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chihming avatar

chihming/awesome-network-embedding

0
View on GitHub↗
2,625 stars·491 forks·14 views

Awesome Network Embedding

A curated list of network embedding techniques.

Features

  • Awesome List - A community-curated directory that catalogs and links out to other open-source projects, rather than a standalone tool you run yourself.
  • Graph Analysis - Techniques and papers for learning vector representations of network nodes.
  • Graph Machine Learning - Techniques for mapping network nodes into low-dimensional vector spaces.
  • Graph Representation Learning - Collection of papers and tools for network embedding.
  • Curated Resource Lists - Methods and papers for learning vector representations of networks.
  • Research and Examples - Curated list of node-level network embedding research.

Star history

Star history chart for chihming/awesome-network-embeddingStar history chart for chihming/awesome-network-embedding

How this analysis was created: This summary and feature list are AI-generated from collected project material and can contain mistakes. Stars, license and language are imported from GitHub. Inclusion does not mean that we have tested or audited this project. Check the source documentation for any feature you depend on. Learn more on our About page.

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Projects sharing features with Awesome Network Embedding

These projects share indexed features with Awesome Network Embedding. Shared tags can include platform or build tooling; verify the primary use case before treating a result as a replacement.
  • benedekrozemberczki/awesome-graph-classificationbenedekrozemberczki avatar

    benedekrozemberczki/awesome-graph-classification

    4,805View on GitHub↗

    A collection of important graph embedding, classification and representation learning papers with implementations.

    Pythonattention-mechanismclassification-algorithmdeep-graph-kernels
    View on GitHub↗4,805
  • benedekrozemberczki/awesome-community-detectionbenedekrozemberczki avatar

    benedekrozemberczki/awesome-community-detection

    2,447View on GitHub↗

    A curated list of community detection research papers with implementations.

    Pythonbigclamclusteringcommunity-detection
    View on GitHub↗2,447
  • benedekrozemberczki/awesome-decision-tree-papersbenedekrozemberczki avatar

    benedekrozemberczki/awesome-decision-tree-papers

    2,473View on GitHub↗

    A collection of research papers on decision, classification and regression trees with implementations.

    Python
    View on GitHub↗2,473
  • benedekrozemberczki/awesome-fraud-detection-papersbenedekrozemberczki avatar

    benedekrozemberczki/awesome-fraud-detection-papers

    1,807View on GitHub↗

    A curated list of data mining papers about fraud detection.

    Python
    View on GitHub↗1,807
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Frequently asked questions

What does chihming/awesome-network-embedding do?

A curated list of network embedding techniques.

What are the main features of chihming/awesome-network-embedding?

The main features of chihming/awesome-network-embedding are: Awesome List, Graph Analysis, Graph Machine Learning, Graph Representation Learning, Curated Resource Lists, Research and Examples.

Which projects share features with chihming/awesome-network-embedding?

Projects with overlapping indexed features include: benedekrozemberczki/awesome-community-detection — A curated list of community detection research papers with implementations. benedekrozemberczki/awesome-graph-classification — A collection of important graph embedding, classification and representation learning papers with implementations. benedekrozemberczki/awesome-fraud-detection-papers — A curated list of data mining papers about fraud detection. benedekrozemberczki/awesome-monte-carlo-tree-search-papers — A curated list of Monte Carlo tree search papers with implementations. benedekrozemberczki/awesome-decision-tree-papers — A collection of research papers on decision, classification and regression trees with implementations. benedekrozemberczki/awesome-gradient-boosting-papers — A curated list of gradient boosting research papers with implementations.