English(EN) Distance-Preserving Embeddings in Inhomogeneous Random Graphs

新方法改进了复杂网络的距离保持嵌入

作者 PulseAugur 编辑部 · [1 个来源] · 2026-07-14 04:00

研究人员开发了一种新的方法，用于在inhomogeneous随机图（一种具有类型相关边概率的复杂网络）中创建距离保持嵌入。该方法使用基于地标的嵌入来保持最短路径长度，与之前的最坏情况界限相比，提供了更紧凑的维度-失真权衡。通过使用GNN增强的变体，该方法得到了进一步的改进，该变体使用神经网络代理进行精确的最短路径查询，并展示了对真实世界网络的稳健泛化能力。 AI

排序理由该集群包含一篇研究论文，详细介绍了一种新的图嵌入方法。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.LG TIER_1 English(EN) · My Le, Luana Ruiz, Souvik Dhara · 2026-07-14 04:00

Distance-Preserving Embeddings in Inhomogeneous Random Graphs

arXiv:2607.10074v1 Announce Type: new Abstract: Graph machine learning provides powerful tools for understanding complex networks and learning meaningful node representations. A central challenge, however, is designing embeddings with minimal distortion of both local and global f…

本文如何被排名

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Distance-Preserving Embeddings in Inhomogeneous Random Graphs

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