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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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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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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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Chaocheng He, Panhao Ma, Lusha Zhou and Jiang Wu

within discussion forums. Similarly, Kellogg et al. (2014) studied the social networks in the forum. Anderson et al. (2014) investigated how forum participation relates to other parts of the course, where they found that badges can serve as incentives for engagement in a MOOC, including its forum. 3 Analysis on the impact of Forum Participation on Course Performance Chinese College MOOC is one of the biggest MOOC platforms in China. Like other MOOC platforms, it provides instructional videos, exercises, tests, and interactive forums for students. In this

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Ling Wei, Haiyun Xu, Zhenmeng Wang, Kun Dong, Chao Wang and Shu Fang

-occurrence networks: After data preprocessing, the top 300 high-frequency keywords are selected to generate a co-occurrence matrix and co-occurrence network using the social network analysis tools Ucinet and Gephi. When separating clusters, the Louvain community detection algorithm embedded in Gephi is applied, and the default value 1.0 is taken as the threshold; Extraction of weak tie co-occurrence network: The high-frequency keyword co-occurrence network is filtered to a weak tie co-occurrence network on the premise that, the weak tie co-occurrence network should keep the basic

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Haiyun Xu, Chao Wang, Kun Dong and Zenghui Yue

) mined the cross-research topics between Library and Information Science (LIS) and computer science using a complex network discovery tool named CFinder, which made an analysis of the cross-research topics by a visual display of clustering and overlapping social networks between these two disciplines. Zhang et al. (2011) undertook empirical research on interdisciplinarity by analyzing ten years of literatures from the domestic core journals of LIS and computer science, constructed the network of both authors and literatures based on citation relationships, and

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Raf Guns

Physical Review E 64 1 016132 Opsahl, T., Agneessens, F., & Skvoretz, J. (2010). Node centrality in weighted networks: Generalizing degree and shortest paths. Social Networks, 32(3), 245–251. 10.1016/j.socnet.2010.03.006 Opsahl T. Agneessens F. Skvoretz J. 2010 Node centrality in weighted networks: Generalizing degree and shortest paths Social Networks 32 3 245 251 Otte, E., & Rousseau, R. (2002). Social network analysis: A powerful strategy, also for the information sciences. Journal of Information Science, 28(6), 441–453. 10

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to use available data.” Yet, in all practical applications, e.g. when dealing with data related to research evaluation, and derived metrics, these data must be interpreted by experts. Colleagues from the Centre for Science and Technology Studies (CWTS) (Leiden, the Netherlands) presented VOSViewer and CitNetExplorer, two software tools developed for network and data analysis. An example of the use of these tools in the study of the biomarker HER2 was provided by another participant. A presentation on research fronts included the essential question: What is a

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Wenjing Ruan, Haiyan Hou and Zhigang Hu

research field. To detect hot topics based on scientific articles, researchers have employed various approaches such as word frequency analysis, co-word analysis, co-citation analysis, and social network analysis. Word frequency analysis is a basic method of hot topic detection and often combined with other methods. For example, Gong and Ye (2006) analyzed research hotspots using index terms that captured the essence of a topic because they believed that keywords with a high frequency represented academic hotspots in a given field. Xiao (2011) employed the h