Graph Based Deep Learning Literature
A curated collection of links to conference publications, surveys, and software in graph-based deep learning.
What is Graph Based Deep Learning Literature?
Graph-based Deep Learning Literature is a curated repository of links to academic publications, surveys, and software in the domain of graph-based deep learning. It aggregates research from top-tier conferences and organizes them by conference, year, and topic to help researchers and developers efficiently navigate the literature. The project addresses the challenge of keeping up with the fast-paced advancements in graph neural networks and related methodologies.
Researchers, graduate students, and practitioners in machine learning, artificial intelligence, and data science who are focused on graph-based deep learning, graph neural networks, or related subfields.
It provides a centralized, structured, and up-to-date index of graph-based deep learning resources, saving time on literature searches and offering a historical overview of the field's evolution. Unlike generic academic search engines, it is specifically tailored to graph-based deep learning and includes curated lists of top-cited papers.
Overview
links to conference publications in graph-based deep learning
Use Cases
Best For
- Finding foundational and recent papers on graph neural networks
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