Graph Few-shot Learning via Knowledge Transfer

By Huaxiu Yao, Chuxu Zhang, Ying Wei, Meng Jiang, Suhang Wang, Junzhou Huang, Nitesh V. Chawla, Zhenhui Jessie Li. 2019

194 citations recorded by the source corpus.

Towards the challenging problem of semi-supervised node classification, there have been extensive studies. As a frontier, Graph Neural Networks (GNNs) have aroused great interest recently, which update the representation of each node by aggregating information of its neighbors. However, most GNNs have shallow layers with a limited receptive field and may not achieve satisfactory performance especially when the number of labeled nodes is quite small. To address this challenge, we innovatively propose a graph few-shot learning (GFL) algorithm that incorporates prior knowledge learned from auxiliary graphs to improve classification accuracy on the target graph. Specifically, a transferable metric space characterized by a node embedding and a graph-specific prototype embedding function is shared between auxiliary graphs and the target, facilitating the transfer of structural knowledge. Extensive experiments and ablation studies on four real-world graph datasets demonstrate the effectiveness of our proposed model and the contribution of each component.

Subfields: Artificial Intelligence, Neurology

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Citations in the corpus

Knowledge Distillation: A Survey (2020, background): “Most of knowledge distillation algorithms focus on transferring individual instance knowledge from the teacher to the student, while some recent methods have been proposed to explore the intra-data relationships using graphs (Chen et al., 2020b; Zhang and Peng, 2018; Lee and Song, 2019; Park et al.,”
CGLB: Benchmark Tasks for Continual Graph Learning (2022, background): “Finally, the essential difference between CGL, dynamic graph learning [14, 48, 18, 60, 35, 66, 33, 13, 5], and few-shot graph learning [65, 16, 57, 43] is worth noting.”
A Comprehensive Survey of Few-shot Learning: Evolution, Applications, Challenges, and Opportunities (2022, background): “The prototype network was improved by GFL [141] network that focuses on learning small samples of data with a graph structure.”
Knowledge Distillation and Student-Teacher Learning for Visual Intelligence: A Review and New Outlooks (2020, methodology): “GFL [250] and HGKD [221] aim to improve semi-supervised node classification via graph-based knowledge transfer, whereas GRL [150] exploits graph-based knowledge for multi-task learning.”