Graph Neural Networks (GNNs) have received significant attention for demonstrating their
capability to handle graph data. However, they are difficult to be deployed in resource …

Graph neural network (GNN) is a powerful learning approach for graph-based recommender
systems. Recently, GNNs integrated with contrastive learning have shown superior …

The emergence of large-scale pre-trained language models has revolutionized various AI
research domains. Transformers-based Large Language Models (LLMs) have gradually …

Graph data have become increasingly important, and graph node clustering has emerged
as a fundamental task in data analysis. In recent years, graph node clustering has gradually …

Benefiting from the intrinsic supervision information exploitation capability, contrastive
learning has achieved promising performance in the field of deep graph clustering recently …

Generative self-supervised learning (SSL), especially masked autoencoders, has become
one of the most exciting learning paradigms and has shown great potential in handling …

Graph clustering, which aims to divide nodes in the graph into several distinct clusters, is a
fundamental yet challenging task. Benefiting from the powerful representation capability of …

Contrastive graph node clustering via learnable data augmentation is a hot research spot in
the field of unsupervised graph learning. The existing methods learn the sampling …

Continual graph learning routinely finds its role in a variety of real-world applications where
the graph data with different tasks come sequentially. Despite the success of prior works, it …

Graph structure learning (GSL), which aims to learn the adjacency matrix for graph neural
networks (GNNs), has shown great potential in boosting the performance of GNNs. Most …