首页|Stability and Generalization of Hypergraph Collaborative Networks

Stability and Generalization of Hypergraph Collaborative Networks

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Graph neural networks have been shown to be very effective in utilizing pairwise relationships across samples.Recently,there have been several successful proposals to generalize graph neural networks to hypergraph neural networks to exploit more com-plex relationships.In particular,the hypergraph collaborative networks yield superior results compared to other hypergraph neural net-works for various semi-supervised learning tasks.The collaborative network can provide high quality vertex embeddings and hyperedge embeddings together by formulating them as a joint optimization problem and by using their consistency in reconstructing the given hy-pergraph.In this paper,we aim to establish the algorithmic stability of the core layer of the collaborative network and provide generaliz-ation guarantees.The analysis sheds light on the design of hypergraph filters in collaborative networks,for instance,how the data and hypergraph filters should be scaled to achieve uniform stability of the learning process.Some experimental results on real-world datasets are presented to illustrate the theory.

Hypergraphsverticeshyperedgescollaborative networksgraph convolutional neural networks(CNNs)stabilitygeneralization guarantees

Michael K.Ng、Hanrui Wu、Andy Yip

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Institute of Data Science,The University of Hong Kong,Hong Kong,China

Department of Mathematics,The University of Hong Kong,Hong Kong,China

College of Information Science and Technology,Jinan University,Guangzhou 510006,China

Hong Kong Research Grant Council General Research Fund(GRF)ChinaHong Kong Research Grant Council General Research Fund(GRF)ChinaHong Kong Research Grant Council General Research Fund(GRF)ChinaHong Kong Research Grant Council General Research Fund(GRF)ChinaHong Kong Research Grant Council General Research Fund(GRF)ChinaHong Kong Research Grant Council General Research Fund(GRF)ChinaHong Kong Research Grant Council General Research Fund(GRF)ChinaNational Natural science Foundation of ChinaYoung Talent Support Project of Guangzhou Association for Science and Technology,ChinaGuangzhou Basic and Applied Basic Research Foundation,ChinaFundamental Research Funds for the Central Universities,ChinaChina Postdoctoral Science Foundation

12300218123005191720102017300021CRF C1013-21GFC7004-21GFJoint NSFC-RGC N-HKU7692162206111QT-2023-0172023A04J1058216223262022M721343

2024

10.1007/s11633-022-1397-1

机器智能研究(英文)
中国科学院自动化所

机器智能研究(英文)

CSTPCDEI
影响因子：0.49
ISSN：2731-538X
年,卷(期)：2024.21(1)
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