learning-literature?
- A curated collection of links to conference publications, workshops, surveys, and software libraries in graph-based deep learning.
- Is graph-based-deep-learning-literature open source?
- Yes — naganandy/graph-based-deep-learning-literature is open source, released under the MIT license.
- What language is graph-based-deep-learning-literature written in?
- naganandy/graph-based-deep-learning-literature is primarily written in Jupyter Notebook.
- How popular is graph-based-deep-learning-literature?
- naganandy/graph-based-deep-learning-literature has 5.1k stars on GitHub.
- Where can I find graph-based-deep-learning-literature?
- naganandy/graph-based-deep-learning-literature is on GitHub at https://github.com/naganandy/graph-based-deep-learning-literature.