Understanding the Generalization Performance of GNNs: Topology Awareness and Future Directions | HackerNoonGNNs' topology awareness is crucial for their generalization performance, particularly in semi-supervised tasks.
Exploring Topology Awareness, Generalization, and Active Learning in Graph Neural Networks | HackerNoonIncreasing topology awareness in GNNs doesn't guarantee improved generalization performance due to complexities in varying structures across domains.
Framework for Analyzing Topology Awareness and Generalization in Graph Neural Networks | HackerNoonThe framework investigates how topology awareness affects generalization performance in GNNs using metric distortion.
On the Topology Awareness and Generalization Performance of Graph Neural Networks: Main Results | HackerNoonThe study explores the relationship between generalization performance and structural distance in Graph Neural Networks, emphasizing the role of topology awareness.
Understanding Topology Awareness in Graph Neural Networks | HackerNoonGNN topology awareness impacts generalization performance, revealing potential issues with unfair generalization across different structural groups.
Case Study: Evaluating GNN Performance Using Shortest-Path Distance for Generalization and Fairness | HackerNoonOur framework effectively analyzes GNN performance through a case study focused on shortest path distance, offering insights into real-world applications and graph learning problems.
Understanding the Generalization Performance of GNNs: Topology Awareness and Future Directions | HackerNoonGNNs' topology awareness is crucial for their generalization performance, particularly in semi-supervised tasks.
Exploring Topology Awareness, Generalization, and Active Learning in Graph Neural Networks | HackerNoonIncreasing topology awareness in GNNs doesn't guarantee improved generalization performance due to complexities in varying structures across domains.
Framework for Analyzing Topology Awareness and Generalization in Graph Neural Networks | HackerNoonThe framework investigates how topology awareness affects generalization performance in GNNs using metric distortion.
On the Topology Awareness and Generalization Performance of Graph Neural Networks: Main Results | HackerNoonThe study explores the relationship between generalization performance and structural distance in Graph Neural Networks, emphasizing the role of topology awareness.
Understanding Topology Awareness in Graph Neural Networks | HackerNoonGNN topology awareness impacts generalization performance, revealing potential issues with unfair generalization across different structural groups.
Case Study: Evaluating GNN Performance Using Shortest-Path Distance for Generalization and Fairness | HackerNoonOur framework effectively analyzes GNN performance through a case study focused on shortest path distance, offering insights into real-world applications and graph learning problems.