GraphMETRO: A Novel GNN Architecture for Enhanced Generalization by Addressing Complex Distribution Shifts Using a Mixture of Aligned Experts
Graph Neural Networks (GNNs) often struggle with real-world data due to complex distribution shifts. GraphMETRO, a novel GNN architecture, tackles this challenge by employing a mixture-of-experts approach to decompose and mitigate these shifts, leading to improved generalization and performance on various graph-based tasks.
Bridging Generalization and Expressivity in Graph Neural Networks: A Variance-Based Analysis
The generalization ability of graph neural networks (GNNs) is closely tied to the variance in graph structures they can capture, and this variance can be analyzed through the lens of expressivity, offering a way to understand how a more expressive GNN can sometimes lead to better generalization.
The VC Dimension of 1-Dimensional Graph Neural Networks with Unbounded Graph Size is Infinite
Even simple 1-dimensional Graph Neural Networks (GNNs) with limited parameters have an infinite VC dimension for unbounded graphs, suggesting inherent limitations in their generalization ability regardless of activation function complexity.
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