Distance Recomputator and Topology Reconstructor for Graph Neural Networks

• URL: http://arxiv.org/abs/2406.17281v1
• Date: Tue, 25 Jun 2024 05:12:51 GMT
• Title: Distance Recomputator and Topology Reconstructor for Graph Neural Networks
• Authors: Dong Liu, Meng Jiang,
• Abstract summary: We introduce Distance Recomputator and Topology Reconstructor methodologies, aimed at enhancing Graph Neural Networks (GNNs) The Distance Recomputator dynamically recalibrates node distances using a dynamic encoding scheme, thereby improving the accuracy and adaptability of node representations. The Topology Reconstructor adjusts local graph structures based on computed "similarity distances," optimizing network configurations for improved learning outcomes.
• Score: 22.210886585639063
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper introduces novel methodologies, the Distance Recomputator and Topology Reconstructor, aimed at enhancing Graph Neural Networks (GNNs). The Distance Recomputator dynamically recalibrates node distances within k-hop neighborhoods using a dynamic encoding scheme, thereby improving the accuracy and adaptability of node representations. Concurrently, the Topology Reconstructor adjusts local graph structures based on computed "similarity distances," optimizing network configurations for improved learning outcomes. These methods address the limitations of static node representations and fixed aggregation schemes in traditional GNNs, offering a more nuanced approach to modeling complex and dynamic graph topologies. Furthermore, our experimental evaluations demonstrate significant performance advantages over existing methods across various benchmark datasets. The proposed Distance Recomputator and Topology Reconstructor not only enhance node relationship modeling accuracy but also optimize information aggregation efficiency through an asynchronous aggregation mechanism. This approach proves particularly effective in scenarios involving dynamic or large-scale graphs, showcasing the methods' robustness and applicability in real-world graph learning tasks.

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