A Survey on Mobile Social Networks: Applications, Platforms, System Architectures, and Future Research Directions

2015 ◽  
Vol 17 (3) ◽  
pp. 1557-1581 ◽  
Author(s):  
Xiping Hu ◽  
Terry H. S. Chu ◽  
Victor C. M. Leung ◽  
Edith C.-H. Ngai ◽  
Philippe Kruchten ◽  
...  
Keyword(s):  
Social Networks ◽  
Future Research ◽  
Mobile Social Networks ◽  
Research Directions ◽  
System Architectures ◽  
Future Research Directions

scholarly journals Opportunistic mobile social networks: architecture, privacy, security issues and future directions

2019 ◽  
Vol 9 (2) ◽  
pp. 1145
Author(s):  
Vimitha R. Vidhya Lakshmi ◽  
Gireesh Kumar T.
Keyword(s):  
Social Networks ◽  
Social Networking ◽  
Future Research ◽  
Mobile Social Networks ◽  
Privacy And Security ◽  
Research Directions ◽  
Security Issues ◽  
Mobile Social Networking ◽  
Future Research Directions ◽  
Opportunistic Mobile Social Networks

Mobile Social Networks and its related applications have made a very great impact in the society. Many new technologies related to mobile social networking are booming rapidly now-a-days and yet to boom. One such upcoming technology is Opportunistic Mobile Social Networking. This technology allows mobile users to communicate and exchange data with each other without the use of Internet. This paper is about Opportunistic Mobile Social Networks, its architecture, issues and some future research directions. The architecture and issues of Opportunistic Mobile Social Networks are compared with that of traditional Mobile Social Networks. The main contribution of this paper is regarding privacy and security issues in Opportunistic Mobile Social Networks. Finally, some future research directions in Opportunistic Mobile Social Networks have been elaborated regarding the data's privacy and security.

Probabilistic Rule Learning Systems

2021 ◽  
Vol 54 (4) ◽  
pp. 1-16
Author(s):  
Abdus Salam ◽  
Rolf Schwitter ◽  
Mehmet A. Orgun
Keyword(s):  
Probability Theory ◽  
Rule Learning ◽  
Background Knowledge ◽  
Learning Systems ◽  
Future Research ◽  
Research Directions ◽  
System Architectures ◽  
Probabilistic Rule ◽  
Future Research Directions ◽  
High Level

This survey provides an overview of rule learning systems that can learn the structure of probabilistic rules for uncertain domains. These systems are very useful in such domains because they can be trained with a small amount of positive and negative examples, use declarative representations of background knowledge, and combine efficient high-level reasoning with the probability theory. The output of these systems are probabilistic rules that are easy to understand by humans, since the conditions for consequences lead to predictions that become transparent and interpretable. This survey focuses on representational approaches and system architectures, and suggests future research directions.

Play It Like Beckham!

2017 ◽  
pp. 43-61
Author(s):  
Sylvaine Castellano ◽  
Insaf Khelladi
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Academic Research ◽  
Future Research ◽  
The Internet ◽  
Research Directions ◽  
Media Usage ◽  
Future Research Directions ◽  
The Impact

New opportunities and challenges are emerging thanks to the growing Internet importance and social media usage. Although practitioners have already recognized the strategic dimension of e-reputation and the power of social media, academic research is still in its infancy when it comes to e-reputation determinants in a social networks context. A study was conducted in the sports setting to explore the impact of social networks on the sportspeople's e-reputation. Whereas the study emphasized (1) the influence of social networks' perception on the sportspeople's e-reputation, and the neutral roles of (2) the motives for following sportspeople online, and (3) the negative content on the Internet, additional insights are formulated on maintaining, restoring and managing e-reputation on social networks. Finally, future research directions are suggested on the role of image to control e-reputation.

scholarly journals A Review on Epidemiological Methods to Detect Untrue Information

2021 ◽  
Vol 10 (11) ◽  
pp. 16-19
Author(s):  
Akanksha Mathur ◽  
◽  
Prof. C. P. Gupta ◽  
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Epidemiological Methods ◽  
Future Research ◽  
Epidemiological Models ◽  
Research Directions ◽  
Diffusion Of Information ◽  
Prime Concern ◽  
History Of ◽  
Future Research Directions

Online propagation of untrue information has been and is becoming an increasing problem. Understanding and modeling the diffusion of information on Online Social Networks (OSN's) of voluminous data is the prime concern. The paper provides the history of the epidemic spread and its analogy with untrue information. This paper provides a review of untrue information on online social networks and methods of detection of untrue information based on epidemiological models. Open research challenges and potential future research directions are also highlighted. The paper aimed at aiding research for the identification of untrue information on OSNs.

An Overview of Graph Indexing and Querying Techniques

2011 ◽  
pp. 71-88 ◽  
Author(s):  
Sherif Sakr ◽  
Ghazi Al-Naymat
Keyword(s):  
Social Networks ◽  
Query Language ◽  
Future Research ◽  
Protein Networks ◽  
Research Directions ◽  
Query Graph ◽  
Graph Indexing ◽  
Graph Query ◽  
Graph Query Language ◽  
Future Research Directions

Recently, there has been a lot of interest in the application of graphs in different domains. Graphs have been widely used for data modeling in different application domains such as: chemical compounds, protein networks, social networks and Semantic Web. Given a query graph, the task of retrieving related graphs as a result of the query from a large graph database is a key issue in any graph-based application. This has raised a crucial need for efficient graph indexing and querying techniques. In this chapter, we provide an overview of different techniques for indexing and querying graph databases. An overview of several proposals of graph query language is also given. Finally, we provide a set of guidelines for future research directions.

Games and Social Networks

2013 ◽  
pp. 204-219
Author(s):  
Yulia Bachvarova ◽  
Stefano Bocconi
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Social Network ◽  
Future Research ◽  
Network Games ◽  
Research Directions ◽  
Game Mechanics ◽  
Social Tv ◽  
Future Research Directions ◽  
Game Industry

Social media and social networks have gained an unprecedented role in connecting people, knowledge, and experiences. Game industry is using the power of social networks by creating Social Network Games, which can be even more engaging than traditional games. In this chapter, the main characteristics of Social Network Games and their potential are discussed. This potentiality can also be used for serious games (i.e. games with purposes beyond entertainment) and especially games related to learning and behavioural changes. This leads to introducing the emerging field of Serious Social Network Games and their unique characteristics that make them suitable for serious applications. Finally, the rising phenomenon of Social TV is discussed, which combines the power of TV and social media. Based on a project by the authors, preliminary findings on the most engaging techniques of Social TV Games are presented, together with initial suggestions on what constitutes good game mechanics for such games. The chapter concludes with future research directions for Social Network Games to become even more engaging and effective for purposes beyond pure entertainment.

An Overview of Graph Indexing and Querying Techniques

2013 ◽  
pp. 222-239
Author(s):  
Sherif Sakr ◽  
Ghazi Al-Naymat
Keyword(s):  
Social Networks ◽  
Query Language ◽  
Future Research ◽  
Protein Networks ◽  
Research Directions ◽  
Query Graph ◽  
Graph Indexing ◽  
Graph Query ◽  
Graph Query Language ◽  
Future Research Directions

Recently, there has been a lot of interest in the application of graphs in different domains. Graphs have been widely used for data modeling in different application domains such as: chemical compounds, protein networks, social networks and Semantic Web. Given a query graph, the task of retrieving related graphs as a result of the query from a large graph database is a key issue in any graph-based application. This has raised a crucial need for efficient graph indexing and querying techniques. In this chapter, we provide an overview of different techniques for indexing and querying graph databases. An overview of several proposals of graph query language is also given. Finally, we provide a set of guidelines for future research directions.

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