Tag Ranking Multi-agent Semantic Social Networks

2017 ◽  
Author(s):  
Rushdi A. Hamamreh ◽  
Sameh Awad
Keyword(s):  
Social Networks ◽  
Tag Ranking ◽  
Multi Agent

Enhancing Social Networks with Agent and Semantic Web Technologies

2012 ◽  
pp. 83-100 ◽  
Author(s):  
Federico Bergenti ◽  
Enrico Franchi ◽  
Agostino Poggi
Keyword(s):  
Social Networks ◽  
Semantic Web ◽  
Collaborative Work ◽  
Multi Agent Systems ◽  
Semantic Web Technologies ◽  
Agent Based ◽  
Web Technologies ◽  
Agent Systems ◽  
Multi Agent ◽  
Relevant Work

In this chapter, the authors describe the relationships between multi-agent systems, social networks, and the Semantic Web within collaborative work; they also review how the integration of multi-agent systems and Semantic Web technologies and techniques can be used to enhance social networks at all scales. The chapter first provides a review of relevant work on the application of agent-based models and abstractions to the key ingredients of our work: collaborative systems, the Semantic Web, and social networks. Then, the chapter discusses the reasons current multi-agent systems and their foreseen evolution might be a fundamental means for the realization of the future Semantic Social Networks. Finally, some conclusions are drawn.

Multiagent Social Computing

2011 ◽  
Vol 3 (4) ◽  
pp. 56-68 ◽  
Author(s):  
Ben Choi
Keyword(s):  
Social Networks ◽  
Intelligent Agents ◽  
Social Computing ◽  
Discussion Groups ◽  
Personal Computers ◽  
Parallel And Distributed Computing ◽  
Computing Power ◽  
Computing Platform ◽  
The Social ◽  
Multi Agent

This article provides a framework for extending social networks to social computing. When people join social networks, such as Facebook and discussion groups, their personal computers can also join the social networks. This framework facilitates sharing of computing resources among friends and groups. Computers of friends and groups act autonomously to help each other perform various tasks. The framework combines many key technologies, including intelligent agents, multi-agent system, object space, and parallel and distributed computing, into a new computing platform, which has been successfully implemented and tested. With this framework, any person will have access to not only the computing power of his or her own personal computer but also the vast computing power of a community of computers. The collective capabilities of humans and computers working in communities will create complementary capabilities of computing to achieve behaviors that transcend those of people and computers in isolation. The future of computing is moving from personal computers to societies of computers.

scholarly journals Analyzing users’ activity in online social networks over time through a multi-agent framework

2016 ◽  
Vol 20 (11) ◽  
pp. 4331-4345 ◽  
Author(s):  
E. del Val ◽  
C. Martínez ◽  
V. Botti
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Multi Agent ◽  
Over Time

Hierarchical Social Network Analysis Using a Multi-Agent System

2013 ◽  
Vol 5 (3) ◽  
pp. 14-32
Author(s):  
Lizhu Ma ◽  
Xin Zhang
Keyword(s):  
Social Networks ◽  
Student Performance ◽  
Hierarchical Clustering ◽  
Hierarchical Structure ◽  
School Quality ◽  
Multi Agent System ◽  
Agent System ◽  
Funding Policies ◽  
Multi Agent ◽  
K 12

The quality of K-12 education has been a major concern in the nation for years. School systems, just like many other social networks, appear to have a hierarchical structure. Understanding this structure could be the key to better evaluating student performance and improving school quality. Many studies have been focusing on detecting hierarchical structure by using hierarchical clustering algorithms. The authors design an interaction-based similarity measure to accomplish hierarchical clustering in order to detect hierarchical structures in social networks (e.g. school district networks). This method uses a multi-agent system, for it is based on agent interactions. With the network structure detected, they also built a model, which is based on the MAXQ algorithm, to decompose the funding policy task into subtasks and then evaluate these subtasks by using funding distribution policies from past years and looking for possible relationships between student performances and funding policies. For the experiment, the authors used real school data from Bexar county’s 15 school districts in Texas. The first result shows that their interaction-based method is able to generate meaningful clustering and dendrograms for social networks. Additionally the authors’ policy evaluation model is able to evaluate funding policies from the past three years in Bexar County and conclude that increasing funding does not necessarily have a positive impact on student performance and it is generally not the case that the more is spent, the better.

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