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- Graph Robustness
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- Graph robustness is an important research direction in the field of graph representation learning in recent years,
- and we have integrated graph robustness-related algorithms in AutoGL, which can be easily used in conjunction with other modules.
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- Preliminaries
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- In AutoGL, we divide the algorithms for graph robustness into three categories, which are placed in different modules for implementation.
- Robust graph feature engineering aims to generate robust graph features in the data pre-processing phase to enhance the robustness of downstream tasks.
- Robust graph neural networks, on the other hand, are designed at the model level to ensure the robustness of the model during the training process.
- Robust graph neural network architecture search aims to search for a robust graph neural network architecture.
- Each of these three types of graph robustness algorithms will be described in the following sections.
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- Robust Graph Feature Engineering
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- Robust Graph Neural Networks
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- Robust Graph Neural Architecture Search
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