Deep Learning Approaches for Link Prediction in Social Network Services

2013 ◽  
pp. 425-432 ◽  
Author(s):  
Feng Liu ◽  
Bingquan Liu ◽  
Chengjie Sun ◽  
Ming Liu ◽  
Xiaolong Wang
Keyword(s):  
Deep Learning ◽  
Social Network ◽  
Link Prediction ◽  
Network Services ◽  
Learning Approaches ◽  
Social Network Services

Deep Learning in Social Media Analysis

2021 ◽  
pp. 164-194
Author(s):  
Vaishali Yogesh Baviskar ◽  
Rachna Yogesh Sable
Keyword(s):  
Social Media ◽  
Deep Learning ◽  
Media Analysis ◽  
Network Services ◽  
Social Media Analytics ◽  
Learning Approaches ◽  
Election Result ◽  
The Social ◽  
Social Network Services

Social media analytics keep on collecting the information from different media platforms and then calculating the statistical data. Twitter is one of the social network services which has ample amount of data where many users used post significant amounts of data on a regular basis. Handling such a large amount of data using traditional tools and technologies is very complicated. One of the solutions to this problem is the use of machine learning and deep learning approaches. In this chapter, the authors present a case study showing the use of Twitter data for predicting the election result of the political parties.

scholarly journals An Information Recommendation Technique Based on Influence and Activeness of Users in Social Networks

2021 ◽  
Vol 11 (6) ◽  
pp. 2530
Author(s):  
Minsoo Lee ◽  
Soyeon Oh
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Recommendation Systems ◽  
Network Services ◽  
The Past ◽  
User Behaviors ◽  
User Influence ◽  
User Activity ◽  
Information Recommendation ◽  
Social Network Services

Over the past few years, the number of users of social network services has been exponentially increasing and it is now a natural source of data that can be used by recommendation systems to provide important services to humans by analyzing applicable data and providing personalized information to users. In this paper, we propose an information recommendation technique that enables smart recommendations based on two specific types of analysis on user behaviors, such as the user influence and user activity. The components to measure the user influence and user activity are identified. The accuracy of the information recommendation is verified using Yelp data and shows significantly promising results that could create smarter information recommendation systems.

Face hallucination and recognition in social network services

2014 ◽  
Vol 71 (6) ◽  
pp. 2035-2049 ◽  
Author(s):  
Feng Jiang ◽  
Seungmin Rho ◽  
Bo-Wei Chen ◽  
Xiaodan Du ◽  
Debin Zhao
Keyword(s):  
Social Network ◽  
Network Services ◽  
Face Hallucination ◽  
Social Network Services
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