Graph representation is a critical technology in the field of knowledge engineering and
knowledge-based applications since most knowledge bases are represented in the graph …

Masked graph autoencoder (MGAE) has emerged as a promising self-supervised graph pre-
training (SGP) paradigm due to its simplicity and effectiveness. However, existing efforts …

Self-supervised graph representation Learning (SSGRL) is an emerging technique for
machine learning-based expert applications. SSGRL can effectively encode unlabeled data …

Self-supervised learning on graphs can be bifurcated into contrastive and generative
methods. Contrastive methods, also known as graph contrastive learning (GCL), have …

Abstract Knowledge Graph (KG) stores human knowledge facts in an intuitive graphical
structure but faces challenges such as incomplete construction or inability to handle new …

Graph Neural Networks (GNNs) are powerful in learning semantics of graph data. Recently,
a new paradigm" pre-train, prompt" has shown promising results in adapting GNNs to …

In recent years," pre-training and fine-tuning" has emerged as a promising approach in
addressing the issues of label dependency and poor generalization performance in …

Masked graph autoencoders have emerged as a powerful graph self-supervised learning
method that has yet to be fully explored. In this paper, we unveil that the existing discrete …

For graph self-supervised learning (GSSL), masked autoencoder (MAE) follows the
generative paradigm and learns to reconstruct masked graph edges or node features while …

Abstract Graph Neural Networks (GNNs) are powerful in learning semantics of graph data.
Recently, a new paradigm “pre-train & prompt” has shown promising results in adapting …