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A Generative Bayesian Graph Attention Network for Semi-supervised Classification on Scarce Data

Sun, Zhongtian; Harit, Anoushka; Yu, Jialin; Cristea, Alexandra; Al Moubayed, Noura

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Authors

Zhongtian Sun zhongtian.sun@durham.ac.uk
PGR Student Doctor of Philosophy

Jialin Yu jialin.yu@durham.ac.uk
Academic Visitor



Abstract

This research focuses on semi-supervised classification tasks, specifically for graph-structured data under datascarce situations. It is known that the performance of conventional supervised graph convolutional models is mediocre at classification tasks, when only a small fraction of the labeled nodes are given. Additionally, most existing graph neural network models often ignore the noise in graph generation and consider all the relations between objects as genuine ground-truth. Hence, the missing edges may not be considered, while other spurious edges are included. Addressing those challenges, we propose a Bayesian Graph Attention model which utilizes a generative model to randomly generate the observed graph. The method infers the joint posterior distribution of node labels and graph structure, by combining the Mixed-Membership Stochastic Block Model with the Graph Attention Model. We adopt a variety of approximation methods to estimate the Bayesian posterior distribution of the missing labels. The proposed method is comprehensively evaluated on three graph-based deep learning benchmark data sets. The experimental results demonstrate a competitive performance of our proposed model BGAT against the current state of the art models when there are few labels available (the highest improvement is 5%), for semi-supervised node classification tasks.

Citation

Sun, Z., Harit, A., Yu, J., Cristea, A., & Al Moubayed, N. (2021). A Generative Bayesian Graph Attention Network for Semi-supervised Classification on Scarce Data. . https://doi.org/10.1109/ijcnn52387.2021.9533981

Conference Name IEEE International Joint Conference on Neural Network (IJCNN2021)
Conference Location Virtual
Start Date Jul 18, 2021
End Date Jul 22, 2021
Acceptance Date Jul 1, 2021
Online Publication Date Sep 20, 2021
Publication Date 2021
Deposit Date Jul 20, 2021
Publicly Available Date Mar 29, 2024
Series ISSN 2161-4393,2161-4407
DOI https://doi.org/10.1109/ijcnn52387.2021.9533981

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Accepted Conference Proceeding (589 Kb)
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