Financial fraud is a persistent problem in the finance industry that may have severe
consequences for individuals, businesses, and economies. Graph Neural Networks (GNNs) …

Abstract Many studies on Graph Data Augmentation (GDA) approaches have emerged. The
techniques have rapidly improved performance for various graph neural network (GNN) …

Emerging as fundamental building blocks for diverse artificial intelligence applications,
foundation models have achieved notable success across natural language processing and …

Large language models (LLMs), while exhibiting exceptional performance, suffer from
hallucinations, especially on knowledge-intensive tasks. Existing works propose to augment …

Deep multi-view representation learning focuses on training a unified low-dimensional
representation for data with multiple sources or modalities. With the rapidly growing attention …

Graph neural networks (GNNs) have been widely used for graph structure learning and
achieved excellent performance in tasks such as node classification and link prediction …

The development of FPGA-based applications using HLS is fraught with performance pitfalls
and large design space exploration times. These issues are exacerbated when the …

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 …

This work presents a graph neural network (GNN) framework for solving the maximum
independent set (MIS) problem, inspired by dynamic programming (DP). Specifically, given …

Graph regression is a fundamental task and has received increasing attention in a wide
range of graph learning tasks. However, the inference process is often not interpretable …