Introduction to Graph-based Deep Learning Literature
The Graph-based Deep Learning Literature repository is a comprehensive hub for researchers and enthusiasts interested in the intersection of graph structures and deep learning techniques. It consolidates a vast array of conference publications, workshops, literature reviews, books, and software that are pivotal in advancing the graph-based deep learning field.
Core Content
At the heart of this repository are conference publications related to graph-based deep learning. These publications are carefully categorized by year and conference, providing a structured way to access cutting-edge research and developments. This makes it an invaluable resource for anyone looking to stay updated with the latest breakthroughs in the field.
Additional Resources
Beyond conference papers, the repository offers links to:
- Related Workshops: These workshops provide additional insights and discussions on specialized topics within graph-based deep learning.
- Surveys, Literature Reviews, and Books: For those new to the field or looking to gain a deeper understanding, these resources compile existing knowledge into comprehensive reviews and books.
- Software and Libraries: Essential tools and libraries that aid in implementing graph-based deep learning models are also listed, supporting practical applications and research.
Structured by Conference and Year
The publications are further organized into distinct categories based on the conference and the year, reflecting the evolution and growth of the field across several prestigious venues:
-
Machine Learning Conferences: This includes conferences like NeurIPS, ICML, and ICLR, all of which host groundbreaking research and developments in machine learning methodologies, including graph-based techniques.
-
Data Mining Conferences: Conferences such as KDD, ICDM, and WSDM focus on mining insights from large datasets, with graph-based methods being integral to such analyses.
-
Artificial Intelligence Conferences: Prestigious forums such as TheWebConf, AAAI, and IJCAI host innovations in AI, where graph deep learning continues to be a key player.
-
Computer Vision Conferences: With venues like CVPR, ICCV, and ECCV, this area explores visual data interpretation using graphs.
-
Computational Linguistics Conferences: Conferences such as ACL and EMNLP examine natural language processing, leveraging graph deep learning to understand and generate human language.
Conclusion
By compiling such an extensive range of resources, the Graph-based Deep Learning Literature repository serves as a beacon for researchers, developers, and students alike. It supports the exploration of diverse graph-based deep learning approaches, fostering innovation and collaboration across disciplines. Whether you are delving into the scientific literature, attending workshops, or utilizing software tools, this repository offers a rich starting point for your journey into graph-based deep learning.