Jointly Attacking Graph Neural Network and its Explanations
Wenqi Fan, Han Xu, et al.
ICDE 2023
Deep Learning models are at the core of research in Artificial Intelligence research today. A tide in research for deep learning on graphs or graph neural networks. This wave of research at the intersection of graph theory and deep learning has also influenced other fields of science, including computer vision, natural language processing, program synthesis and analysis, financial security, Drug Discovery and so on. However, there are still many challenges regarding a broad range of the topics in deep learning on graphs, from methodologies to applications, and from foundations to the new frontiers of GNNs. This international workshop on "Deep Learning on Graphs: Method and Applications (DLG-KDD'23)"aims to bring together both academic researchers and industrial practitioners from different backgrounds and perspectives to above challenges.
Wenqi Fan, Han Xu, et al.
ICDE 2023
Sakib Haque, Aakash Bansal, et al.
SANER 2021
Zhen Zhang, Yijian Xiang, et al.
NeurIPS 2019
Kai Shen, Lingfei Wu, et al.
IJCAI 2020