Discovering Information Diffusion Processes Based on Hidden Markov Models for Social Network Services

2015 ◽  
pp. 170-182 ◽  
Author(s):  
Berny Carrera ◽  
JinSung Lee ◽  
Jae-Yoon Jung
Keyword(s):  
Social Network ◽  
Hidden Markov Models ◽  
Information Diffusion ◽  
Markov Models ◽  
Diffusion Processes ◽  
Hidden Markov ◽  
Network Services ◽  
Social Network Services

Identity Perception and Information Diffusion in Social Network Services

2015 ◽  
Vol 2015 (1) ◽  
pp. 16908
Author(s):  
Young-Kyu Kim ◽  
Dongwon Lee ◽  
Janghyuk Lee ◽  
Ji-Hwan Lee
Keyword(s):  
Social Network ◽  
Information Diffusion ◽  
Network Services ◽  
Social Network Services ◽  
Identity Perception

scholarly journals Privacy-Preserving Discovery of Topic-Based Events from Social Sensor Signals: An Experimental Study on Twitter

2014 ◽  
Vol 2014 ◽  
pp. 1-5 ◽  
Author(s):  
Duc T. Nguyen ◽  
Jai E. Jung
Keyword(s):  
Social Network ◽  
Information Diffusion ◽  
Relevant Information ◽  
Network Services ◽  
Social Software ◽  
Smart Devices ◽  
Social Events ◽  
The Social ◽  
Social Network Services ◽  
Social Sensor

Social network services (e.g., Twitter and Facebook) can be regarded as social sensors which can capture a number of events in the society. Particularly, in terms of time and space, various smart devices have improved the accessibility to the social network services. In this paper, we present a social software platform to detect a number of meaningful events from information diffusion patterns on such social network services. The most important feature is to process the social sensor signal for understanding social events and to support users to share relevant information along the social links. The platform has been applied to fetch and cluster tweets from Twitter into relevant categories to reveal hot topics.

Predicting the scale of information diffusion in social network services

2013 ◽  
Vol 20 ◽  
pp. 100-104 ◽  
Author(s):  
Zheng-ren LI ◽  
Ting-jie LÜ ◽  
Wen-hua SHI ◽  
Xiao-hang ZHANG
Keyword(s):  
Social Network ◽  
Information Diffusion ◽  
Network Services ◽  
Social Network Services

Estimating Personality Impression from Speech Record Using Hidden Markov Models

2015 ◽  
Vol 135 (12) ◽  
pp. 1517-1523 ◽  
Author(s):  
Yicheng Jin ◽  
Takuto Sakuma ◽  
Shohei Kato ◽  
Tsutomu Kunitachi
Keyword(s):  
Hidden Markov Models ◽  
Markov Models ◽  
Hidden Markov

Hidden Markov Processes

2014 ◽  
Author(s):  
M. Vidyasagar
Keyword(s):  
Hidden Markov Models ◽  
Markov Processes ◽  
Viterbi Algorithm ◽  
Markov Models ◽  
Hidden Markov ◽  
Local Alignment ◽  
Biological Applications ◽  
Standard Material ◽  
Hidden Markov Processes ◽  
Genomics And Proteomics

This book explores important aspects of Markov and hidden Markov processes and the applications of these ideas to various problems in computational biology. It starts from first principles, so that no previous knowledge of probability is necessary. However, the work is rigorous and mathematical, making it useful to engineers and mathematicians, even those not interested in biological applications. A range of exercises is provided, including drills to familiarize the reader with concepts and more advanced problems that require deep thinking about the theory. Biological applications are taken from post-genomic biology, especially genomics and proteomics. The topics examined include standard material such as the Perron–Frobenius theorem, transient and recurrent states, hitting probabilities and hitting times, maximum likelihood estimation, the Viterbi algorithm, and the Baum–Welch algorithm. The book contains discussions of extremely useful topics not usually seen at the basic level, such as ergodicity of Markov processes, Markov Chain Monte Carlo (MCMC), information theory, and large deviation theory for both i.i.d and Markov processes. It also presents state-of-the-art realization theory for hidden Markov models. Among biological applications, it offers an in-depth look at the BLAST (Basic Local Alignment Search Technique) algorithm, including a comprehensive explanation of the underlying theory. Other applications such as profile hidden Markov models are also explored.

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