DeltaGNN: Graph Neural Network with Information Flow Control

• URL: http://arxiv.org/abs/2501.06002v1
• Date: Fri, 10 Jan 2025 14:34:20 GMT
• Title: DeltaGNN: Graph Neural Network with Information Flow Control
• Authors: Kevin Mancini, Islem Rekik,
• Abstract summary: Graph Neural Networks (GNNs) are designed to process graph-structured data through neighborhood aggregations in the message passing process. Message-passing enables GNNs to understand short-range spatial interactions, but also causes them to suffer from over-smoothing and over-squashing. We propose a mechanism called emph information flow control to address over-smoothing and over-squashing with linear computational overhead. We benchmark our model across 10 real-world datasets, including graphs with varying sizes, topologies, densities, and homophilic ratios, showing superior performance
• Score: 5.563171090433323
• License:
• Abstract: 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.

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