  The data-hungry problem, characterized by insufficiency and low-quality of
data, poses obstacles for deep learning models. Transfer learning has been a
feasible way to transfer knowledge from high-quality external data of source
domains to limited data of target domains, which follows a domain-level
knowledge transfer to learn a shared posterior distribution. However, they are
usually built on strong assumptions, e.g., the domain invariant posterior
distribution, which is usually unsatisfied and may introduce noises, resulting
in poor generalization ability on target domains. Inspired by Graph Neural
Networks (GNNs) that aggregate information from neighboring nodes, we redefine
the paradigm as learning a knowledge-enhanced posterior distribution for target
domains, namely Knowledge Bridge Learning (KBL). KBL first learns the scope of
knowledge transfer by constructing a Bridged-Graph that connects knowledgeable
samples to each target sample and then performs sample-wise knowledge transfer
via GNNs.KBL is free from strong assumptions and is robust to noises in the
source data. Guided by KBL, we propose the Bridged-GNN} including an Adaptive
Knowledge Retrieval module to build Bridged-Graph and a Graph Knowledge
Transfer module. Comprehensive experiments on both un-relational and relational
data-hungry scenarios demonstrate the significant improvements of Bridged-GNN
compared with SOTA methods
