Deep learning models for graphs have achieved strong performance for the task of node classification. Despite their proliferation, currently there is no study of their robustness to …
Deep learning models for graphs have achieved strong performance for the task of node classification. Despite their proliferation, little is known about their robustness to adversarial …
Graph Neural Networks (GNNs) have demonstrated their powerful capability in learning representations for graph-structured data. Consequently, they have enhanced the …
K Li, Y Liu, X Ao, Q He - The Eleventh International Conference on …, 2023 - openreview.net
Recent studies have shown that structural perturbations are significantly effective in degrading the accuracy of Graph Neural Networks (GNNs) in the semi-supervised node …
S Yang, BG Doan, P Montague, O De Vel… - Proceedings of the 25th …, 2022 - dl.acm.org
Graph Neural Networks (GNNs) have achieved tremendous success in many graph mining tasks benefitting from the message passing strategy that fuses the local structure and node …
H Dai, H Li, T Tian, X Huang, L Wang… - … on machine learning, 2018 - proceedings.mlr.press
Deep learning on graph structures has shown exciting results in various applications. However, few attentions have been paid to the robustness of such models, in contrast to …
Deep neural networks (DNNs) have been widely applied to various applications, including image classification, text generation, audio recognition, and graph data analysis. However …
Graph deep learning models, such as graph convolutional networks (GCN) achieve remarkable performance for tasks on graph data. Similar to other types of deep models …
D Zhu, Z Zhang, P Cui, W Zhu - Proceedings of the 25th ACM SIGKDD …, 2019 - dl.acm.org
Graph Convolutional Networks (GCNs) are an emerging type of neural network model on graphs which have achieved state-of-the-art performance in the task of node classification …