How fast can we reach a target vertex in stochastic temporal graphs?
How fast can we reach a target vertex in stochastic temporal graphs?
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我们能够以多快的速度到达随机时间图中的目标顶点?
DOI:
10.1016/j.jcss.2020.05.005
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发表时间:
2020
影响因子:
1.1
通讯作者:
Akrida E
中科院分区:
文献类型:
--
作者:
Akrida E
Temporal graphs abstractly model real-life inherently dynamic networks. Given a graph G, a temporal graph with G as the underlying graph is a sequence of subgraphs (snapshots) G t of G, where t≥ 1. In this paper we study stochastic temporal graphs, ie stochastic processes G whose random variables are the snapshots of a temporal graph on G. A natural feature observed in various real-life scenarios is a memory effect in the appearance probabilities of particular edges; ie the probability an edge e∈ E appears at time step t depends on its appearance (or absence) at the previous k steps. We study the hierarchy of models of memory-k, k≥ 0, in an edge-centric network evolution setting: every edge of G has its own independent probability distribution for its appearance over time. We thoroughly investigate the complexity of two naturally related, but fundamentally different, temporal path problems, called Minimum Arrival and Best Policy.
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影响因子:
3.8
作者:
Akrida E
通讯作者:
Akrida E
影响因子:
22.7
作者:
O. Michail;P. Spirakis
通讯作者:
P. Spirakis
DOI:
--
发表时间:
2017
期刊:
Safety-critical Systems Symposium
影响因子:
--
作者:
I. Lamprou;R. Martin;P. Spirakis
通讯作者:
P. Spirakis
DOI:
--
发表时间:
2018
期刊:
ACM Interational Symposium on Mobile Ad Hoc Networking and Computing
影响因子:
--
作者:
Sébastien Henri;S. Shneer;Patrick Thiran
通讯作者:
Patrick Thiran
影响因子:
2.8
作者:
Anne-Sophie Himmel;Hendrik Molter;R. Niedermeier;Manuel Sorge
通讯作者:
Anne-Sophie Himmel;Hendrik Molter;R. Niedermeier;Manuel Sorge