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

Sun, Zhongtian and Harit, Anoushka and Yu, Jialin and Cristea, Alexandra and Al Moubayed, Noura (2021) 'A Generative Bayesian Graph Attention Network for Semi-supervised Classification on Scarce Data.', IEEE International Joint Conference on Neural Network (IJCNN2021) Virtual, 18-22 Jul 2021.


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.

Item Type:Conference item (Paper)
Full text:(AM) Accepted Manuscript
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Publisher statement:© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Date accepted:01 July 2021
Date deposited:20 July 2021
Date of first online publication:20 September 2021
Date first made open access:23 July 2021

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