A Bibliographic Analysis and Collaboration Patterns of IEEE &lt;sc&gt;Transactions on Intelligent Transportation Systems&lt;/sc&gt; Between 2000 and 2015

Xu, Xiujuan; Wang, Wei; Liu, Yu; Zhao, Xiaowei; Xu, Zhenzhen; Zhou, Haibin

doi:10.1109/tits.2016.2519038

Cited by 22 publications

(7 citation statements)

References 28 publications

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“…The words needed to create a co-word analysis can be collected from the titles of the papers, as in Milojević et al (2011) or from the abstracts and author keywords as in Zhao et al (2018) and Choi et al (2011). These different approaches allow us to identify subareas in each field and study their characteristics and trends (Lee et al 2018), portray the global research profile (Liu et al 2016b;Xu et al 2016), find hot topics (Zhao et al 2018), disruptive trends (Dotsika and Watkins 2017), look for cooperative relationships and interpret collaboration patterns among authors (Newman 2001a;Newman 2001b), etc.…”

Section: Literature On Keyword Network Analysismentioning

confidence: 99%

Complex network analysis of keywords co-occurrence in the recent efficiency analysis literature

et al. 2019

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A network analysis (NA) of keyword co-occurrences for a broad collection of Data envelopment analysis (DEA) papers in the period 2008-2017 is carried out. The raw keywords have been cleaned up and standardized to consolidate and increase the consistency of the keywords. The resulting network has been characterized using network-level as well as node-level NA measures. Although the size of the network steadily increases with time, the average path length does not, showing its small world character. The disassortativity of the network indicates that the keywords used in a given paper generally include one or more common, frequently-used terms plus other less common terms that refer to the specific context of the research. The evolving nature of the keyword network is highlighted with some DEA keywords staying at the top of the ranking during the whole period and other emerging topics significantly increasing their strength during this period. The community structure of the network, which reflects the field's knowledge structure, is also presented. The identified communities generally include specific DEA methodology terms, linked with corresponding application areas as well as with some geographical references. Also, the ego-network of some sample keywords is shown, and some research gaps in DEA are identified.

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Section: Literature On Keyword Network Analysismentioning

confidence: 99%

Complex network analysis of keywords co-occurrence in the recent efficiency analysis literature

et al. 2019

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“…Wang et al [21] investigate the productivity and collaboration patterns in the publications of IEEE Transactions on Intelligent Transportation Systems (TITS) between 2010 and 2013. Xu et al [22] identify the most productive authors, institutions, and countries/regions in IEEE TITS from 2000 to 2015. Three networks including co-authorship network, keyword co-occurrence network and author co-keyword network are generated to analyze the collaboration patterns among authors in the ITS field.…”

Section: B Bibliographic Analysis Workmentioning

confidence: 99%

Progress and Outlook of Visual Tracking: Bibliographic Analysis and Perspective

Liu

Wang

et al. 2019

IEEE Access

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Benefitting from continuous progress in computer architecture and computer vision algorithms, the visual tracking field has earned its rapid development in recent years. This paper surveys this interesting field through bibliographic analysis on the Web-of-Science literature from 1990 to 2019. Specifically, statistical analysis methods are used to obtain the most productive authors and countries/regions, the most cited papers, and so on. In order to realize an in-depth analysis, the co-authors, co-keywords and keywordauthor co-occurrence networks are built to intuitively exhibit the evolution of research hotspots and the collaboration patterns among worldwide researchers. Brief introductions of the topics that occur frequently in co-keywords networks are provided as well. Furthermore, existing challenges and future research directions within the visual tracking field are discussed, revealing that tracking-by-detection and deep learning will continue receiving much attention. In addition, the parallel vision approach should be adopted for training and evaluating visual tracking models in a virtual-real interaction manner. INDEX TERMS Visual tracking, bibliographic analysis, collaboration patterns, research hotspots, parallel vision.

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“…Over the last decade, some library datasets have been used for bibliographic analysis, such as DBLP, IEEE trans, 12 IEEE conferences data, 13 and CNKI. 14 From these datasets, the research topic, co-author, co-citation, 15 and collaboration relationships are mined and visualized onscreen.…”

Section: The Bibliographic Analysismentioning

confidence: 99%

Interactive temporal display through collaboration networks visualization

Ming¹,

Li²,

Zhang

2018

Information Visualization

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Visual analytics play an important role in understanding complex datasets. The bibliographic database is often visualized as a collaboration network to illustrate the connections between researchers. Static networks, however, barely reveal any information when the dataset includes temporal variables. In this article, we propose an embedded network visualization to display the temporal patterns hiding in the data and use intelligent filters to avoid occlusion. We examined different graphing styles, such as the temporal display and the time direction, to find the best way to present the temporal features. In addition, we demonstrate the utility of our approach with case studies and evaluations of real bibliographic databases.

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A Bibliographic Analysis and Collaboration Patterns of IEEE <sc>Transactions on Intelligent Transportation Systems</sc> Between 2000 and 2015

Cited by 22 publications

References 28 publications

Complex network analysis of keywords co-occurrence in the recent efficiency analysis literature

Complex network analysis of keywords co-occurrence in the recent efficiency analysis literature

Progress and Outlook of Visual Tracking: Bibliographic Analysis and Perspective

Interactive temporal display through collaboration networks visualization

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