Name
Abstract | ||
|---|---|---|
Graph neural networks have emerged as a popular and powerful tool for learning hierarchical representation of graph data. In complement to graph convolution operators, graph pooling is crucial for extracting hierarchical representation of data in graph neural networks. However, most recent graph pooling methods still fail to efficiently exploit the geometry of graph data. In this paper, we propose... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/TSIPN.2020.3044913 | IEEE Transactions on Signal and Information Processing over Networks |
Keywords | DocType | Volume |
Convolution,Topology,Kernel,Network topology,Laplace equations,Information processing,Task analysis | Journal | 7 |
ISSN | Citations | PageRank |
2373-776X | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xing Gao | 1 | 0 | 1.01 |
| Wenrui Dai | 2 | 64 | 25.01 |
| Chenglin Li | 3 | 116 | 17.93 |
| Hongkai Xiong | 4 | 512 | 82.84 |
| Pascal Frossard | 5 | 3015 | 230.41 |