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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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- Robust Graph Neural Architecture Search aims to search for adversarial robust Graph Neural Networks under attacks.
- In AutoGL, this module is the code realization of G-RNA.
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- Specifically, we design a robust search space for the message-passing mechanism by adding the adjacency mask operations into the search space,
- which is inspired by various defensive operators and allows us to search for defensive GNNs.
- Furthermore, we define a robustness metric to guide the search procedure, which helps to filter robust architectures.
- G-RNA allows us to effectively search for optimal robust GNNs and understand GNN robustness from an architectural perspective.
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- Adjacency Mask Operations
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- Inspired from the success of current defensive approaches, we conclude the properties of operations on graph structure for robustness and
- design representative defensive operators in our search space accordingly.
- In this way, we can choose the most appropriate defensive strategies when confronting perturbed graphs.
- To our best knowledge, this is the first time for the search space to be designed with a specific purpose to enhance the robustness of GNNs.
- Specifically, we include five mask operations in the search space.
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- - Identity keeps the same adjacency matrix as previous layer
- - Low Rank Approximation (LRA) reconstructs the adjacency matrix from the top-k components of singular value decomposition.
- - Node Feature Similarity (NFS) deletes edges that have small jaccard similarities among node features.
- - Neighbor Importance Estimation (NIE) updates mask values with a pruning strategy base on quantifying the relevance among nodes.
- - Variable Power Operator (VPO) forms a variable power graph from the original adjacency matrix weighted by the parameters of influence strengths
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- Measuring Robustnes
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- Intuitively, the performance of a robust GNN should not deteriorate too much when confronting various perturbed
- graph data.
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