Introduction to the Awesome Self-Supervised Graph Neural Networks Project
The "awesome-self-supervised-gnn" project serves as a comprehensive repository, curating an extensive list of papers that explore the realm of self-supervised learning on Graph Neural Networks (GNNs). This project aims to be a resourceful guide for researchers, students, and professionals interested in this field, classifying the papers based on their publication years, ensuring an easy navigation through the advancements over time.
Key Features
Year-Based Categorization
The repository organizes papers primarily based on the year of publication, beginning from 2023 and onwards. This chronological approach not only helps in tracking the evolution of research in this field but also allows users to quickly access the most recent studies, ensuring that they are up-to-date with the latest advancements.
Community Contributions
An important aspect of this project is its community-driven nature. Researchers and practitioners are encouraged to participate by reporting any discrepancies or missing papers. This open-source collaboration is facilitated through GitHub, where users can open issues or submit pull requests to contribute to the repository’s continuous improvement.
Continuous Updates
The team behind this project commits to periodically updating the repository. This commitment guarantees that users have access to the most recent research papers in the field of GNNs, as new findings and techniques emerge.
Notable Papers Highlight
To aid users in finding impactful and influential work, papers that have been cited extensively (with more than 80 citations) are marked with a fire emoji 🔥. This feature helps in identifying seminal works that have contributed significantly to the field.
Selection of Papers Examples
Here are some examples of papers featured in the repository:
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2024:
- [ICASSP 2024] "Contrastive Deep Nonnegative Matrix Factorization for Community Detection" attempts to enhance community detection methodologies using contrastive learning techniques applied to matrix factorization.
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2023:
- [ICLR 2023] "Empowering Graph Representation Learning with Test-Time Graph Transformation" explores methods to enhance graph representation learning by introducing transformations at test time.
- [AAAI 2023] "Eliciting Structural and Semantic Global Knowledge in Unsupervised Graph Contrastive Learning" focuses on integrating global structural and semantic knowledge within unsupervised learning frameworks.
Conclusion
The "awesome-self-supervised-gnn" repository stands as a valuable and dynamic resource for anyone interested in the burgeoning field of self-supervised learning in GNNs. By providing carefully categorized and updated listings of relevant work, it bridges the gap between emerging research and the community, fostering a collaborative environment for innovation and discovery in this exciting area of machine learning.