WebDeep learning has been shown successful in a number of domains, ranging from acoustics, images to natural language processing. However, applying deep learning to the ubiquitous graph data is non-trivial because of the unique characteristics of graphs. Recently, a significant amount of research efforts have been devoted to this area, greatly advancing … WebDeep Learning on Graphs: A Survey Ziwei Zhang, Peng Cui and Wenwu Zhu, Fellow, IEEE Abstract—Deep learning has been shown to be successful in a number of …

[2202.08235] Data Augmentation for Deep Graph …

WebOct 12, 2024 · In our survey, we focused on analyzing the background text graph transformation concepts and different deep learning-based architectures which are used in each model. We also provide details of the existing challenges, perspectives and further possible enhancements for the TG-GNN area which might be useful for other … WebAs an essential part of artificial intelligence, a knowledge graph describes the real-world entities, concepts and their various semantic relationships in a structured way and has … atar rankings 2022

Class-Imbalanced Learning on Graphs: A Survey - Semantic Scholar

WebDec 11, 2024 · Deep Learning on Graphs: A Survey. Deep learning has been shown successful in a number of domains, ranging from acoustics, images to natural language … WebDec 8, 2024 · In this survey, we formally formulate the problem of graph data augmentation and further review the representative techniques and their applications in different deep … WebDeep Learning on Graphs: A Survey. Deep learning has been shown to be successful in a number of domains, ranging from acoustics, images, to natural language processing. However, applying deep learning to the ubiquitous graph data is non-trivial because of the unique characteristics of graphs. Recently, substantial research efforts have been ... atar rc