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Non-exhaustive Learning Using Gaussian Mixture Generative Adversarial Networks Read chapter Read first chapter

2021 | OriginalPaper | Chapter

Semantic-Specific Hierarchical Alignment Network for Heterogeneous Graph Adaptation

Authors : YuanXin Zhuang, Chuan Shi, Cheng Yang, Fuzhen Zhuang, Yangqiu Song

Published in: Machine Learning and Knowledge Discovery in Databases. Research Track

Publisher: Springer International Publishing

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Abstract

Node classification has been substantially improved with the advent of Heterogeneous Graph Neural Networks (HGNNs). However, collecting numerous labeled data is expensive and time-consuming in many applications. Domain Adaptation (DA) tackles this problem by transferring knowledge from a label-rich domain to a label-scarce one. However the heterogeneity and rich semantic information bring great challenges for adapting HGNN for DA. In this paper, we propose a novel semantic-specific hierarchical alignment network for heterogeneous graph adaptation, called HGA. HGA designs a sharing-parameters HGNN aggregating path-based neighbors and hierarchical domain alignment strategies with the MMD and \(L_1\) normalization term. Extensive experiments on four datasets demonstrate that the proposed model can achieve remarkable results on node classification.

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previous chapter Multi-view Self-supervised Heterogeneous Graph Embedding
next chapter The KL-Divergence Between a Graph Model and its Fair I-Projection as a Fairness Regularizer
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Metadata
Title
Semantic-Specific Hierarchical Alignment Network for Heterogeneous Graph Adaptation
Authors
YuanXin Zhuang
Chuan Shi
Cheng Yang
Fuzhen Zhuang
Yangqiu Song
Copyright Year
2021
Book
Machine Learning and Knowledge Discovery in Databases. Research Track

Print ISBN: 978-3-030-86519-1

Electronic ISBN: 978-3-030-86520-7

Copyright Year: 2021

DOI
https://doi.org/10.1007/978-3-030-86520-7_21

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