Temporal Cliques Admit Sparse Spanners
Temporal Cliques Admit Sparse Spanners
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时间派系承认稀疏扳手
DOI:
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发表时间:
2018
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通讯作者:
Jason Schoeters
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作者:
A. Casteigts;Joseph G. Peters;Jason Schoeters
Let ${cal G}=(G,lambda)$ be a labeled graph on $n$ vertices with $lambda:E_G o mathbb{N}$ a locally injective mapping that assigns to every edge a single integer label. The label is seen as a discrete time when the edge is present. This graph is {em temporally connected} if a path exists with increasing labels from every vertex to every other vertex. In a seminal paper, Kempe, Kleinberg, and Kumar (JCSS 2002) asked whether, given such a labeled graph, a {em sparse} subset of edges can always be found that preserves temporal connectivity if the other edges are removed -- we call such subsets {em temporal spanners}. Recently, Axiotis and Fotakis (ICALP 2016) answered negatively, exhibiting a family of minimally connected temporal graphs with $Omega(n^2)$ edges. The natural question then becomes whether sparse spanners can be found in specific classes of dense graphs.
In this article, we settle the question {em positively} for complete graphs, showing that one can always remove all but $o(n^2)$ edges, whatever the labels, while preserving temporal connectivity. The best approach so far led to removing only $O(n)$ edges, leaving the asymptotic density of the graph unchanged (Akrida et al., ToCS 2017). We start by observing that the same argument can be generalized to removing $O(n^2)$ edges (a sixth of the edges). Then, using a completely different approach, we establish a gradual set of results, showing that a quarter of the edges can be removed, then half of the edges, and eventually {em all} but $O(n log n)$ edges. This result is robust in the sense that it extends, under mild assumptions, to more general models of temporal cliques where the labels may not be locally unique and a same edge may have several labels. The main open question is now to understand where the separation occurs between graphs that admit sparse spanners and graphs that do not.