RAwR: Role-Aware Rewiring via Approximate Equitable Partition for GNNs
Researchers have introduced RAwR, a novel and computationally efficient rewiring framework designed to enhance the performance of Graph Neural Networks (GNNs) in tasks requiring long-range interactions. Traditional GNNs often suffer from oversquashing, where structural bottlenecks hinder signal propagation across network topologies. RAwR addresses this by augmenting the input graph with a quotient graph derived from approximate equitable partitions, identified through Weisfeiler-Leman graph coloring. This method accelerates communication between nodes sharing identical structural roles, effectively reducing the system's total effective resistance. The framework allows for controllable reduction of the quotient graph, encompassing conventional Master Node rewiring as a special case. Empirical evaluations on homophilic, heterophilic, and synthetic long-range datasets demonstrate that RAwR achieves state-of-the-art results. Additionally, the study provides theoretical foundations using a teacher-student model of linear GNNs and introduces Spectral Role Lift (SRL), a new metric for optimizing predictive performance by identifying the best approximate equitable partition.
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RAwR: Role-Aware Rewiring via Approximate Equitable Partition for GNNs
Researchers have introduced RAwR, a novel and computationally efficient rewiring framework designed to enhance the performance of Graph Neural Networks (GNNs) in tasks requiring long-range interactions. Traditional GNNs often suffer from oversquashing, where structural bottlenecks hinder signal propagation across network topologies. RAwR addresses this by augmenting the input graph with a quotient graph derived from approximate equitable partitions, identified through Weisfeiler-Leman graph coloring. This method accelerates communication between nodes sharing identical structural roles, effectively reducing the system's total effective resistance. The framework allows for controllable reduction of the quotient graph, encompassing conventional Master Node rewiring as a special case. Empirical evaluations on homophilic, heterophilic, and synthetic long-range datasets demonstrate that RAwR achieves state-of-the-art results. Additionally, the study provides theoretical foundations using a teacher-student model of linear GNNs and introduces Spectral Role Lift (SRL), a new metric for optimizing predictive performance by identifying the best approximate equitable partition.
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