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 …

Learning on Graphs has attracted immense attention due to its wide real-world applications.
The most popular pipeline for learning on graphs with textual node attributes primarily relies …

As fraudulent activities have shot up manifolds, fraud detection has emerged as a pivotal
process in different fields (eg, e-commerce, online reviews, and social networks). Since …

Learning on Graphs has attracted immense attention due to its wide real-world applications.
The most popular pipeline for learning on graphs with textual node attributes primarily relies …

Recent advancements in text-attributed graphs (TAGs) have significantly improved the
quality of node features by using the textual modeling capabilities of language models …

Training a deep-learning text classification model usually requires a large amount of labeled
data, yet labeling data are usually labor-intensive and time-consuming. Few-shot text …

As malicious bots reside in a network to disrupt network stability, graph neural networks
(GNNs) have emerged as one of the most popular bot detection methods. However, in most …

Machine translation is integral to international communication and extensively employed in
diverse human-related applications. Despite remarkable progress, fairness issues persist …

Social media has drastically reshaped the world that allows billions of people to engage in
such interactive environments to conveniently create and share content with the public …

Social media generates a rich source of text data with intrinsic user attributes (eg, age,
gender), where different parties benefit from disclosing them. Attribute inference can be cast …