.png){kind=icon}
返回
Motif Counting Beyond Five Nodes
.png){kind=icon}
OA
AI
DOI：10.1145/3186586.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Counting graphlets is a well-studied problem in graph mining and social network analysis. Recently, several papers explored very simple and natural algorithms based on Monte Carlo sampling of Markov Chains (MC), and reported encouraging results. We show, perhaps surprisingly, that such algorithms are outperformed by color coding (CC) [2], a sophisticated algorithmic technique that we extend to the case of graphlet sampling and for which we prove strong statistical guarantees. Our computational experiments on graphs with millions of nodes show CC to be more accurate than MC; furthermore, we formally show that the mixing time of the MC approach is too high in general, even when the input graph has high conductance. All this comes at a price however. While MC is very efficient in terms of space, CC's memory requirements become demanding when the size of the input graph and that of the graphlets grow. And yet, our experiments show that CC can push the limits of the state-of-the-art, both in terms of the size of the input graph and of that of the graphlets.
Keyword:
Motif counting
subgraph counting
color coding
graph mining
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
4.8
论文数:
1.3K
被引数:
4.4K
机构
引用论文
The Influence of an Anti-Steroidogenic Drug (Aminoglutethimide Phosphate) on Pregnancy Maintenance1
PREAI
分享收藏
Regulation of Genes Involved in Heterocyst Differentiation in the Cyanobacterium Anabaena sp. Strain PCC 7120 by a Group 2 Sigma Factor SigC.png)
LifeOAAI
LifeIF0
OAAI分享收藏
查看更多内容