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@article{Xu2021,

title = {Exploring the Interdisciplinary Nature of Precision Medicine：Network Analysis and Visualization},

author = {Xin Xu and Jiming Hu and Xiaoguang Lyu and Huang He and Cheng Xingyu },

doi = {10.2196/23562 },

year  = {2021},

date = {2021-01-11},

urldate = {2021-01-11},

journal = {JMIR Medical Informatics},

abstract = {The aim of this study is to present the nature of interdisciplinary collaboration in precision medicine based on co-occurrences and social network analysis. A total of 7544 studies about precision medicine, published between 2010 and 2019, were collected from the Web of Science database. We analyzed interdisciplinarity with descriptive statistics, co-occurrence analysis, and social network analysis. An evolutionary graph and strategic diagram were created to clarify the development of streams and trends in disciplinary communities. The results indicate that 105 disciplines are involved in precision medicine research and cover a wide range. However, the disciplinary distribution is unbalanced. Current cross-disciplinary collaboration in precision medicine mainly focuses on clinical application and technology-associated disciplines. The characteristics of the disciplinary collaboration network are as follows: (1) disciplinary cooperation in precision medicine is not mature or centralized; (2) the leading disciplines are absent; (3) the pattern of disciplinary cooperation is mostly indirect rather than direct. There are 7 interdisciplinary communities in the precision medicine collaboration network; however, their positions in the network differ. Community 4, with disciplines such as genetics and heredity in the core position, is the most central and cooperative discipline in the interdisciplinary network. This indicates that Community 4 represents a relatively mature direction in interdisciplinary cooperation in precision medicine. Finally, according to the evolution graph, we clearly present the development streams of disciplinary collaborations in precision medicine. We describe the scale and the time frame for development trends and distributions in detail. Importantly, we use evolution graphs to accurately estimate the developmental trend of precision medicine, such as biological big data processing, molecular imaging, and widespread clinical applications.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Deng2020,

title = {Exploring the topic structure and evolution of associations in information behavior research through co-word analysis},

author = {Shengli Deng and Sudi Xia and Jiming Hu and Hongxiu Li and Yong Liu },

doi = {https://doi.org/10.1177/0961000620938120},

year  = {2020},

date = {2020-07-01},

urldate = {2020-07-01},

journal = {Journal of Librarianship and Information Science},

abstract = {This study aims to reveal the distribution of topics, and the associations among them, in information behavior research from 2009 to 2018. Working with a collection of 6744 publications from the Web of Science database, co-word analysis is used to investigate the overall topic structure, the associations among the topics, and their evolution in different years, which is supplemented by visualization with science maps. The results uncovered an unbalanced distribution of topics, and that the topics cluster into six communities representing subdivisions of this field: information behavior in patient-centered studies; information interaction in the digital environment; information literacy in health and academic contexts; health literacy on the Internet; information behavior in child-centered studies; and information behavior in medical informatics. The findings supplement and provide refinements to work on the state of this field, and help researchers obtain an overview of the past decade to guide their future work.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Wei2020,

title = {Analyzing the topic distribution and evolution of foreign relations from parliamentary debates: A framework and case study},

author = {Wei Lu and Jiamin Wang and Jiming Hu},

doi = {https://doi.org/10.1016/j.ipm.2019.102191},

year  = {2020},

date = {2020-05-01},

urldate = {2020-05-01},

journal = {Information Processing & Management},

volume = {55},

number = {3},

abstract = {Parliamentary texts are records of discussions of domestic and international affairs, which reflect national attitudes and development trends in foreign relations. In this paper, a research framework is proposed to analyze foreign relations on the basis of parliamentary texts. First, topic words are extracted from parliamentary texts, and then a co-word network is constructed to represent the correlation structure of topic words. The basic statistics, calculation of network indicators, community detection, and visualization of network maps and evolution venation, as well as the depiction of a strategic diagram, elucidate deeper characteristics and connotations of foreign relations. This case study on UK-China relations during the period of 2011-2017 using British parliamentary texts reveals the following findings. Over this period, UK-China relations changed in terms of the topics involved, topics which are greatly unbalanced in distribution, but are quite concentrated. Five different directions exist, centering on Trade, Human rights, Nuclear, Steel, and Visa. The evolution of topics includes merging and differentiation. A minority of topics exhibit marked continuity, which constitute the main focal points discussed each year, such as Economy and Trade. Regarding development trends, themes related to trade and steel remain focal points in UK-China relations. Overall, the framework proposed in this paper is proven to be both effective and feasible, and its application through this case study can foster a deeper understanding of the status and development of UK-China relations.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Lyu2020,

title = {Intellectual Structure and Evolutionary Trends of Precision Medicine Research: Coword Analysis},

author = {Xiaoguang Lyu and Jiming Hu and Weiguo Dong and Xin Xu},

url = {https://medinform.jmir.org/2020/2/e11287},

doi = {10.2196/11287},

issn = {2291-9694},

year  = {2020},

date = {2020-02-04},

urldate = {2020-02-04},

journal = {JMIR Med Inform},

volume = {8},

number = {2},

pages = {e11287},

abstract = {Background: Precision medicine (PM) is playing a more and more important role in clinical practice. In recent years, the scale of PM research has been growing rapidly. Many reviews have been published to facilitate a better understanding of the status of PM research. However, there is still a lack of research on the intellectual structure in terms of topics. Objective: This study aimed to identify the intellectual structure and evolutionary trends of PM research through the application of various social network analysis and visualization methods. Methods: The bibliographies of papers published between 2009 and 2018 were extracted from the Web of Science database. Based on the statistics of keywords in the papers, a coword network was generated and used to calculate network indicators of both the entire network and local networks. Communities were then detected to identify subdirections of PM research. Topological maps of networks, including networks between communities and within each community, were drawn to reveal the correlation structure. An evolutionary graph and a strategic graph were finally produced to reveal research venation and trends in discipline communities. Results: The results showed that PM research involves extensive themes and, overall, is not balanced. A minority of themes with a high frequency and network indicators, such as Biomarkers, Genomics, Cancer, Therapy, Genetics, Drug, Target Therapy, Pharmacogenomics, Pharmacogenetics, and Molecular, can be considered the core areas of PM research. However, there were five balanced theme directions with distinguished status and tendencies: Cancer, Biomarkers, Genomics, Drug, and Therapy. These were shown to be the main branches that were both focused and well developed. Therapy, though, was shown to be isolated and undeveloped. Conclusions: The hotspots, structures, evolutions, and development trends of PM research in the past ten years were revealed using social network analysis and visualization. In general, PM research is unbalanced, but its subdirections are balanced. The clear evolutionary and developmental trend indicates that PM research has matured in recent years. The implications of this study involving PM research will provide reasonable and effective support for researchers, funders, policymakers, and clinicians.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Zhou2019,

title = {Intellectual structure and evolution patterns of archival information resource research in China},

author = { Yaolin Zhou and Jingqiong Sun and Jiming Hu},

doi = {https://doi.org/10.1108/LHT-08-2018-0101},

year  = {2019},

date = {2019-06-01},

urldate = {2019-06-01},

journal = {Library Hi Tech},

volume = {37},

number = {2},

pages = {233-250},

abstract = {Purpose The purpose of this paper is to identify the leading topics and developmental trends of archival information resource research in China by visualizing the intellectual structure and evolution patterns of archival information resource research. Design/methodology/approach This study took China National Knowledge Infrastructure (CNKI) as the data source and extracted keywords from relevant articles in archival information resource research as the sample. First, the frequency and co-occurrence of keywords were calculated by using SCI2. Second, this study analyzed the co-word network indicators by using Pajek. Then, topic community detection was conducted by using a VOS viewer, as well as the visualization of intellectual structures. Next, this study developed a graphical mapping of the evolution of research topics over time by using Cortext. Findings The research topics of archival information resources in China were unbalanced but distinct. Researchers focus on the construction and utilization of archival information resource, which consist of five evident research directions. The phenomena of fusion and differentiation coexist in research topic evolution. There were both continuities of traditional research and innovations in emerging research. The archival information resource research tended to be systematized and extended, reflecting the vertical and horizontal extension of the research content. Originality/value Based on a large number of previous studies, this study adopted quantitative methods to reveal the intellectual structure and evolution patterns of archival information resource research in China, providing guidance for researchers and institutions to grasp research status and developmental trends.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Hu2017,

title = {Discovering the interdisciplinary nature of Big Data research through social network analysis and visualization},

author = {Jiming Hu and Yin Zhang},

doi = {10.1007/s11192-017-2383-1},

year  = {2017},

date = {2017-05-01},

urldate = {2017-05-01},

journal = {Scientometrics},

volume = {112},

pages = {91–109},

abstract = {Big Data is a research field involving a large number of collaborating disciplines. Based on bibliometric data downloaded from the Web of Science, this study applies various social network analysis and visualization tools to examine the structure and patterns of interdisciplinary collaborations, as well as the recently evolving overall pattern. This study presents the descriptive statistics of disciplines involved in publishing Big Data research; and network indicators of the interdisciplinary collaborations among disciplines, interdisciplinary communities, interdisciplinary networks, and changes in discipline communities over time. The findings indicate that the scope of disciplines involved in Big Data research is broad, but that the disciplinary distribution is unbalanced. The overall collaboration among disciplines tends to be concentrated in several key fields. According to the network indicators, Computer Science, Engineering, and Business and Economics are the most important contributors to Big Data research, given their position and role in the research collaboration network. Centering around a few important disciplines, all fields related to Big Data research are aggregated into communities, suggesting some related research areas, and directions for Big Data research. An ever-changing roster of related disciplines provides support, as illustrated by the evolving graph of communities.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}