@article{wang_krim_viniotis_2014, title={Analysis and Control of Beliefs in Social Networks}, volume={62}, ISSN={["1941-0476"]}, DOI={10.1109/tsp.2014.2352591}, abstractNote={In this paper, we investigate the problem of how beliefs diffuse among members of social networks. We propose an information flow model (IFM) of belief that captures how interactions among members affect the diffusion and eventual convergence of a belief. The IFM model includes a generalized Markov Graph (GMG) model as a social network model, which reveals that the diffusion of beliefs depends heavily on two characteristics of the social network characteristics, namely degree centralities and clustering coefficients. We apply the IFM to both converged belief estimation and belief control strategy optimization. The model is compared with an IFM including the Barabási-Albert model, and is evaluated via experiments with published real social network data.}, number={21}, journal={IEEE TRANSACTIONS ON SIGNAL PROCESSING}, author={Wang, Tian and Krim, Hamid and Viniotis, Yannis}, year={2014}, month={Nov}, pages={5552–5564} }
 @article{wang_krim_viniotis_2013, title={A Generalized Markov Graph Model: Application to Social Network Analysis}, volume={7}, ISSN={["1941-0484"]}, DOI={10.1109/jstsp.2013.2246767}, abstractNote={In this paper we propose a generalized Markov Graph model for social networks and evaluate its application in social network synthesis, and in social network classification. The model reveals 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. The application of this model to social network synthesis leads to a capacity to generate networks dominated by the degree distribution and the clustering coefficient distribution. Another application is a new social network classification method based on comparing the statistics of their degree distributions and clustering coefficient distributions as well as their crowding coefficient distributions. In contrast to the widely held belief that a social network graph is solely defined by its degree distribution, the novelty of this paper consists in establishing the strong dependence of social networks on the degree distribution, the clustering coefficient distribution and the crowding coefficient distribution, and in demonstrating that they form minimal information to classify social networks as well as to design a new social network synthesis tool. We provide numerous experiments with published data and demonstrate very good performance on both counts.}, number={2}, journal={IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING}, author={Wang, Tian and Krim, Hamid and Viniotis, Yannis}, year={2013}, month={Apr}, pages={318–332} }
 @inproceedings{wang_krim_2012, title={Statistical classification of social networks}, DOI={10.1109/icassp.2012.6288789}, abstractNote={This paper proposes a new social network classification method by comparing statistics of their centralities and clustering coefficients. Specifically, the proposed method uses the statistics of Degree Centralities and clustering coefficients of networks as a classification criterion. A theoretical justification to this method is also given. In relation to the widely held belief that a social network graph is solely defined by its degree distribution, the novelty of this paper consists in revealing the strong dependence of social networks on Degree Centralities and clustering coefficients, and in using them as minimal information to classify social networks. In addition, 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.}, booktitle={2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, author={Wang, T. and Krim, H.}, year={2012}, pages={3977–3980} }