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.

本文介绍了一种增强图神经网络（Graph Neural Networks）的新方法，即 Distance Recomputator 和 Topology Reconstructor。通过动态编码方案，Distance Recomputator 动态重新调整 k-hop 邻域内的节点距离，提高节点表示的准确性和适应性。同时，Topology Reconstructor 基于计算出的 ' 相似距离 ' 调整了局部图结构，优化了网络配置以改善学习效果。这些方法解决了传统图神经网络中静态节点表示和固定聚合方案的局限性，提供了一种更细致的建模复杂动态图拓扑结构的方法。此外，我们的实验评估结果显示，所提出的 Distance Recomputator 和 Topology Reconstructor 在各种基准数据集上都具有显著的性能优势。这些方法不仅提升了节点关系建模的准确性，还通过异步聚合机制优化了信息聚合效率。这种方法在处理动态或大规模图的情景中表现出强大的鲁棒性和适用性，展示了这些方法在实际图学习任务中的潜力。