Deep reinforcement learning (DRL) has empowered a variety of artificial intelligence fields,
including pattern recognition, robotics, recommendation systems, and gaming. Similarly …

Deep neural networks (DNNs) have been widely applied to various applications, including
image classification, text generation, audio recognition, and graph data analysis. However …

Graph is an important data representation ubiquitously existing in the real world. However,
analyzing the graph data is computationally difficult due to its non-Euclidean nature. Graph …

Many real-world data come in the form of graphs. Graph neural networks (GNNs), a new
family of machine learning (ML) models, have been proposed to fully leverage graph data to …

Abstract Graph Convolutional Networks (GCNs) are promising deep learning approaches in
learning representations for graph-structured data. Despite the proliferation of such …

Social media is one of the main sources for news consumption, especially among the
younger generation. With the increasing popularity of news consumption on various social …

Many data mining tasks rely on graphs to model relational structures among individuals
(nodes). Since relational data are often sensitive, there is an urgent need to evaluate the …

Graph neural networks (GNNs) have emerged as a powerful tool for graph classification and
representation learning. However, GNNs tend to suffer from over-smoothing problems and …

Offline reinforcement learning (RL) is a challenging task, whose objective is to learn policies
from static trajectory data without interacting with the environment. Recently, offline RL has …

Deep Graph Learning (DGL) has emerged as a crucial technique across various domains.
However, recent studies have exposed vulnerabilities in DGL models, such as susceptibility …