Tian Wang

Works (3)

Updated: July 5th, 2023 15:44

2014 journal article

Analysis and Control of Beliefs in Social Networks

IEEE TRANSACTIONS ON SIGNAL PROCESSING, 62(21), 5552–5564.

By: T. Wang n, H. Krim n & Y. Viniotis n

author keywords: Complex networks; information flow; machine learning
TL;DR: An information flow model (IFM) of belief that captures how interactions among members affect the diffusion and eventual convergence of a belief is proposed and applied to both converged belief estimation and belief control strategy optimization. (via Semantic Scholar)
Source: Web Of Science
Added: August 6, 2018

2013 journal article

A Generalized Markov Graph Model: Application to Social Network Analysis

IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING, 7(2), 318–332.

By: T. Wang n, H. Krim n & Y. Viniotis n

author keywords: Complex networks; pattern recognition; classification; Markov graph model
TL;DR: A generalized Markov Graph model for social networks is proposed and it is revealed that the degree distribution, the clustering coefficient distribution as well as a newly discovered feature, a crowding coefficient distribution, are fundamental to characterizing a social network. (via Semantic Scholar)
UN Sustainable Development Goal Categories
10. Reduced Inequalities (OpenAlex)
Source: Web Of Science
Added: August 6, 2018

2012 conference paper

Statistical classification of social networks

2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 3977–3980.

By: T. Wang n & H. Krim  n

TL;DR: Experimental classification demonstrates a very good performance of the proposed method on real social network data, and validates the hypothesis that Degree Centralities and clustering coefficients are the only two viable independent properties of a social network. (via Semantic Scholar)
UN Sustainable Development Goal Categories
10. Reduced Inequalities (OpenAlex)
Source: NC State University Libraries
Added: August 6, 2018

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