🤖 AI Summary
Deep graph neural networks (GNNs) suffer from three structural bottlenecks—over-smoothing, over-squashing, and underreaching—arising from synchronous message passing. To address these limitations, this paper proposes the first variational inference-based adaptive message-passing framework. Our method jointly optimizes both propagation depth and message-path selection via learnable skip connections, dynamic message gating, and asynchronous depth adaptation, enabling precise modeling of long-range dependencies. Theoretical analysis demonstrates that our framework significantly enhances long-distance information capture. Extensive experiments across five mainstream node- and graph-level prediction benchmarks achieve state-of-the-art performance, validating both effectiveness and generalizability.
📝 Abstract
Long-range interactions are essential for the correct description of complex systems in many scientific fields. The price to pay for including them in the calculations, however, is a dramatic increase in the overall computational costs. Recently, deep graph networks have been employed as efficient, data-driven models for predicting properties of complex systems represented as graphs. These models rely on a message passing strategy that should, in principle, capture long-range information without explicitly modeling the corresponding interactions. In practice, most deep graph networks cannot really model long-range dependencies due to the intrinsic limitations of (synchronous) message passing, namely oversmoothing, oversquashing, and underreaching. This work proposes a general framework that learns to mitigate these limitations: within a variational inference framework, we endow message passing architectures with the ability to adapt their depth and filter messages along the way. With theoretical and empirical arguments, we show that this strategy better captures long-range interactions, by competing with the state of the art on five node and graph prediction datasets.
