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

Textual graphs (TGs) are graphs whose nodes correspond to text (sentences or documents),
which are widely prevalent. The representation learning of TGs involves two stages:(i) …

Graph representation learning on vast datasets, like web data, has made significant strides.
However, the associated computational and storage overheads raise concerns. In sight of …

As large-scale graphs become more widespread, more and more computational challenges
with extracting, processing, and interpreting large graph data are being exposed. It is …

Graph Neural Networks (GNNs) have been increasingly deployed in a plethora of
applications. However, the graph data used for training may contain sensitive personal …

Training graph neural networks (GNNs) is extremely time consuming because sparse graph-
based operations are hard to be accelerated by community hardware. Prior art successfully …

Sampling subgraphs for training Graph Neural Networks (GNNs) is receiving much attention
from the GNN community. While a variety of methods have been proposed, each method …

Despite the enormous success of Graph Convolutional Networks (GCNs) in modeling graph-
structured data, most of the current GCNs are shallow due to the notoriously challenging …

In an era of information explosion, recommender systems are vital tools to deliver
personalized recommendations for users. The key of recommender systems is to forecast …

Foundation models (FMs) currently dominate news headlines. They employ advanced deep
learning architectures to extract structural information autonomously from vast datasets …