English(EN) Exponential random graph models with soft clique constraints

新的随机图模型倾向于稀疏团结构

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-01 04:00

研究人员开发了一种新的指数随机图模型，该模型包含软团约束。该模型为具有较少r-团的图分配更高的概率，由正权重参数控制。研究证明，对于大型图，该模型渐近地倾向于划分为大小大致相等的r-1个部分，部分之间的边密度约为1/2，部分内部的边密度低于指定epsilon。只要权重为正，这些结构属性就保持一致，与分配的权重无关。 AI

排序理由学术论文发表在arXiv上，详细介绍了图论的新数学模型。[lever_c_demoted from research: ic=1 ai=0.1]

在 arXiv cs.AI 阅读 →

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arXiv cs.AI TIER_1 English(EN) · Yasmin Tousinejad, Vera Koponen · 2026-09-01 04:00

具有软团簇约束的指数随机图模型

arXiv:2608.30869v1 Announce Type: cross Abstract: Let $r\geq3$ be fixed, and let $\mathbf{G}_n$ be the set of all simple graphs with vertex set $[n]=\{1,\ldots,n\}$. We consider an exponential random graph model which gives higher probability to $G \in \mathbf{G}_n$ than to $H \i…

本文如何被排名

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具有软团簇约束的指数随机图模型

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