Graph Stochastic Neural Networks for semi-supervised learning

Haibo Wang, Chuan Zhou, Xin Chen, Jia Wu, Shirui Pan, Jilong Wang

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Graph Neural Networks (GNNs) have achieved remarkable performance in the task of the semi-supervised node classification. However, most existing models learn a deterministic classification function, which lack sufficient flexibility to explore better choices in the presence of kinds of imperfect observed data such as the scarce labeled nodes and noisy graph structure. To improve the rigidness and inflexibility of deterministic classification functions, this paper proposes a novel framework named Graph Stochastic Neural Networks (GSNN), which aims to model the uncertainty of the classification function by simultaneously learning a family of functions, i.e., a stochastic function. Specifically, we introduce a learnable graph neural network coupled with a high-dimensional latent variable to model the distribution of the classification function, and further adopt the amortised variational inference to approximate the intractable joint posterior for missing labels and the latent variable. By maximizing the lower-bound of the likelihood for observed node labels, the instantiated models can be trained in an end-to-end manner effectively. Extensive experiments on three real-world datasets show that GSNN achieves substantial performance gain in different scenarios compared with state-of-the-art baselines.

Original languageEnglish
Title of host publicationAdvances in Neural Information Processing Systems 33 (NeurIPS 2020)
EditorsH. Larochelle, M. Ranzato, R. Hadsell , M.F. Balcan, H. Lin
Place of PublicationSan Diego CA USA
PublisherNeural Information Processing Systems (NIPS)
Number of pages10
Publication statusPublished - 2020
EventAdvances of Neural Information Processing Systems 2020 - Online, Virtual, Online, United States of America
Duration: 6 Dec 202012 Dec 2020
Conference number: 34th (Proceedings ) (Website)

Publication series

NameAdvances in Neural Information Processing Systems
PublisherMorgan Kaufmann Publishers
ISSN (Print)1049-5258


ConferenceAdvances of Neural Information Processing Systems 2020
Abbreviated titleNeurIPS 2020
Country/TerritoryUnited States of America
CityVirtual, Online
Internet address

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