A cursory reading of the literature suggests that we have made a lot of progress in designing
effective adversarial defenses for Graph Neural Networks (GNNs). Yet, the standard …

Data augmentation has recently seen increased interest in graph machine learning given its
demonstrated ability to improve model performance and generalization by added training …

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

In recent years, prompt tuning has sparked a research surge in adapting pre-trained models.
Unlike the unified pre-training strategy employed in the language field, the graph field …

Abstract Graph Structure Learning (GSL) has recently garnered considerable attention due
to its ability to optimize both the parameters of Graph Neural Networks (GNNs) and the …

Graphs are widely used to describe real-world objects and their interactions. Graph Neural
Networks (GNNs) as a de facto model for analyzing graphstructured data, are highly …

The sequential recommendation system has been widely studied for its promising
effectiveness in capturing dynamic preferences buried in users' sequential behaviors …

In recent years, prompt tuning has set off a research boom in the adaptation of pre-trained
models. In this paper, we propose Graph Prompt as an efficient and effective alternative to …

Recently, the dual-target cross-domain recommendation has been an emerging research
problem, which aims to improve the performances of both source and target domains by …

Node classification using Graph Neural Networks (GNNs) has been widely applied in
various real-world scenarios. However, in recent years, compelling evidence emerges that …