GraphMinNet: Learning Dependencies in Graphs with Light Complexity Minimal Architecture

• URL: http://arxiv.org/abs/2502.00282v1
• Date: Sat, 01 Feb 2025 02:46:48 GMT
• Title: GraphMinNet: Learning Dependencies in Graphs with Light Complexity Minimal Architecture
• Authors: Md Atik Ahamed, Andrew Cheng, Qiang Ye, Qiang Cheng,
• Abstract summary: This paper introduces GraphMinNet, a novel GNN architecture that generalizes the idea of minimal Gated Recurrent Units to graph-structured data. Our approach achieves efficient LRD modeling with linear computational complexity. Our results show superior performance on 6 out of 10 datasets and competitive results on the others.
• Score: 12.267920696617017
• License:
• Abstract: Graph Neural Networks (GNNs) have demonstrated remarkable success in various applications, yet they often struggle to capture long-range dependencies (LRD) effectively. This paper introduces GraphMinNet, a novel GNN architecture that generalizes the idea of minimal Gated Recurrent Units to graph-structured data. Our approach achieves efficient LRD modeling with linear computational complexity while maintaining permutation equivariance and stability. The model incorporates both structural and positional information through a unique combination of feature and positional encodings, leading to provably stronger expressiveness than the 1-WL test. Theoretical analysis establishes that GraphMinNet maintains non-decaying gradients over long distances, ensuring effective long-range information propagation. Extensive experiments on ten diverse datasets, including molecular graphs, image graphs, and synthetic networks, demonstrate that GraphMinNet achieves state-of-the-art performance while being computationally efficient. Our results show superior performance on 6 out of 10 datasets and competitive results on the others, validating the effectiveness of our approach in capturing both local and global graph structures.

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