English(EN) Fast Geometric Embedding for Node Influence Maximization

新的几何嵌入方法加速了网络中节点影响力的最大化

作者 PulseAugur 编辑部 · [1 个来源] · 2026-04-29 04:00

研究人员开发了一种新颖的力布局算法，将大规模图嵌入到低维空间中。这种嵌入使得到原点的径向距离可以作为各种中心性度量的代理，与度数和PageRank等指标高度相关。与传统的贪婪算法相比，该方法为识别网络中有影响力的节点提供了一种更快、更具可扩展性的替代方案。 AI

影响为分析网络结构和识别关键节点提供了一种更有效的方法，可能影响基于图的人工智能应用。

排序理由这是一篇详细介绍图嵌入和节点影响力最大化新算法的研究论文。

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arXiv cs.LG TIER_1 English(EN) · Alexander Kolpakov, Igor Rivin · 2026-04-29 04:00

用于节点影响力最大化的快速几何嵌入

arXiv:2506.07435v3 Announce Type: replace-cross Abstract: Computing classical centrality measures such as betweenness and closeness is computationally expensive on large-scale graphs. In this work, we introduce an efficient force layout algorithm that embeds a graph into a low-di…