scholarly journals Cover Time in Edge-Uniform Stochastically-Evolving Graphs

2017 ◽  
pp. 441-455
Author(s):  
Ioannis Lamprou ◽  
Russell Martin ◽  
Paul Spirakis
Keyword(s):  
Cover Time ◽  
Evolving Graphs

scholarly journals Cover Time in Edge-Uniform Stochastically-Evolving Graphs

Algorithms ◽  
2018 ◽  
Vol 11 (10) ◽  
pp. 149 ◽  
Author(s):  
Ioannis Lamprou ◽  
Russell Martin ◽  
Paul Spirakis
Keyword(s):  
Random Walk ◽  
Single Agent ◽  
Simple Random Walk ◽  
Electrical Network ◽  
Dynamic Graph ◽  
Cover Time ◽  
Time Step ◽  
Evolving Graphs ◽  
Expected Time ◽  
Experimental Findings

We define a general model of stochastically-evolving graphs, namely the edge-uniform stochastically-evolving graphs. In this model, each possible edge of an underlying general static graph evolves independently being either alive or dead at each discrete time step of evolution following a (Markovian) stochastic rule. The stochastic rule is identical for each possible edge and may depend on the past k ≥ 0 observations of the edge’s state. We examine two kinds of random walks for a single agent taking place in such a dynamic graph: (i) The Random Walk with a Delay (RWD), where at each step, the agent chooses (uniformly at random) an incident possible edge, i.e., an incident edge in the underlying static graph, and then, it waits till the edge becomes alive to traverse it. (ii) The more natural Random Walk on what is Available (RWA), where the agent only looks at alive incident edges at each time step and traverses one of them uniformly at random. Our study is on bounding the cover time, i.e., the expected time until each node is visited at least once by the agent. For RWD, we provide a first upper bound for the cases k = 0 , 1 by correlating RWD with a simple random walk on a static graph. Moreover, we present a modified electrical network theory capturing the k = 0 case. For RWA, we derive some first bounds for the case k = 0 , by reducing RWA to an RWD-equivalent walk with a modified delay. Further, we also provide a framework that is shown to compute the exact value of the cover time for a general family of stochastically-evolving graphs in exponential time. Finally, we conduct experiments on the cover time of RWA in edge-uniform graphs and compare the experimental findings with our theoretical bounds.

scholarly journals AutoAudit: Mining Accounting and Time-Evolving Graphs

2020 ◽  
Author(s):  
Meng-Chieh Lee ◽  
Yue Zhao ◽  
Aluna Wang ◽  
Pierre Jinghong Liang ◽  
Leman Akoglu ◽  
...  
Keyword(s):  
Evolving Graphs

scholarly journals Time Series Analysis of Climatic Variables in Peninsular Spain. Trends and Forecasting Models for Data between 20th and 21st Centuries

Climate ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 119
Author(s):  
Pitshu Mulomba Mukadi ◽  
Concepción González-García
Keyword(s):  
Time Series ◽  
Temperature Series ◽  
Climatic Variables ◽  
Monthly Precipitation ◽  
Agricultural Crops ◽  
Arima Models ◽  
Cover Time ◽  
Pests And Diseases ◽  
Historical Series ◽  
Preliminary Phase

Time series of mean monthly temperature and total monthly precipitation are two of the climatic variables most easily obtained from weather station records. There are many studies analyzing historical series of these variables, particularly in the Spanish territory. In this study, the series of these two variables in 47 stations of the provincial capitals of mainland Spain were analyzed. The series cover time periods from the 1940s to 2013; the studies reviewed in mainland Spain go up to 2008. ARIMA models were used to represent their variation. In the preliminary phase of description and identification of the model, a study to detect possible trends in the series was carried out in an isolated manner. Significant trends were found in 15 of the temperature series, and there were trends in precipitation in only five of them. The results obtained for the trends are discussed with reference to those of other, more detailed studies in the different regions, confirming whether the same trend was maintained over time. With the ARIMA models obtained, 12-month predictions were made by measuring errors with the observed data. More than 50% of the series of both were modeled. Predictions with these models could be useful in different aspects of seasonal job planning, such as wildfires, pests and diseases, and agricultural crops.

The cover time of the giant component of a random graph

2008 ◽  
Vol 32 (4) ◽  
pp. 401-439 ◽  
Author(s):  
Colin Cooper ◽  
Alan Frieze
Keyword(s):  
Random Graph ◽  
Giant Component ◽  
Cover Time

Approximating centrality in evolving graphs: toward sublinearity

2017 ◽  
Author(s):  
Benjamin W. Priest ◽  
George Cybenko
Keyword(s):  
Evolving Graphs

scholarly journals Multitarget search on complex networks: A logarithmic growth of global mean random cover time

2017 ◽  
Vol 27 (9) ◽  
pp. 093103 ◽  
Author(s):  
Tongfeng Weng ◽  
Jie Zhang ◽  
Michael Small ◽  
Ji Yang ◽  
Farshid Hassani Bijarbooneh ◽  
...  
Keyword(s):  
Complex Networks ◽  
Cover Time ◽  
Global Mean ◽  
Logarithmic Growth

Monte Carlo Based Incremental PageRank on Evolving Graphs

2017 ◽  
pp. 356-367 ◽  
Author(s):  
Qun Liao ◽  
ShuangShuang Jiang ◽  
Min Yu ◽  
Yulu Yang ◽  
Tao Li
Keyword(s):  
Monte Carlo ◽  
Evolving Graphs
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