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Chunjuan Luan and Alan L. Porter

Science (WoS) were renamed and extended to 225 WoS Categories (WCs) (also, a new set of 151 Subject Areas were added, but a higher level of aggregation) ( Leydesdorff, Carley, & Rafols, 2013 ). Thus, we use WCs to detect the disciplinary structure of N&N, further conducting a comparison with conclusions of Porter and Youtie (2009) with the predecessor SCs. Besides analysis from the perspective of the social network analysis of the disciplinary structure of N&N, cluster analysis by employing cliques embedded in Ucinet software has also been conducted in this paper

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Stanisław Saganowski, Piotr Bródka and Przemysław Kazienko

., Kazienko P.: GED: The Method for Group Evolution Discovery in Social Networks, Social Network Analysis and Mining , 2012, DOI:10.1007/s13278-012-0058-8 [4] Chakrabarti D, Kumar R, Tomkins A, Evolutionary Clustering , KDD 2006, ACM, Philadelphia. [5] Lin, Y.R., Chi, Y., Zhu, S., Sundaram, H., Tseng, B.L. FacetNet: A Framework for Analyzing Communities and Their Evolutions in Dynamic Networks , WWW 2008, ACM, 2008, pp. 685-694. [6] Musiał K.., Kazienko P., Bródka P.: User position measures in social networks. In

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Carlos Luis González-Valiente, Magda León Santos and Ricardo Arencibia-Jorge

-known software for science mapping, was employed for the network construction and visualizations of first authors only. This software groups the nodes as networks derived from co-citation measures using the visualization of similarities (VOS) method ( Waltman, van Eck, & Noyons, 2010 ). To interpret the clusters, we examined scholarly output of co-cited authors. Given that the research interests of authors vary in time, we took into consideration their scientific contributions limited to each of the periods analysed. Some statistical values derived from social network analysis

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Hua Guan, Zhen Zhao and Lu Dai

References 1. Litvak, N., W. R. W. Scheinhardt, Y. V. Volkovich. Probabilistic Relation between In-Degree and PageRank. - In: Proc. of 4th International Workshop WAW’2006, 2006, Banff, Canada, pp. 72-83. 2. Liu, L. G., Z. G. Xuan, Z. Y. Dang, Q. Guo, Z. T. Wang. Weighted Network Properties of Chinese Nature Science Basic Research. - Physica A-Statistical Mechanics and Its Applications, Vol. 377, 2011, No 1, pp. 302-314. 3. Ma, N., J. Guan, Y. Zhao. Bringing PageRank to the Citation Analysis. - Information

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Li Zhao Xing, He Li Le and Zhang Hui

References 1. Wasserman, S. Social Network Analysis: Methods and Applications. Cambridge University Press, 1994. 2. Newman, M. Networks: An Introduction. Oxford University Press, 2010. 3. Wang, X., X. Li, G. Chen. Complex Network Theory and Its Application. Tsinghua University Press, 2006. 4. Girvan, M., M. E. J. Newman. Community Structure in Social and Biological Networks. - Proceedings of the National Academy of Sciences, Vol. 99, 2002, No 12, pp. 7821-7826. 5. Fortunato, S

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Jon Garner, Alan L. Porter, Andreas Leidolf and Michelle Baker

. (2011). Exploratory social network analysis with Pajek (2 nd Edition). New York, NY: Cambridge University Press. de Nooy W. Mrvar A. Batgelj V. 2011 Exploratory social network analysis with Pajek 2 nd New York, NY Cambridge University Press Garner, J., Porter, A.L., Borrego, M., Tran, E., & Teutonico, R. (2013). Facilitating social and natural science cross-disciplinarity: Assessing the human and social dynamics program, Research Evaluation, 22(2), 134–144. Garner J. Porter A.L. Borrego M. Tran E. Teutonico R. 2013 Facilitating social and natural science cross

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Journal of Applied Computer Science Methods

The Journal of University of Social Science in Lodz

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Aleksejs Cumiks and Andrejs Romanovs

References [1] V. Levorato, Modeling groups in social networks, 25th European Conference on Modelling and Simulation, Krakow, Poland. 2011, pp. 133-180. [2] I. Herman, G. Melancon, M.S. Marshall, Graph visualization and navigation in information visualization: a survey. IEEE Transactions on Visualization and Computer Graphics, Volume 6, Issue 1, 2000, pp 24-43. [3] M. Kaufmann, D. Wagner, Drawing Graphs: Methods and Models. Lecture Notes in Computer Science. Springer, Volume 2025, 2001

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R. Deeptha and Rajeswari Mukesh

Analysis and Security Guidelines. – In: Proc. of 30th IEEE Computer Security Foundations Symposium, 2017. 7. Mukhamedov, Aybek. Full Agreement in BAN Kerberos. – In: Proc. of IEEE Workshop of the 1st International Conference on Security and Privacy for Emerging Areas in Communication Networks, 2005, pp. 218-223. 8. Abdelmajid, N. T., et al. Location-Based Kerberos Authentication Protocol. – In: Proc. of 2nd IEEE International Conference on Social Computing, 2010, pp. 1099-1104. 9. Shaw, J. Enterprise Single Sign-on: The Holy Grail of Computing. 2009

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Katalin Tünde Jánosi-Rancz, Zoltán Kátai and Roland Bogosi

References [1] S. Baccianella, A. Esuli, F. Sebastiani, SentiWordNet 3.0: An enhanced lexical resource for sentiment analysis and opinion mining, LREC 2010, Seventh International Conference on Language Resources and Evaluation , Valetta, Malta, May 17–23, 2010, pp. 2200–2204. ⇒188, 189, 198 [2] V. Basile, M. Nissim, Sentiment analysis on Italian tweets, Proc. 4th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis , June 14, 2013, Atlanta, Georgia, USA, pp. 100–107. ⇒198 [3] G. Berend, R. Farkas, Opinion