Neighbor-anchoring adversarial graph neural networks
Graph neural networks (GNNs) have witnessed widespread adoption due to their ability to learn superior representations for graph data. While GNNs exhibit strong discriminative power, they often fall short of learning the underlying node distribution for increased robustness. To deal with this, inspi...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/172860 |
| _version_ | 1811684049358422016 |
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| author | Liu, Zemin Fang, Yuan Liu, Yong Zheng, Vincent Wenchen |
| author2 | School of Computer Science and Engineering |
| author_facet | School of Computer Science and Engineering Liu, Zemin Fang, Yuan Liu, Yong Zheng, Vincent Wenchen |
| author_sort | Liu, Zemin |
| collection | NTU |
| description | Graph neural networks (GNNs) have witnessed widespread adoption due to their ability to learn superior representations for graph data. While GNNs exhibit strong discriminative power, they often fall short of learning the underlying node distribution for increased robustness. To deal with this, inspired by generative adversarial networks (GANs), we investigate the problem of adversarial learning on graph neural networks, and propose a novel framework named NAGNN (i.e., Neighbor-anchoring Adversarial Graph Neural Networks) for graph representation learning, which trains not only a discriminator but also a generator that compete with each other. In particular, we propose a novel neighbor-anchoring strategy, where the generator produces samples with explicit features and neighborhood structures anchored on a reference real node, so that the discriminator can perform neighborhood aggregation on the fake samples to learn superior representation. The advantage of our neighbor-anchoring strategy can be demonstrated both theoretically and empirically. Furthermore, as a by-product, our generator can synthesize realistic-looking features, enabling potential applications such as automatic content summarization. Finally, we conduct extensive experiments on four public benchmark datasets, and achieve promising results under both quantitative and qualitative evaluations. |
| first_indexed | 2024-10-01T04:22:27Z |
| format | Journal Article |
| id | ntu-10356/172860 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T04:22:27Z |
| publishDate | 2023 |
| record_format | dspace |
| spelling | ntu-10356/1728602023-12-27T02:25:43Z Neighbor-anchoring adversarial graph neural networks Liu, Zemin Fang, Yuan Liu, Yong Zheng, Vincent Wenchen School of Computer Science and Engineering Joint NTU-UBC Research Centre of Excellence in Active Living for the Elderly (LILY) Engineering::Computer science and engineering Generative Adversarial Network Graph Neural Network Graph neural networks (GNNs) have witnessed widespread adoption due to their ability to learn superior representations for graph data. While GNNs exhibit strong discriminative power, they often fall short of learning the underlying node distribution for increased robustness. To deal with this, inspired by generative adversarial networks (GANs), we investigate the problem of adversarial learning on graph neural networks, and propose a novel framework named NAGNN (i.e., Neighbor-anchoring Adversarial Graph Neural Networks) for graph representation learning, which trains not only a discriminator but also a generator that compete with each other. In particular, we propose a novel neighbor-anchoring strategy, where the generator produces samples with explicit features and neighborhood structures anchored on a reference real node, so that the discriminator can perform neighborhood aggregation on the fake samples to learn superior representation. The advantage of our neighbor-anchoring strategy can be demonstrated both theoretically and empirically. Furthermore, as a by-product, our generator can synthesize realistic-looking features, enabling potential applications such as automatic content summarization. Finally, we conduct extensive experiments on four public benchmark datasets, and achieve promising results under both quantitative and qualitative evaluations. Agency for Science, Technology and Research (A*STAR) This work was supported by the Agency for Science, Technology and Research (A*STAR) through AME Programmatic Funds under Grant A20H6b0151. 2023-12-27T02:25:43Z 2023-12-27T02:25:43Z 2023 Journal Article Liu, Z., Fang, Y., Liu, Y. & Zheng, V. W. (2023). Neighbor-anchoring adversarial graph neural networks. IEEE Transactions On Knowledge and Data Engineering, 35(1), 784-795. https://dx.doi.org/10.1109/TKDE.2021.3087970 1041-4347 https://hdl.handle.net/10356/172860 10.1109/TKDE.2021.3087970 2-s2.0-85145168819 1 35 784 795 en A20H6b0151 IEEE Transactions on Knowledge and Data Engineering © 2021 IEEE. All rights reserved. |
| spellingShingle | Engineering::Computer science and engineering Generative Adversarial Network Graph Neural Network Liu, Zemin Fang, Yuan Liu, Yong Zheng, Vincent Wenchen Neighbor-anchoring adversarial graph neural networks |
| title | Neighbor-anchoring adversarial graph neural networks |
| title_full | Neighbor-anchoring adversarial graph neural networks |
| title_fullStr | Neighbor-anchoring adversarial graph neural networks |
| title_full_unstemmed | Neighbor-anchoring adversarial graph neural networks |
| title_short | Neighbor-anchoring adversarial graph neural networks |
| title_sort | neighbor anchoring adversarial graph neural networks |
| topic | Engineering::Computer science and engineering Generative Adversarial Network Graph Neural Network |
| url | https://hdl.handle.net/10356/172860 |
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