The success of deep learning is highly dependent on large-scale labeled data. This presents a formidable challenge in fields like molecular design and materials science, where data annotation is prohibitively expensive. Consequently, developing label-efficient learning methods to maximize model performance under limited annotation budgets has recently become more and more critical.

However, most of the current mainstream label-efficient algorithms, like active learning and semi-supervised learning, are primarily designed for Euclidean data, such as images. They cannot effectively process the non-Euclidean graph-structured data, thus overlooking the rich topological information embedded within.

In this talk, we aim to bridge this gap through a progressive research path that addresses three core challenges in data annotation for graph-structured data. First, to address the high cost of annotation, we adapt active learning and semi-supervised learning from general domains to explicit graph data, enabling the precise labeling of high-value nodes. Second, to address label scarcity, we pioneer methods to construct and leverage implicit graph structures, propagating existing labels and generating new information to boost the performance of semi-supervised and self-supervised learning. Finally, to address label noise, we perform the fusion of both explicit and implicit graphs. By learning an implicit structure from noisy explicit graph data, our methods will identify and mitigate the impact of noise.