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Related Questions
- What are the typical architectures used to prevent over-smoothing in Graph Convolutional Networks (GCNs), and how do they address the issue?
- How does the incorporation of residual connections in GCNs affect the mitigation of over-smoothing, particularly in cold-start scenarios?
- Can you explain the concept of over-smoothing in the context of GCNs and how it relates to the performance of the model in cold-start scenarios?
- What are the key differences between layer-wise and residual connections in GCNs, and how do they impact the learning of node representations?
- In what ways do different GCN variants, such as those with layer-wise or residual connections, address the problem of over-smoothing in cold-start scenarios?
- How do the design choices in GCN architectures, including the use of residual connections, affect the model's ability to handle node classification tasks in cold-start scenarios?
- What are some potential strategies for mitigating over-smoothing in GCNs, and how do they relate to the architecture of the network, such as the use of residual connections?
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