Abstract Pretrained Foundation Models (PFMs) are regarded as the foundation for various
downstream tasks across different data modalities. A PFM (eg, BERT, ChatGPT, GPT-4) is …

In the last decade, deep learning has reinvigorated the machine learning field. It has solved
many problems in computer vision, speech recognition, natural language processing, and …

Deep learning on graphs has attracted significant interests recently. However, most of the
works have focused on (semi-) supervised learning, resulting in shortcomings including …

Despite the promising representation learning of graph neural networks (GNNs), the
supervised training of GNNs notoriously requires large amounts of labeled data from each …

Graph neural networks (GNNs) have emerged as a series of competent graph learning
methods for diverse real-world scenarios, ranging from daily applications like …

Molecular representation learning contributes to multiple downstream tasks such as
molecular property prediction and drug design. To properly represent molecules, graph …

Contrastive learning has been successfully applied in unsupervised representation learning.
However, the generalization ability of representation learning is limited by the fact that the …

Artificial General Intelligence (AGI) has revolutionized numerous fields, yet its integration
with graph data, a cornerstone in our interconnected world, remains nascent. This paper …

Graphs are typical non-Euclidean data of complex structures. Recently, Riemannian graph
representation learning emerges as an exciting alternative to the traditional Euclidean ones …

With the prosperity of contrastive learning for visual representation learning (VCL), it is also
adapted to the graph domain and yields promising performance. However, through a …