Joint Sequence Complexity Analysis: Application to Social Networks Information Flow

2014 ◽  
Vol 18 (4) ◽  
pp. 75-88 ◽  
Author(s):  
Dimitrios Milioris ◽  
Philippe Jacquet
Keyword(s):  
Social Networks ◽  
Information Flow ◽  
Complexity Analysis ◽  
Sequence Complexity ◽  
Analysis Application

Coupling entropy of co-processing model on social networks

2015 ◽  
Vol 29 (25) ◽  
pp. 1550149
Author(s):  
Zhanli Zhang
Keyword(s):  
Social Networks ◽  
Information Flow ◽  
Structural Characteristics ◽  
Time Lag ◽  
Optimal Routing ◽  
Information Propagation ◽  
Time Processing ◽  
Scale Free ◽  
Routing Strategy ◽  
Processing Ability

Coupling entropy of co-processing model on social networks is investigated in this paper. As one crucial factor to determine the processing ability of nodes, the information flow with potential time lag is modeled by co-processing diffusion which couples the continuous time processing and the discrete diffusing dynamics. Exact results on master equation and stationary state are achieved to disclose the formation. In order to understand the evolution of the co-processing and design the optimal routing strategy according to the maximal entropic diffusion on networks, we propose the coupling entropy comprehending the structural characteristics and information propagation on social network. Based on the analysis of the co-processing model, we analyze the coupling impact of the structural factor and information propagating factor on the coupling entropy, where the analytical results fit well with the numerical ones on scale-free social networks.

Information Diffusion in Social Networks

2011 ◽  
pp. 146-163 ◽  
Author(s):  
Dmitry Zinoviev
Keyword(s):  
Social Networks ◽  
Information Flow ◽  
Online Social Networks ◽  
Information Diffusion ◽  
Diffusion Processes ◽  
Small World ◽  
The Social ◽  
Strength Of Weak Ties ◽  
Strategic Position

The issue of information diffusion in small-world social networks was first systematically brought to light by Mark Granovetter in his seminal paper “The Strength of Weak Ties” in 1973 and has been an area of active academic studies in the past three decades. This chapter discusses information proliferation mechanisms in massive online social networks (MOSN). In particular, the following aspects of information diffusion processes are addressed: the role and the strategic position of influential spreaders of information; the pathways in the social networks that serve as conduits for communication and information flow; mathematical models describing proliferation processes; short-term and long-term dynamics of information diffusion, and secrecy of information diffusion.

Influence and Information Flow in Online Social Networks

2021 ◽  
pp. 502-520
Author(s):  
Afrand Agah ◽  
Mehran Asadi
Keyword(s):  
Social Networks ◽  
Information Flow ◽  
Online Social Networks ◽  
Large Data ◽  
Marketing Strategies ◽  
Divide And Conquer ◽  
Data Sets ◽  
Divide And Conquer Algorithm ◽  
Flow Of Information

This article introduces a new method to discover the role of influential people in online social networks and presents an algorithm that recognizes influential users to reach a target in the network, in order to provide a strategic advantage for organizations to direct the scope of their digital marketing strategies. Social links among friends play an important role in dictating their behavior in online social networks, these social links determine the flow of information in form of wall posts via shares, likes, re-tweets, mentions, etc., which determines the influence of a node. This article initially identities the correlated nodes in large data sets using customized divide-and-conquer algorithm and then measures the influence of each of these nodes using a linear function. Furthermore, the empirical results show that users who have the highest influence are those whose total number of friends are closer to the total number of friends of each node divided by the total number of nodes in the network.

scholarly journals Social networks for better education and better grades

2020 ◽  
Vol 66 (1) ◽  
pp. 55-65
Author(s):  
Alta Pavin Banović ◽  
Sanja Dravinski
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Medical School ◽  
Information Flow ◽  
Research Method ◽  
Positive Attitude ◽  
Online Survey ◽  
Educational Content ◽  
Teachers And Students ◽  
Use Of Social Media

The goal of the paper is to show the results of a research conducted among students of Medical School Osijek on the use of social media as tools for exchanging educational content with their teachers, which leads to a better adoption of the content and better grades. Teachers and students learn about social media through preventive programs. The research method is an online survey conducted in 10 class departments. Results: Modern social media used in teaching enable better communication between students and teachers, faster information flow and easier preparation of students for school as well as development of a positive attitude on the use of social media in the teaching process.

Network-Based Job Search

2012 ◽  
Vol 41 (4) ◽  
Author(s):  
Gerhard Krug ◽  
Martina Rebien
Keyword(s):  
Social Networks ◽  
Labor Market ◽  
Theoretical Model ◽  
Propensity Score ◽  
Information Flow ◽  
Job Search ◽  
Unobserved Heterogeneity ◽  
Labor Market Outcomes ◽  
Causal Effects ◽  
Market Outcomes

SummaryUsing a search-theoretical model proposed by Montgomery (1992), we analyze the effects of information flow via social networks (friends, relatives, and other personal contacts) on monetary and non-monetary labor market outcomes. Propensity score matching on survey data from low-status unemployed respondents is used to identify causal effects. The analysis takes into account unobserved heterogeneity by applying Rosenbaum bounds. We show that the standard approach to investigating labor market outcomes in terms of how jobs are found is misleading. As an alternative, we propose focusing comparative analyses of labor market outcomes on how individuals search for jobs and, more particularly, on whether they search for jobs via social networks. Using this approach we find no evidence for causal effects on monetary outcomes such as wages and wage satisfaction. We also find no effects for non-monetary outcomes like job satisfaction.

Sign in / Sign up

Export Citation Format

Share Document