Big Data in Online Social Networks: User Interaction Analysis to Model User Behavior in Social Networks

Author(s):  
Divyakant Agrawal ◽  
Ceren Budak ◽  
Amr El Abbadi ◽  
Theodore Georgiou ◽  
Xifeng Yan
Author(s):  
Mark Alan Underwood

Intranets are almost as old as the concept of a web site. More than twenty-five years ago the text Business Data Communications closed with a discussion of intranets (Stallings, 1990). Underlying technology improvements in intranets have been incremental; intranets were never seen as killer developments. Yet the popularity of Online Social Networks (OSNs) has led to increased interest in the part OSNs play – or could play – in using intranets to foster knowledge management. This chapter reviews research into how social graphs for an enterprise, team or other collaboration group interacts with the ways intranets have been used to display, collect, curate and disseminate information over the knowledge life cycle. Future roles that OSN-aware intranets could play in emerging technologies, such as process mining, elicitation methods, domain-specific intelligent agents, big data, and just-in-time learning are examined.


IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 108387-108401
Author(s):  
Xuefeng Li ◽  
Yang Xin ◽  
Chensu Zhao ◽  
Yixian Yang ◽  
Shoushan Luo ◽  
...  

2015 ◽  
Vol 2 (1) ◽  
Author(s):  
Ming Jia ◽  
Hualiang Xu ◽  
Jingwen Wang ◽  
Yiqi Bai ◽  
Benyuan Liu ◽  
...  

2020 ◽  
Vol 9 (5) ◽  
pp. 290
Author(s):  
Chuan Ai ◽  
Bin Chen ◽  
Hailiang Chen ◽  
Weihui Dai ◽  
Xiaogang Qiu

Recently, spatial interaction analysis of online social networks has become a big concern. Early studies of geographical characteristics analysis and community detection in online social networks have shown that nodes within the same community might gather together geographically. However, the method of community detection is based on the idea that there are more links within the community than that connect nodes in different communities, and there is no analysis to explain the phenomenon. The statistical models for network analysis usually investigate the characteristics of a network based on the probability theory. This paper analyzes a series of statistical models and selects the MDND model to classify links and nodes in social networks. The model can achieve the same performance as the community detection algorithm when analyzing the structure in the online social network. The construction assumption of the model explains the reasons for the geographically aggregating of nodes in the same community to a degree. The research provides new ideas and methods for nodes classification and geographic characteristics analysis of online social networks and mobile communication networks and makes up for the shortcomings of community detection methods that do not explain the principle of network generation. A natural progression of this work is to geographically analyze the characteristics of social networks and provide assistance for advertising delivery and Internet management.


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