The Effect of Agents’ Diversities on the Running Time of the Random Walk-Based Rendezvous Search

In this paper, based on Walktrap algorithm with the idea of random walk, and by selecting the neighbor communities, introducing improved signed probabilistic mixture (SPM) model and considering the edges within the community as positive links and the edges between the communities as negative links, a novel algorithm Walktrap-SPM for detecting overlapping community is proposed. This algorithm not only can identify the overlapping communities, but also can greatly increase the objectivity and accuracy of the results. In order to verify the accuracy, the performance of this algorithm is tested on several representative real-world networks and a set of computer-generated networks based on LFR benchmark. The experimental results indicate that this algorithm can identify the communities accurately, and it is more suitable for overlapping community detection. Compared with Walktrap, SPM and LMF algorithms, the presented algorithm can acquire higher values of modularity and NMI. Moreover, this new algorithm has faster running time than SPM and LMF algorithms.

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Analysis of Biological Record Data: Improvements of the Occupancy–Detection Models

10.1101/408724 ◽

2018 ◽

Author(s):

Shiyu Li

Keyword(s):

Random Walk ◽

List Length ◽

Biodiversity Loss ◽

Model Structure ◽

Large Dataset ◽

Prior Distributions ◽

Running Time ◽

Detection Model ◽

Occupancy Detection ◽

Record Data

AbstractMany indicators are used to monitor the progress of the target which aims to stop the biodiversity loss by 2020. However, the occupancy-detection model which is currently applied to calculate the indicator is biased. Hence, more robust models are required to track the trend of the species precisely. This research first reviews the previous works on improving this occupancy-detection model by changing the prior distributions of one of the quantities and of the models considered previously, a model based on a random walk is found to be the most appropriate although it has some potential deficiencies. Then this research provides some potential improvements of the random walk model by changing the way of modelling the prior distributions of each quantity and changing the model structure. Then the hoverflies datasets are used in this research to analyse the performance of the models. These models are compared by the running times of fitting the models and the plots of the trend of the species of all models. As a result, the categorical list length model is considered to be the most precise model among all models with a reasonable running time. Then, we fit this model with a large dataset, however, it takes a long running time to get the result. Finally, some potential improvements are suggested which may be useful for further research.

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Exact sampling of the infinite horizon maximum of a random walk over a nonlinear boundary

Journal of Applied Probability ◽

10.1017/jpr.2019.9 ◽

2019 ◽

Vol 56 (01) ◽

pp. 116-138

Author(s):

Jose Blanchet ◽

Jing Dong ◽

Zhipeng Liu

Keyword(s):

Random Walk ◽

Steady State ◽

Service Time ◽

Time Distribution ◽

Nonlinear Boundary ◽

Infinite Horizon ◽

Simulation Method ◽

Service Time Distribution ◽

Departure Process ◽

Running Time

AbstractWe present the first algorithm that samples maxn≥0{Sn − nα}, where Sn is a mean zero random walk, and nα with $\alpha \in ({1 \over 2},1)$ defines a nonlinear boundary. We show that our algorithm has finite expected running time. We also apply this algorithm to construct the first exact simulation method for the steady-state departure process of a GI/GI/∞ queue where the service time distribution has infinite mean.

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