WebApr 14, 2024 · Inspired by InfoMin principle proposed by , AD-GCL optimizes adversarial graph augmentation strategies to train GNNs to avoid capturing redundant information during the training. However, AD-GCL is designed to work on unsupervised graph classification with lots of small graphs, under the pre-training & fine-tuning scheme. WebJun 24, 2024 · Robust Optimization as Data Augmentation for Large-scale Graphs Abstract: Data augmentation helps neural networks generalize better by enlarging the training set, but it remains an open question how to effectively augment graph data to enhance the performance of GNNs (Graph Neural Networks).

Adversarial Graph Augmentation to Improve Graph Contrastive …

WebGraph augmentation is an essential strategy to improve the performance of graph-based tasks, and has been widely utilized for analyzing web and social graphs. However, previous works for graph augmentation either a) involve the target model in the process of augmentation, losing the generalizability to other tasks, or b) rely on simple ... WebAug 15, 2024 · In this work, by introducing an adversarial graph view for data augmentation, we propose a simple but effective method, Adversarial Graph … man spot divorce

Model-Agnostic Augmentation for Accurate Graph Classification

WebHere, we propose a novel principle, termed adversarial-GCL (\textit {AD-GCL}), which enables GNNs to avoid capturing redundant information during the training by … WebInstance Relation Graph Guided Source-Free Domain Adaptive Object Detection Vibashan Vishnukumar Sharmini · Poojan Oza · Vishal Patel ... Edges to Shapes to Concepts: … Webas adversarial attacks. The results show that, even without tuning augmentation extents nor using sophisticated GNN architectures, our GraphCL framework can produce … man stabbed in columbia sc