DummyMAE: automatic encoder based on edge-to-vertex transformation

Ruiting Wang; Enguang Zuo; Jie Zhong; Junyi Yan; Ziwei Yan; Tianle Li; Xiaoyi Lv

doi:10.1117/12.3005986

10 October 2023 DummyMAE: automatic encoder based on edge-to-vertex transformation

Ruiting Wang, Enguang Zuo, Jie Zhong, Junyi Yan, Ziwei Yan, Tianle Li, Xiaoyi Lv

Proceedings Volume 12799, Third International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023); 127994A (2023) https://doi.org/10.1117/12.3005986
Event: 3rd International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023), 2023, Kuala Lumpur, Malaysia

Abstract

Autoencoders, as a type of generative self-supervised learning, have received increasing attention in information processing and data mining in recent years. However, existing autoencoders usually generate graph data conforming to the feature distribution from only one aspect of reconstructing edge or node features, which allows the models to extract only a single level of information, limiting their application in real-world applications. In this paper, we propose DummyMAE, a generative self-supervised learning framework that synchronously generates edge and node features. In general, it losslessly converts vertex graphs into corresponding line graphs by introducing edge-to-vertex transformations. The vertex graph provides the model with information on node features, and the line graph provides the model with the ability to capture information on the graph structure, which complements each other. The task of simultaneously reconstructing edges and features is achieved in this way. The task of graph classification serves as a pivotal component within the realm of graph learning, we have conducted sufficient experiments on four widely used graph classification datasets, and the results show that DummyMAE outperforms the current state-of-the-art baselines for the graph classification task.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Ruiting Wang, Enguang Zuo, Jie Zhong, Junyi Yan, Ziwei Yan, Tianle Li, and Xiaoyi Lv "DummyMAE: automatic encoder based on edge-to-vertex transformation", Proc. SPIE 12799, Third International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023), 127994A (10 October 2023); https://doi.org/10.1117/12.3005986

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KEYWORDS

Data modeling

Performance modeling

Matrices

Design and modelling

Feature extraction

Data mining

Data processing

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