.png){kind=icon}
返回
MuL-GRN: Multi-Level Graph Relation Network for Few-Shot Node Classification
.png){kind=icon}
PRE
AI
DOI：10.1109/TKDE.2022.3176880.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Few-shot learning (FSL) that acquires new knowledge with little supervision, attracts much attention due to expensive cost of data annotation. Various meta-learning methods have made a great progress for few-shot problem in image and text data. In reality, data samples are not independent but rich in link relations. Large amounts of data exists in the form of graph structure such as citation, social, and biological networks. However, FSL study on graph data is still in its infancy because of the obstacle on extracting meta-knowledge from a meta node classification task. Current research simply combines the FSL methods experienced in computer vision with node representation models together, ignoring the effect of rich links among support and query nodes in few-shot meta-task. For this issue, we propose a novel Multi-Level Graph Relation Network (MuL-GRN) for the challenging few-shot node classification. MuL-GRN extracts node embeddings through the popular graph neural networks (GNNs). And it includes a relation learning module to mine the deep node relations from three views, namely node-level, global subgraph-level, and local subgraph-level relations. For any two nodes, the node-level relation is computed on their node embeddings, global subgraph-level relation is measured on their subgraph embeddings, and the local subgraph-level relation is mined according to the pairwise node comparison information in their subgraphs. The three-view relation vectors are fused together with an interesting relation fusion module, which measures the importance of relation vector for the current few-shot classification task automatically. Extensive experiments on five real datasets show that MuL-GRN significantly outperforms existing state-of-the-art methods by a large margin.
Keyword:
Computer vision
Costs
Network topology
Fuses
Current measurement
Feature extraction
Graph neural networks
Node classification
few-shot learning
three-view relations
subgraph relation
graph neural networks
期刊
IF:
10.4
论文数:
6.8K
被引数:
3.2W
机构
引用论文
分享收藏Scheduled sampling for one-shot learning via matching network通过匹配网络进行一次性学习的计划采样.png)
PATTERN RECOGNITION
PATTERN RECOGNITIONIF7.6
PREAI
分享收藏
查看更多内容