Name
Abstract | ||
|---|---|---|
Node classification has a wide range of application scenarios such as citation analysis and social network analysis. In many real-world attributed networks, a large portion of classes only contain limited labeled nodes. Most of the existing node classification methods cannot be used for few-shot node classification. To train the model effectively and improve the robustness and reliability of the model with scarce labeled samples, in this paper, we propose a local adaptive discriminant structure learning (LADSL) method for few-shot node classification. LADSL aims to properly represent the nodes in the attributed graphs and learn a metric space with a strong discriminating power by reducing the intra-class variations and enlarging inter-class differences. Extensive experiments conducted on various attributed networks datasets demonstrate that LADSL is superior to the other methods on few-shot node classification task. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1007/s11704-022-1259-6 | Frontiers of Computer Science |
Keywords | DocType | Volume |
few-shot learning, node classification, graph neural network, adaptive structure learning, attention strategy | Journal | 17 |
Issue | ISSN | Citations |
2 | 2095-2228 | 0 |
PageRank | References | Authors |
0.34 | 7 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhe Xue | 1 | 72 | 14.60 |
| Junping Du | 2 | 789 | 91.80 |
| Xin Xu | 3 | 0 | 1.01 |
| Xiangbin LIU | 4 | 0 | 0.34 |
| Junfu WANG | 5 | 0 | 0.34 |
| Fei-Fei Kou | 6 | 1 | 3.72 |