scholarly journals Extracting Knowledge from the Geometric Shape of Social Network Data Using Topological Data Analysis

Entropy ◽  
2017 ◽  
Vol 19 (7) ◽  
pp. 360 ◽  
Author(s):  
Khaled Almgren ◽  
Minkyu Kim ◽  
Jeongkyu Lee
Keyword(s):  
Social Network ◽  
Data Analysis ◽  
Geometric Shape ◽  
Topological Data Analysis ◽  
Network Data ◽  
Social Network Data ◽  
Topological Data

scholarly journals QUADRIVEN: A Framework for Qualitative Taxi Demand Prediction Based on Time-Variant Online Social Network Data Analysis

Sensors ◽  
2019 ◽  
Vol 19 (22) ◽  
pp. 4882 ◽  
Author(s):  
Fernando Terroso-Saenz ◽  
Andres Muñoz ◽  
José Cecilia
Keyword(s):  
Social Network ◽  
Data Analysis ◽  
Online Social Networks ◽  
Road Traffic ◽  
Online Social Network ◽  
Network Data ◽  
Regression Problem ◽  
Factors Affecting ◽  
Demand Prediction ◽  
Social Network Data

Road traffic pollution is one of the key factors affecting urban air quality. There is a consensus in the community that the efficient use of public transport is the most effective solution. In that sense, much effort has been made in the data mining discipline to come up with solutions able to anticipate taxi demands in a city. This helps to optimize the trips made by such an important urban means of transport. However, most of the existing solutions in the literature define the taxi demand prediction as a regression problem based on historical taxi records. This causes serious limitations with respect to the required data to operate and the interpretability of the prediction outcome. In this paper, we introduce QUADRIVEN (QUalitative tAxi Demand pRediction based on tIme-Variant onlinE social Network data analysis), a novel approach to deal with the taxi demand prediction problem based on human-generated data widely available on online social networks. The result of the prediction is defined on the basis of categorical labels that allow obtaining a semantically-enriched output. Finally, this proposal was tested with different models in a large urban area, showing quite promising results with an F1 score above 0.8.

scholarly journals Social Network Big Data Hierarchical High-Quality Node Mining

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Dongning Jia ◽  
Bo Yin ◽  
Xianqing Huang
Keyword(s):  
Big Data ◽  
Social Network ◽  
Data Analysis ◽  
Shopping Mall ◽  
Network Data ◽  
Clustering Methods ◽  
Learning Mechanisms ◽  
Social Network Data ◽  
Analysis Methods ◽  
Network Big Data

Compared with the conventional network data analysis, the data analysis based on social network has a very clear object of analysis, various forms of analysis, and more methods and contents of analysis. If the conventional analysis methods are applied to social network data analysis, we will find that the analysis results do not reach our expected results. The results of the above studies are usually based on statistical methods and machine learning methods, but some systems use other methods, such as self-organizing self-learning mechanisms and concept retrieval. With regard to the current data analysis methods, data models, and social network data, this paper conducts a series of researches from data acquisition, data cleaning and processing, data model application and optimization of the model in the process of application, and how the formed data analysis results can be used for managers to make decisions. In this paper, the number of customer evaluations, the time of evaluation, the frequency of evaluation, and the score of evaluation are clustered and analyzed, and finally, the results obtained by the two clustering methods applied in the analysis process are compared to build a customer grading system. The analysis results can be used to maintain the current Amazon purchase customers in a hierarchical manner, and the most valuable customers need to be given key attention, combining social network big data with micro marketing to improve Amazon’s sales performance and influence, developing from the original single shopping mall model to a comprehensive e-commerce platform, and cultivating their own customer base.

Generating demand responsive bus routes from social network data analysis

2021 ◽  
Vol 128 ◽  
pp. 103194
Author(s):  
Lidia Sala ◽  
Steve Wright ◽  
Caitlin Cottrill ◽  
Emilio Flores-Sola
Keyword(s):  
Social Network ◽  
Data Analysis ◽  
Network Data ◽  
Social Network Data
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