Finding influential nodes in social networks based on neighborhood correlation coefficient

2020 ◽  
Vol 194 ◽  
pp. 105580 ◽  
Author(s):  
Ahmad Zareie ◽  
Amir Sheikhahmadi ◽  
Mahdi Jalili ◽  
Mohammad Sajjad Khaksar Fasaei
2021 ◽  
Vol 1818 (1) ◽  
pp. 012177
Author(s):  
Zainab Naseem Attuah ◽  
Firas Sabar Miften ◽  
Evan Abdulkareem Huzan

Author(s):  
I. А. Rodello ◽  
V. Dândolo ◽  
M. M. Grande

Relevance of the study: Based on data collection and analysis, present research made it possible to identify how the activities devised by a group-buying website on Facebook may exert influence on the KPIs for success.Purpose: The main task of present research is to answer the following question: can a digital social network be considered an effective tool for the improvement of key performance indicators (KPI) of a group-buying website?Findings: The research was conducted by considering data collected via mechanical observation using the computational tools Facebook Dashboard and Google Analytics. Data were analyzed using the means of comparison and a Pearson correlation coefficient, which demonstrated positive results of the campaign. When compared, the key performance indicators of the web site relating to Facebook displayed a larger dynamics than the general performance indicators of this web site. By the correlation coefficient, it was found that a higher power range of the Facebook Enterprise´s fan page could result in the increased traffic page hits of the examined web site, and an increase, mainly, in the number of new visitors.Originality / value: This paper analyzes some key performance indicators of a promotional campaign on Facebook for an online group-buying website in the city of Ribeirão Preto, São Paulo State, Brazil.Practical implications: Based on the collected data and performed analysis, it was found that the promotional activities on Facebook can increase the flow of new visitors and attract potential buyers to a group-buying website.Future research: It is recommended to perform further research for other social networks and in other countries.


Author(s):  
Kousik Das ◽  
Rupkumar Mahapatra ◽  
Sovan Samanta ◽  
Anita Pal

Social network is the perfect place for connecting people. The social network is a social structure formed by a set of nodes (persons, organizations, etc.) and a set of links (connection between nodes). People feel very comfortable to share news and information through a social network. This chapter measures the influential persons in different types of online and offline social networks.


Author(s):  
Liqing Qiu ◽  
Shuang Zhang ◽  
Chunmei Gu ◽  
Xiangbo Tian

Influence maximization is a problem that aims to select top [Formula: see text] influential nodes to maximize the spread of influence in social networks. The classical greedy-based algorithms and their improvements are relatively slow or not scalable. The efficiency of heuristic algorithms is fast but their accuracy is unacceptable. Some algorithms improve the accuracy and efficiency by consuming a large amount of memory usage. To overcome the above shortcoming, this paper proposes a fast and scalable algorithm for influence maximization, called K-paths, which utilizes the influence tree to estimate the influence spread. Additionally, extensive experiments demonstrate that the K-paths algorithm outperforms the comparison algorithms in terms of efficiency while keeping competitive accuracy.


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