DeltaGNN: Graph Neural Network with Information Flow Control
This addresses a key bottleneck in GNNs for researchers and practitioners working with graph-structured data, offering a scalable and generalizable solution, though it is incremental as it builds on existing GNN frameworks.
The paper tackles the problem of over-smoothing and over-squashing in Graph Neural Networks (GNNs) for semi-supervised node classification, proposing DeltaGNN with information flow control to capture long-range interactions, achieving superior performance across 10 real-world datasets with linear computational overhead.
Graph Neural Networks (GNNs) are popular deep learning models designed to process graph-structured data through recursive neighborhood aggregations in the message passing process. When applied to semi-supervised node classification, the message-passing enables GNNs to understand short-range spatial interactions, but also causes them to suffer from over-smoothing and over-squashing. These challenges hinder model expressiveness and prevent the use of deeper models to capture long-range node interactions (LRIs) within the graph. Popular solutions for LRIs detection are either too expensive to process large graphs due to high time complexity or fail to generalize across diverse graph structures. To address these limitations, we propose a mechanism called \emph{information flow control}, which leverages a novel connectivity measure, called \emph{information flow score}, to address over-smoothing and over-squashing with linear computational overhead, supported by theoretical evidence. Finally, to prove the efficacy of our methodology we design DeltaGNN, the first scalable and generalizable approach for detecting long-range and short-range interactions. We benchmark our model across 10 real-world datasets, including graphs with varying sizes, topologies, densities, and homophilic ratios, showing superior performance with limited computational complexity. The implementation of the proposed methods are publicly available at https://github.com/basiralab/DeltaGNN.