Recent success of deep learning is largely attributed to the sheer amount of data used for
training deep neural networks. Despite the unprecedented success, the massive data …

Deep learning technology has developed unprecedentedly in the last decade and has
become the primary choice in many application domains. This progress is mainly attributed …

Graph neural networks, a powerful deep learning tool to model graph-structured data, have
demonstrated remarkable performance on numerous graph learning tasks. To address the …

Training on large-scale graphs has achieved remarkable results in graph representation
learning, but its cost and storage have attracted increasing concerns. Existing graph …

Data augmentation has recently seen increased interest in graph machine learning given its
demonstrated ability to improve model performance and generalization by added training …

Graph condensation, which reduces the size of a large-scale graph by synthesizing a small-
scale condensed graph as its substitution, has immediate benefits for various graph learning …

The popularity of deep learning has led to the curation of a vast number of massive and
multifarious datasets. Despite having close-to-human performance on individual tasks …

Dataset Distillation (DD), a newly emerging field, aims at generating much smaller but
efficient synthetic training datasets from large ones. Existing DD methods based on gradient …

The great success of deep learning heavily relies on increasingly larger training data, which
comes at a price of huge computational and infrastructural costs. This poses crucial …

The huge volume of emerging graph datasets has become a double-bladed sword for graph
machine learning. On the one hand, it empowers the success of a myriad of graph neural …