what is awesome-self-supervised-learning-for-graphs fr?

This repository is a curated reading list for anyone interested in self-supervised learning applied to graphs. It collects academic papers, blog posts, and talks so you don't have to hunt them down yourself. Graphs are a way to represent connected data, think social networks, molecular structures, or transportation maps. Normally, machine learning models need lots of labeled examples to understand graph data, which is expensive and time-consuming to produce. Self-supervised learning is an approach where models teach themselves by finding patterns in the data without requiring human-labeled examples. This list tracks the latest research on how to make that work for graph-shaped data. The collection organizes papers into a few main categories. Surveys give you a broad overview of the field. The rest of the papers are split into two approaches: generative or predictive methods, which try to reconstruct or predict parts of the graph, and contrastive methods, which learn by comparing different views of the graph against each other. There's also a section for real-world applications like drug interaction prediction and recommendation systems. Researchers, data scientists, and graduate students would find this useful. If you're building a recommendation engine, detecting fraud in financial networks, or working on drug discovery, and you want to avoid the cost of labeling massive datasets, this list points you to the relevant techniques. It also includes links to blog posts and talks for people who want a gentler introduction before diving into dense academic papers. The list is community-driven, meaning anyone can submit new papers through a pull request. It leans heavily into recent work, with most papers from 2019 to 2021, reflecting how new this subfield is. If you want to understand the cutting edge of teaching machines to understand connected data without labels, this is a solid starting point.