Name
Title | ||
|---|---|---|
The Sixth International Workshop on Deep Learning on Graphs - Methods and Applications (DLG-KDD'21) |
Abstract | ||
|---|---|---|
ABSTRACTDeep 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'21)" aims to bring together both academic researchers and industrial practitioners from different backgrounds and perspectives to the above challenges. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1145/3447548.3469470 | Knowledge Discovery and Data Mining |
Keywords | DocType | Citations |
Graph neural network, deep learning, graph representation learning, graph mining | Conference | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lingfei Wu | 1 | 116 | 32.05 |
| Jiliang Tang | 2 | 3323 | 140.81 |
| Xia, Yinglong | 3 | 217 | 26.91 |
| Jian Pei | 4 | 19002 | 995.54 |
| Xiaojie Guo | 5 | 0 | 0.34 |