arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2301.02569cs.DScs.CC
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稀疏图中模式的发现与计数

Finding and Counting Patterns in Sparse Graphs

Balagopal Komarath, Anant Kumar, Suchismita Mishra, Aditi Sethia

摘要:

我们认为算法发现和计数小,固定的图形在稀疏的主机图。在非稀疏情况下,参数树深和树宽分别在快速算法、常数空间算法和多项式空间算法中起着至关重要的作用。我们发现了两个新的参数,我们称之为匹配树深和匹配树宽。我们发现,发现和计数模式与低匹配树深和低匹配树宽可以做渐近快于现有的算法时,主机图是稀疏的许多模式。作为一个应用程序,以寻找和计数固定大小的模式,我们发现$\otilde(m^3)$ -时间\footnote{$\otilde$ hides factors that are logarithmic in the input size.},恒定空间算法的周期长度最多$11$和$\otilde(m^2)$ -时间,多项式空间算法的路径长度最多$10$。

英文摘要:

We consider algorithms for finding and counting small, fixed graphs in sparse host graphs. In the non-sparse setting, the parameters treedepth and treewidth play a crucial role in fast, constant-space and polynomial-space algorithms respectively. We discover two new parameters that we call matched treedepth and matched treewidth. We show that finding and counting patterns with low matched treedepth and low matched treewidth can be done asymptotically faster than the existing algorithms when the host graphs are sparse for many patterns. As an application to finding and counting fixed-size patterns, we discover $\otilde(m^3)$-time \footnote{$\otilde$ hides factors that are logarithmic in the input size.}, constant-space algorithms for cycles of length at most $11$ and $\otilde(m^2)$-time, polynomial-space algorithms for paths of length at most $10$.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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