A Survey of Graph Neural Networks in Real world: Imbalance, Noise, Privacy and OOD Challenges

arXiv:2403.04468v1 Announce Type: new
Abstract: Graph-structured data exhibits universality and widespread applicability across diverse domains, such as social network analysis, biochemistry, financial fraud detection, and network security. Significant strides have been made in leveraging Graph Neural Networks (GNNs) to achieve remarkable success in these areas. However, in real-world scenarios, the training environment for models is often far from ideal, leading to substantial performance degradation of GNN models due to various unfavorable factors, including imbalance in data distribution, the presence of …

abstract analysis arxiv biochemistry challenges cs.ai cs.ir cs.lg cs.si data detection diverse domains financial financial fraud fraud fraud detection gnns graph graph neural networks network networks network security neural networks noise privacy security social structured data success survey type world