2015/04/22 by Jessica Enright, Enright, Jessica, Kitty Meeks +1 · 1 voice · 1 citation
Medicine · Physics and Astronomy · #Complex Network Analysis Techniques #HIV, Drug Use, Sexual Risk #cs.DS #math.CO
paper · pdf · doi:10.48550/arxiv.1504.05773
openalex publication_date 2015/04/22 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Motivated by applications in network epidemiology, we consider the problem of determining whether it is possible to delete at most k edges from a given input graph (of small treewidth) so that the resulting graph avoids a set F of forbidden subgraphs; of particular interest is the problem of determining whether it is possible to delete at most k edges so that the resulting graph has no connected component of more than h vertices, as this bounds the worst-case size of an epidemic. While even this special case of the problem is NP-complete in general (even when h=3), we provide evidence that many of the real-world networks of interest are likely to have small treewidth, and we describe an algorithm which solves the general problem in time \genruntime ~on an input graph having n vertices and whose treewidth is bounded by a fixed constant w, if each of the subgraphs we wish to avoid has at most r vertices. For the special case in which we wish only to ensure that no component has more than h vertices, we improve on this to give an algorithm running in time O((wh)2wn), which we have implemented and tested on real datasets based on cattle movements.