An Efficient Search Strategy for Service Provider Selection in Complex Social Networks

Author(s):  
Yu Xu ◽  
Jianxun Liu ◽  
Mingdong Tang ◽  
Buqing Cao ◽  
Xiaoqing Liu
Algorithms ◽  
2020 ◽  
Vol 13 (6) ◽  
pp. 139 ◽  
Author(s):  
Vincenzo Cutello ◽  
Georgia Fargetta ◽  
Mario Pavone ◽  
Rocco A. Scollo

Community detection is one of the most challenging and interesting problems in many research areas. Being able to detect highly linked communities in a network can lead to many benefits, such as understanding relationships between entities or interactions between biological genes, for instance. Two different immunological algorithms have been designed for this problem, called Opt-IA and Hybrid-IA, respectively. The main difference between the two algorithms is the search strategy and related immunological operators developed: the first carries out a random search together with purely stochastic operators; the last one is instead based on a deterministic Local Search that tries to refine and improve the current solutions discovered. The robustness of Opt-IA and Hybrid-IA has been assessed on several real social networks. These same networks have also been considered for comparing both algorithms with other seven different metaheuristics and the well-known greedy optimization Louvain algorithm. The experimental analysis conducted proves that Opt-IA and Hybrid-IA are reliable optimization methods for community detection, outperforming all compared algorithms.


2018 ◽  
Vol 14 (6) ◽  
pp. 155014771878237
Author(s):  
Wenbiao Tian ◽  
Guosheng Rui ◽  
Daoguang Dong ◽  
Jian Kang

This article introduces a new algorithm that constructs an efficient search strategy, called parallel search, for blind adaptive Karhunen–Loéve transform. Unlike anterior Karhunen–Loéve transform, the proposed algorithm converges quickly by searching for solutions in different directions simultaneously. Moreover, the process is “blind,” which means that minimal information about the original data is used. The new algorithm also avoids repeating the Karhunen–Loéve transform basis learning step in data compression applications. Numerical simulation results verify the validity of the theory and illustrate the capability of the proposed algorithm.


Author(s):  
Monique V. Vieira ◽  
Bruno M. Fonseca ◽  
Rodrigo Damazio ◽  
Paulo B. Golgher ◽  
Davi de Castro Reis ◽  
...  

2016 ◽  
Vol 8 (4) ◽  
pp. 94-102 ◽  
Author(s):  
Krzysztof Stepaniuk

Abstract This paper attempts to decompose, as well as perform a quantitative and qualitative analysis of the way to externalize the perception of the accommodation service. The research material consisted of the opinions of users of accommodation facilities, located in the vicinity of the twelve selected national parks in Poland. It was assumed that the reflection of the perception of the quality of the service process is the transfer of intangible content related to the service itself, which can be externalized, among other things, through entries in social networks. The study was conducted based on the theory of memes as cultural information carriers. According to this theory, in such a transmission, it is possible to distinguish certain components, which can be defined as memes. Therefore, it is possible to analyse and track their presence, transfer, as well as incidence. A memetic pool was constructed using the assumptions of the perceptual-cognitive model of the formation of the tourism image. It was a direct expression of the mental changes of the recipient, resulting from the use of the service. Studies of this type are intended to optimize the design of services in terms of building positive relationships on the line service provider-customer. At the same time, they allow for a slightly different, evolutionary approach to analyses, concerning the formation of the image of the service provider, as well as forming the expectations of service recipients.


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