Name
Abstract | ||
|---|---|---|
The field of graph neural networks (GNNs) has seen rapid and incredible strides over the recent years. Graph neural networks, also known as deep learning on graphs, graph representation learning, or geometric deep learning, have become one of the fastest-growing research topics in machine learning, especially deep learning. This wave of research at the intersection of graph theory and deep learning has also influenced other fields of science, including recommendation systems, computer vision, natural language processing, inductive logic programming, program synthesis, software mining, automated planning, cybersecurity, and intelligent transportation. However, as the field rapidly grows, it has been extremely challenging to gain a global perspective of the developments of GNNs. Therefore, we feel the urgency to bridge the above gap and have a comprehensive tutorial on this fast-growing yet challenging topic. This tutorial of Graph Neural Networks (GNNs): Foundation, Frontiers and Applications will cover a broad range of topics in graph neural networks, by reviewing and introducing the fundamental concepts and algorithms of GNNs, new research frontiers of GNNs, and broad and emerging applications with GNNs. In addition, rich tutorial materials will be included and introduced to help the audience gain a systematic understanding by using our recently published book-Graph Neural Networks (GNN): Foundation, Frontiers, and Applications [12], which can easily be accessed at https://graph-neural-networks.github.io/index.html. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1145/3534678.3542609 | KDD '22: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lingfei Wu | 1 | 116 | 32.05 |
| Peng Cui | 2 | 2317 | 110.00 |
| Jian Pei | 3 | 0 | 0.34 |
| Liang Zhao | 4 | 0 | 0.34 |
| Xiaojie Guo | 5 | 4 | 5.79 |