LiteratureDL4Graph

Why we included this project

Getting into graph deep learning means wading through a scattered literature, and this repository gathers the field's recent papers into one place. It groups hundreds of them into topical sections, from node representation learning and heterogeneous graphs to graph neural networks, generative models, and applications, with each entry listing the authors and venue plus keywords for quick scanning. That makes it a practical starting point for building a mental map of the area, whether you are comparing methods before choosing one for a project or assembling a related-work section. Sorting by topic or venue also helps you trace how ideas moved through different conferences. It is a curated reading list rather than runnable software, so treat it as a research companion for finding and comparing papers, not something to deploy.