生物医学领域科研合作现状及预测研究
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摘要
研究目的
本研究旨在采用理论联系实际、定性和定量相结合的方法，将知识网络背景下的科学计量学理论应用于生物医学研究评价，系统梳理传统的科研合作理论体系，并试图开辟科研合作理论的新领域，即科研合作关系预测研究，最后通过实证研究进一步证实。力求使各级科研管理工作者和科研人员更好地理解国家科研创新体系，认识科研合作的重要性和必要性，为我国科研管理决策提供参考依据。
研究对象
本研究选取死亡原因第一位的心血管病研究领域、总体死亡率居高不下的肿瘤研究领域以及作为现代医学五大创新体系之一的健康管理研究领域作为研究对象。
数据来源
1．心血管病研究领域数据
国内数据：选取《中文核心期刊要目总览》（2008版）涵盖的心血管病研究领域全部5种核心期刊，从中国知网和重庆维普数据库获取2000-2010年期间上述期刊发表所有论文的题录信息。
国际数据：a、从Web of Science（WoS）的Science Citation Index-expanded（SCI-E）中获取1981-2010年期间所有主题词为“coronary”的文献的题录信息；b、选取期刊引证报告“Cardiac&Cardiovascular System”学科中影响因子大于10的4种期刊，从SCI-E中获取2001-2010年上述期刊发表所有论文的题录信息。
2．肿瘤研究领域数据
国内数据：选取中国科学引文数据库涵盖的肿瘤研究领域的全部10种核心期刊，从中国知网和重庆维普数据库获取2000-2009年上述期刊发表所有论文的题录信息。
国际数据：选取期刊引证报告中“Oncology”学科内影响因子和被引频次均处前10%的30种期刊，从WoS中的SCI-E、CPCI-SSH数据库和生物医学领域PubMed数据库获取2001-2010年上述期刊发表所有论文的题录信息。
3．健康管理研究领域数据
国内数据：从万方数据知识服务平台、维普网以及《中华健康管理学杂志》纸质及光盘版，获取2007-2012年期间《中华健康管理学杂志》发表的所有论文的题录信息。
国际数据：从WoS中获取1999-2011年期间主题为“health management”的所有论文的题录信息。
研究方法
1．文献分析法
通过纸质文献和电子期刊文献检索已发表的相关文献，获取原始资料并进行相关分析。
2．科学计量学方法
利用合著分析研究生物医学领域合作现象，利用共词分析分析领域研究热点。
3．社会网络分析
使用Pajek、Ucinet和CiteSpace II等社会网络分析软件实现Component分析、Centrality分析、K-core分析和M-slice分析、Clique分析等，并绘制网络图。
4．统计方法
主要包括曼-惠特尼U检验、Logistic回归、序次Logistic回归分析、聚类分析和多维尺度分析。
5．机器学习
利用LPmade软件计算合著网络中作者对之间的拓扑特征，运用WEKA软件实现监督式机器学习Logstic Regression（LR）模型和Support Vector Machine（SVM）模型。
6．计算机编程和数据库
利用Java和Python编程语言实现原始数据向矩阵数据的转换、各项指标的计算，利用MySQL数据库管理原始数据。
7．实证研究方法
采用理论研究与实证研究相结合的方法，分析生物医学领域科研合作情况。
研究结果
1．生物医学领域科研合作现状分析
（1）心血管病研究领域
国内：2000-2010年期间，我国心血管病研究领域的合著率和篇平均作者数总体上呈增长趋势；科研合作研究的区域分布极不平衡；在该领域的合著网络中，三种中心度排名均在前1%的作者共87位、机构共5所；10-core及以下合著网络中的作者占总数的92.8%；3-slice以下的合著网络中的作者占总数的90.93%；M-slice分析发现了63个合作密切的科研团队；冠状动脉疾病、心肌梗死等为合作研究的热点。
国际：1981-2010年期间，国际冠心病研究领域作者、机构和国家层面的科研合作都处于增长趋势；从作者合作网络、机构合作网络和国家合作网络中分别发现了3000名合作最为密切的作者、572个合作最为密切的机构以及52个合作最为密切的国家；合作最为密切的作者形成了766个Clique，合作最为密切的机构形成了308个Clique；西方国家之间的合作最为密切，并处于国际合作网络的核心，而东方国家/区域则处于网络的边缘位置；国家经济发展水平影响着国际合作行为。
（2）肿瘤研究领域
国内：2000-2009年期间，我国肿瘤研究领域的科研合作主要集中于北京、上海、广东等东部地区；作者的三种中心度排名与其生产能力成正比；合著网络的最大Component包含的作者占总数的58.10%；8-core及以下合著网络中的作者占总数的90%；3-slice及以下的合著网络中的作者占总数的92.25%；11-slice及以上的合著网络包含480位作者（约占全部作者人数的1%）；通过聚类分析发现了该领域12个合作密切的科研团队；通过多维尺度分析发现了该领域6个合作密切的科研团队。
国外：2001-2010年期间，国际肿瘤研究领域最具代表性的作者合著网络形成36个较为密集的凝聚子群；产量最高的作者为Nakamura Y，发表论文117篇；最大的学术共同体由8位作者组成，主要来自哈佛大学医学院Dana Farber癌症研究所和波士顿中心的杰罗姆理柏多发性骨髓瘤研究中心，第二大聚类由5位作者组成，大都来自皮奥里亚大学、伊利诺伊大学医学院神经外科部，第三大聚类由4位作者组成，来自美国德州大学MD安德森癌症中心；36个大型合作网络群体的研究主要集中在多发性骨髓瘤、血管生成和急性淋巴细胞白血病等前沿领域。
（3）健康管理研究领域
国内：2007-2012年期间，《中华健康管理学杂志》期刊中共有1933位作者和625所机构参与了657篇论文的创作工作，平均约每3位作者发表1篇论文；发文量不小于4篇或中心度大于0的作者共有54位，其中学者黄建始、武留信和曾强的发文量及中心度都位于前列，并与白书忠、田京发、韩静等学者形成了影响力较为广泛的合作区域网络，王培玉、杜兵等学者也组成了各自的合作团队；发文量较高的作者多集中于解放军空军航空医学研究所、北京大学医学部、北京市体检中心、中国医学科学院等机构。
国际：1999-2011年期间，国际健康管理领域的研究无论在科研产量，还是在作者、机构和国家层面的科研合作程度都呈上升趋势；通过中心度指标遴选出以O'Toole T1为代表的17名高影响力作者和以明尼苏达大学为代表的37所高影响力研究机构；通过Component分析在作者合著网络中发现了22个具有固定合作关系的科研团队，从机构合著网络中发现了8个具有固定合作关系的机构合作团队；美国、英国、澳大利亚等国处于国际研究合作的中心位置；高频关键词中的Care、Disease、System和Model均与健康管理研究密切相关。
2．生物医学领域科研绩效与国际合作关系研究
科研人员科研绩效与国际合作的关系：心血管病研究领域国际合作论文的被引频次最高，机构间合作论文的被引频次次之，机构内合作论文的被引频次最低；科研人员的科研产量和质量在决定其国际合作度时具有正作用；科研人员的国际合作率与其科研产量和质量之间也存在正依赖性。
国家科研绩效与国际合作关系：国际冠心病研究领域中，高产国家为美国、日本、德国和英国等；高影响力国家为美国、英国和德国等；而国际合作程度最高的国家为匈牙利、瑞士、丹麦、奥地利、芬兰和挪威等。
3．生物医学领域科研合作关系预测研究
国际冠心病研究领域科研合作预测结果显示，LR模型和SVM模型在四个准确度指标上的得分都很高，其中SVM模型在Precision率、Recall率和AUC三个指标上优于LR模型；两个模型对于产量高的作者集预测结果的准确性较高，而对于产量低的作者集预测结果的准确性较低；通过选取的最优特征值对上述两个模型检验发现，二模型的测试结果均有一定的提高，且SVM模型优于LR模型；各个拓扑特征单独预测的测试结果低于综合各个拓扑特征测试的结果。
研究结论
1．生物医学领域的科研合作分布不均衡
虽然我国心血管病研究领域中科研人员的合作趋势、国际冠心病研究领域各层面的合作趋势在不断加强，但是我国心血管病研究领域科研合作的区域合作分布却极不平衡，东部地区科研合作较为密切，且区域内部的合作远远大于区域之间的合作。同时，西方发达国家在冠心病研究领域的国际合作活动中处于核心地位。此外，高收入国家的首选合作对象为高收入国家，其次才是中等
收入国家和低收入国家。2．中心度指标可用于遴选生物医学领域学科带头人
Degree中心度、Closeness中心度和Betweenness中心度可作为衡量作者产量的指标，帮助科研管理部门遴选生物医学领域的学科带头人。
3．凝聚子群可用于发现生物医学领域的优秀科研合作团队
凝聚子群分析，如Compnent分析、K-core分析、M-slice分析和Clique分析等，可以帮助科研管理部门发现生物医学领域的优秀科研合作团队。
4．科研人员的科研绩效与国际科研合作水平存在正相关性
论文质量受科研人员科研合作水平的影响；科研人员的科研产量和质量与其国际科研合作程度之间存在显著的正相关性。
5．利用合著网络中的拓扑特征进行科研合作关系的预测具有可行性
传统的LR模型与新兴的SVM模型在预测合作关系时性能都很好；高产作者之间的合作关系比低产作者之间的合作关系更容易预测；通过拓扑特征的选取可使模型的准确率得以提升；应当综合多个拓扑特征进行链接预测，避免仅选择单一的拓扑特征进行预测。
对策建议
1．通过加强科研合作提高科研人员的科研绩效
鼓励生物医学领域的国际科研合作可以提高该领域的科研产量和质量。而研究结果显示，虽然生物医学领域科研合作程度呈现上升趋势，但是科研合作分布却极不均衡。因此，应当一方面鼓励我国各区域之间进行科研合作研究，另一方面鼓励与发达国家之间进行国际合作研究，促进生物医学科研水平协调发展。
2．社会网络分析法应有效用于学术网络的分析
本研究将社会网络分析法应用于大型合著网络的分析，发现其中心性指标可以帮助科研管理部门遴选学科带头人和科研团队，而凝聚子群方法可以帮助科研管理部门挖掘优秀科研创新团队，再一次证明了社会网络分析在科学计量学领域的适用性和可行性。
3．数据挖掘技术应作为科学计量学方法的有益补充
本研究对数据挖掘技术在科学计量学中的应用进行了初步尝试，通过监督式机器学习方法，利用合著网络中的拓扑结构特征建立链接预测模型，发现这些模型可以用于预测潜在合作关系，从而帮助组建和管理强大的科研团队，为科研管理工作和科技政策制定提供参考和指导。
主要创新点
1．开辟科研合作理论研究的新领域——科研合作预测研究
将科研合作的预测研究与传统的科研合作理论研究结合，形成完整的科研合作理论体系。科研合作关系预测问题的解决，可为组建和管理强大的科研创新团队提供帮助，并大大提高科研部门的工作效率。
2．实现了数据挖掘、机器学习、社会网络分析、多元统计以及计算机编程技术等多种方法在计量学研究领域的结合和合理应用
在研究方法方面引入了数据挖掘技术、机器学习、社会网络分析、多元统计等多种方法，大大拓展了科学计量学的方法体系。
利用计算机编程技术开发各种应用软件，用于CSV、TSV和XML文件向关系数据库格式的转化、数据关系矩阵的构建以及拓扑特征数据向机器学习软件所需格式的转化，实现数据处理完全自动化，提高研究效率以及结果的准确性。
3．高影响力个人和机构遴选结果和科研合作关系预测结果可为我国科研管理的科学化和政策制定提供决策支持
本研究证明了社会网络分析中心度指标与科研人员科研产量之间的高度相关性，验证了利用社会网络分析凝聚子群方法发现的具有稳定合作关系的科研团队在其所属领域的优秀科研绩效；证实了利用合著网络中的拓扑特征进行科研合作关系预测的可行性。这些成果有助于我国科研管理的科学化，并为我国科研管理政策的制定提供决策支持。
不足与展望
1．异构学术网络中的科研合作关系预测研究
在同构网络（合著网络）的科研合作预测研究基础上进行异构学术网络的链接预测问题，异构网络中的节点类型可以为多种（如作者和论文），链接类型也可以为多种（如撰写/被撰写、引用/被引用）。由于异构网络能够提供作者更多的拓扑结构特征，因此有可能会产生比同构网络更好的链接预测效果。
2．主题模型研究与作者合著网络分析的结合
下一步可以将目前常用的主题模型LDA应用于合著网络，从而摆脱仅从“人”的层面分析科研合作的困境，将科研人员的“研究主题”融入到合作关系的分析中。
本文系国家自然科学基金“基于知识网络的科技创新团队成员合作研究--以生物医学为例（批准号：71103114）”和国家自然科学基金“论文合著和专利合作视角的生物医学领域科技合作研究（批准号：71240006）”的组成部分和研究成果之一。
Purpose
By combining theories with empirical studies, as well as qualitative methods withquantitative methods, we aimed to apply scientometrics theories to the evaluation ofthe research collaboration in biomedical fields in the background of knowledgenetwork. We sorted out the traditional research collaboration theory system and madeattempts to establish a new one–the theory of collaboration relationship prediction.Then we confirmed the validity of this system through an empirical study. Theseefforts will help the research management departments of various levels andresearchers have a better understanding of the structure of China’s innovation systemand get a right picture of the importance and necessity of research collaboration. Webelieve this study will provide scientific evidences and suggestions for researchmanagement policymaking.
Research subjects
Three subfields of biomedicine were chosen for this study: Cardiology&Cardiovasology, Oncology, Health Management.
Data
1. Data in the fields of Cardiology&Cardiovasology (C&C)
(1) Chinese data
Five major journals in C&C field were chosen from ‘A Guide to the CoreJournals of China (2008Edition)’. Then, the bibliographic records published in these 5journals from the year2000through the year2010were collected from databases inChina National Knowledge Infrastructure (CNKI) and VIP Journal IntegrationPlatform (VIP).
(2) International data
a. All the bibliographic records containing ‘‘coronary’ in their title, abstract orkeywords from the year1981through the year2010were collected from ScienceCitation Index-expanded (SCI-E) in Web of Science (WoS).
b. Top four journals with5-year Journal Impact Factor greater than10werechosen from category “Cardiac&Cardiovascular System” in Journal Citation Report.Then, all the bibliographic records published in these journals from the year2001tothe year2010were collected from SCI-E.
2. Data in the fields of oncology
(1) Chinese data
We chose all the10Core Oncology journals from Chinese Science CitationDatabase. Then, all the bibliographic records published in these10journals from theyear2000through the year2009were collected from databases in CNKI and VIP.
(2) International data
Top10%(30) journals in terms of both Impact Factor and Citations were chosenfrom category “Oncology” in Journal Citation Report. Then, all the bibliographicrecords in these journals from the2001to the2010were collected from SCI-E,CPCI-SSH of Web of Science database.
3. Data in the fields of health management
(1) Chinese data
All the bibliographic records in “Chinese Journal of Health Management” from2007-2012were collected from VJIP, Wangfang Data and CD-ROM version ofChinese Journal of Health Management.
(2) International data
All the bibliographic records with the keyword “health management” from theyear1999to the year2011were collected from WoS.Methods
1. Literature analysis.
2. Scientomerics: Co-authorship and Co-word analysis.
3. Social network analysis: Component analysis, Centrality analysis, K-coreanalysis, M-slice analysis and Clique analysis.
4. Statistical methods: Mann–Whitney U test, Binary logistic regression, Orderedlogistic regression, Hierarchical Clustering and Multidimensional Scaling.
5. Machine learning: Logistic regression (LR), Support Vector Machine (SVM),Lpmade and WEKA.
6. Programming language: Java and Python; Database management tool: MySQL.
7. Empirical study.
Results
1. The status quo of research collaboration in biomedical domain
(1) The fields of C&C
China: From the year2000to the year2010, the percentage of co-authoredpapers and the average number of authors per paper in Chinese C&C field weregenerally increasing. The geographic distribution of the research collaborationactivity was extremely uneven.87authors and5institutions ranked in top1%of allthe three centralities.92.8%authors belonged to10-Core and below.90.93%authorsbelonged to3-slice and below.63cohesive research groups were found. Coronaryartery disease, myocardial infarction, etc. were the focuses of research collaboration.
The world: From the year1981to the year2010, research collaborations hadincreased at the author, institution and countries/regions level in international CHDresearch.3000most collaborative authors,572most collaborative institutions and52countries/regions were extracted from their corresponding collaboration network.766Cliques were found in the most collaborative authors.308Cliques were found in themost collaborative institutions. Western countries/regions represented the core of theworld's collaboration, while eastern countries/regions scattered at the periphery of thenetwork. The rate of economic development in the countries/regions affected themulti-national collaboration behavior as well.
(2) The fields of Oncology
China: From the year2000to the year2009, the research collaboration In thefield of Chinese oncology research were mostly distributed in eastern regions such asBeijing, Shanghai and Guangdong. Three Centrality measures correlated with therankings of author’s productivity. Most authors (90%) belonged to small K-Core(smaller than9).92.25%of all the authors belonged to M-Slices lower than four.480authors were in11-slice and above, about1%of all the authors.12groups weregenerated by using hierarchical clustering analysis.6groups were found by usingmultidimensional scaling analysis.
The world: From the year2001to the year2010,36most collaborative academiccommunities were indentified in international oncology research domain. NakamuraY was the most productive author, publishing117papers. The largest academiccommunity contains8authors who were mainly from the Harvard University Schoolof Medicine, the Division of Hematology and Oncology of the Dana Farber CancerInstitute, and the Jerome Lipper Multiple Myeloma Center of Boston. All the5authors in the second Component were affiliated with the Department ofNeurosurgery of the University of Illinois College of Medicine at Peoria. All the4authors in the third Component were from the Department of Leukemia of the MDAnderson Cancer Center at the University of Texas. The primary focuses of these36academic communities were multiple myeloma, angiogenesis and lymphocyticleukaemia, etc.
(3) The fields of Health Management
China: From the year2007to the year2012,1933authors and625institutionswere involved in the health management research activities. The average number ofauthors per paper was3. The number of authors with papers no less than4orcentrality larger than0was54. Hunang Jianshi, Wu Liuxin and Zeng Qiang toped theranking of productivity and centrality, and together with Bai Shuzhong, Tian Jingfaand Han Jing, they formed a broader collaboration network. Wang Peiyu and Du Bingformed their own collaboration groups as well. High productivity authors weremostly from Air Force Institute of Aeromedicin, Peking University Health Science Center, Beijing Physical Examination Center and Chinese Academy of MedicalScience&Peking Union Medical College.
The world: From the year1999to the year2011, both the productions andresearch collaboration at the author, institution and countries/regions levels wereincreasing in health managment research domain.17researchers (O'Toole T1, etc)can be seen as the academic leaders in this field.37research institutions (Universityof Minnesota, etc) played a vital role in the information dissemination and resourcescontrol in health management. The Component analysis found that22research groupscan be regarded as the backbone in this field. The8institution groups consisting of33institutions formed the core of this field. USA, UK and Australia lied in the center bycohesive subgroup analysis. High frequency keywords such as care, disease, systemand model were involved in the health management field.
2. The relationship between research collaboration and research performance inbiomedical domain
The relationship between research performance of scientists and internationalcollaboration: In the field of C&C, both international publications andinter-institutional publications were higher quality than those published by scientistswithin the same institution, and that there was a significant positive correlationbetween scientists’research performance and international collaboration.
The relationship between research performance of countries and internationalcollaboration: In international Coronary Heart Disease (CHD) research domain, themost productive countries were USA, Japan, German, British, etc. The mostinfluential countries included USA, British and Germain. However, Hungary,Switzerland, Danmark, Austria, Finland, and Norway led in the ranking measured bytheir proportion of collaborative output.
3. The research collaboration prediction in biomedical domain
The collaboration prediction in CHD research showed that: Both LR model andSVM model scored well for all the four evaluation measures. SVM model beated LRmodel in terms of3evaluation measures: precision rate, recall rate and AUC. Both learning models generally scored high for high productivity author sets in terms of allthe four evaluation measures, but scored low for less productivity author sets. Wetrained the two models with the selected features on the entire author set, and foundthat the testing results were improved for both the LR model and SVM model. TheLR model generally produced relatively lower accuracy rates when testingtopological features separately than it did when testing all the topological features asa whole.
Conclusions
1. The research collaboration in biomedical domain were unevenly distributed
The tendency to work in group among researchers in producing scientificpublications in Chinese C&C field was increasing over time. Collaboration amongscientists, institutios and countries in CHD research had significantly grown over thepast three decades.
However, the research collaboration activities in diffentent regions of China inC&C fields were unevenly distributed, and western countries were placing at the coreof the international CHD research collaboration network. Moreover, the primarycollaborators of high incomes countries were high incomes ones, then the middleincomes and low incomes ones.
2. Centrality analysis can help to choose the discipline academic leader
Degree centrality, Closeness centrality and Betweenness centrality can be goodindicators of author’s productivity, then help to indentify the academic leaders inbiomedical research domain.
3. Cohesive group analysis can help to extract best research groups in biomedicaldomain
Cohesive group analysis such as Component analysis, K-core analysis, M-sliceanalysis and Clique analyis can be used to extract the outstanding research groups inthe biomedical research domain.
4. There was a significant positive correlation between scientists’ researchperformance and international collaboration
The quality of research papers was influenced by the degree of reserachcollaboration, and there was a significant positive correlation between scientists’research performance and international collaboration.
5. The topological features in co-authorship network can be used to makecollaboration relationship prediction
Both the traditionally used algorithm LR and increasingly promising algorithmSVM model performed well in co-author relationship prediction. The collaborationrelationships for high productive authors were easier to predict than less productiveauthors in terms of all the four evaluation measures. Testing results for both modelswere improved through feature selection. The co-author relationship predictionshould be made by using these topological features as a whole instead of using asingle one.
Suggestions
1. Promoting the research performance of scientists by strengthening the researchcollaboration
The research quantity and quality of scientists in the field of biomedicine can bepromoted by strengthening the research collaboration. Results showed that theresearch collaboration degree in biomedical rearsch domain were increasing, howeverthe research collaborations were unevenly distributed. So we should encouragecross-regional research collaboration and international research collaboration inbiomedical research domain so that the biomedical research can develop in harmony.
2. Social network analysis should be applied to the scholarly network analysis
By applying social network analysis (SNA) to the coauthorship analysis,centrality analysis helped to choose the discipline academic leader, and cohesivegroup analysis helped to extract outstanding research groups. This again proved the applicability and feasibility of SNA in the field of scientometrics.
3. Data mining should be a good complement to scientometrics
We made an initial attempt to use data mining techniques in this study. Wepresented supervised machine learning methods for building link prediction modelsfrom topological features of node pairs in co-authorship networks. The models couldbe useful in identifying unrealized yet potentially sucessful collaboration, whichwould in turn faciliate the development of strong research groups, and provideevidence and guiding for research management and policy-making.
Innovation points
1. Developing a brand-new theory for research collaboration--The prediction ofresearch collaboration relationship
The theory of research collaboration evolution and prediction, together withtraditional research collaboration theories, composed a complete theory system ofresearch collaboration. The solving of research collaboration evolution and predictionproblem can help to organize and manage strong research team, and improve thework efficiency.
2. Applying data mining, machine learning social network analysis, multivariatestatistics and computer programming to scientometrics
Data mining, machine learning, prediction analysis, social network analysis, andmultivariate statistics were combined with scientometrics methods to explore theresearch collaboration in biomedical domain, and Java, Python and MySQL wereused to convert CSV, TSV and XML files into the data input for WEKA, Lpmade,MySQL, etc.
3. The results of extracting highly influential scientists and research groups andco-author relationship link prediction can provide scientific evidences for China’sresearch management policymaking
This study proved the significant positive correlation between scientists’ research performance and international collaboration, verified the excellent researchperformance of the research groups found by cohesive groups analysis, and confirmedthe practicability of the co-author relationship prediction by using topoligical featuresin co-authorship network. These facts can provide scientific evidences for China’sresearch management policymaking.
Conceptions for making further study
1. Research collaboration prediction in heterogeneous network
We will examine the collaboration predictions in heterogeneous network basedon the results of link prediction in homogeneous network. As an heterogeneousnetwork contain multiply types of nodes and edges, it can provide more topologicalfeatures, and make the link prediction more accurately.
2. The combination of Topic modeling with co-authorship network
Topic modeling has been widely used in the field of natural language processing.We will apply the LDA method to the co-authorship network so that the “researchsubject” can be incorporated into the collaboration analysis more properly.
The dissertation is done as part of the project ‘‘Cooperation Analysis ofTechnology Innovation Team Member Based on Knowledge Network-EmpiricalEvidence in the Biology and Biomedicine Field (No.71103114)’’ and the project‘‘Scientific and Technological Collaboration in the Field of Biomedicine-UsingCo-authorship and Co-inventorship Analysis (No.71240006)’’, both supported byNational Natural Science Foundation of China.
本研究旨在采用理论联系实际、定性和定量相结合的方法，将知识网络背景下的科学计量学理论应用于生物医学研究评价，系统梳理传统的科研合作理论体系，并试图开辟科研合作理论的新领域，即科研合作关系预测研究，最后通过实证研究进一步证实。力求使各级科研管理工作者和科研人员更好地理解国家科研创新体系，认识科研合作的重要性和必要性，为我国科研管理决策提供参考依据。
研究对象
本研究选取死亡原因第一位的心血管病研究领域、总体死亡率居高不下的肿瘤研究领域以及作为现代医学五大创新体系之一的健康管理研究领域作为研究对象。
数据来源
1．心血管病研究领域数据
国内数据：选取《中文核心期刊要目总览》（2008版）涵盖的心血管病研究领域全部5种核心期刊，从中国知网和重庆维普数据库获取2000-2010年期间上述期刊发表所有论文的题录信息。
国际数据：a、从Web of Science（WoS）的Science Citation Index-expanded（SCI-E）中获取1981-2010年期间所有主题词为“coronary”的文献的题录信息；b、选取期刊引证报告“Cardiac&Cardiovascular System”学科中影响因子大于10的4种期刊，从SCI-E中获取2001-2010年上述期刊发表所有论文的题录信息。
2．肿瘤研究领域数据
国内数据：选取中国科学引文数据库涵盖的肿瘤研究领域的全部10种核心期刊，从中国知网和重庆维普数据库获取2000-2009年上述期刊发表所有论文的题录信息。
国际数据：选取期刊引证报告中“Oncology”学科内影响因子和被引频次均处前10%的30种期刊，从WoS中的SCI-E、CPCI-SSH数据库和生物医学领域PubMed数据库获取2001-2010年上述期刊发表所有论文的题录信息。
3．健康管理研究领域数据
国内数据：从万方数据知识服务平台、维普网以及《中华健康管理学杂志》纸质及光盘版，获取2007-2012年期间《中华健康管理学杂志》发表的所有论文的题录信息。
国际数据：从WoS中获取1999-2011年期间主题为“health management”的所有论文的题录信息。
研究方法
1．文献分析法
通过纸质文献和电子期刊文献检索已发表的相关文献，获取原始资料并进行相关分析。
2．科学计量学方法
利用合著分析研究生物医学领域合作现象，利用共词分析分析领域研究热点。
3．社会网络分析
使用Pajek、Ucinet和CiteSpace II等社会网络分析软件实现Component分析、Centrality分析、K-core分析和M-slice分析、Clique分析等，并绘制网络图。
4．统计方法
主要包括曼-惠特尼U检验、Logistic回归、序次Logistic回归分析、聚类分析和多维尺度分析。
5．机器学习
利用LPmade软件计算合著网络中作者对之间的拓扑特征，运用WEKA软件实现监督式机器学习Logstic Regression（LR）模型和Support Vector Machine（SVM）模型。
6．计算机编程和数据库
利用Java和Python编程语言实现原始数据向矩阵数据的转换、各项指标的计算，利用MySQL数据库管理原始数据。
7．实证研究方法
采用理论研究与实证研究相结合的方法，分析生物医学领域科研合作情况。
研究结果
1．生物医学领域科研合作现状分析
（1）心血管病研究领域
国内：2000-2010年期间，我国心血管病研究领域的合著率和篇平均作者数总体上呈增长趋势；科研合作研究的区域分布极不平衡；在该领域的合著网络中，三种中心度排名均在前1%的作者共87位、机构共5所；10-core及以下合著网络中的作者占总数的92.8%；3-slice以下的合著网络中的作者占总数的90.93%；M-slice分析发现了63个合作密切的科研团队；冠状动脉疾病、心肌梗死等为合作研究的热点。
国际：1981-2010年期间，国际冠心病研究领域作者、机构和国家层面的科研合作都处于增长趋势；从作者合作网络、机构合作网络和国家合作网络中分别发现了3000名合作最为密切的作者、572个合作最为密切的机构以及52个合作最为密切的国家；合作最为密切的作者形成了766个Clique，合作最为密切的机构形成了308个Clique；西方国家之间的合作最为密切，并处于国际合作网络的核心，而东方国家/区域则处于网络的边缘位置；国家经济发展水平影响着国际合作行为。
（2）肿瘤研究领域
国内：2000-2009年期间，我国肿瘤研究领域的科研合作主要集中于北京、上海、广东等东部地区；作者的三种中心度排名与其生产能力成正比；合著网络的最大Component包含的作者占总数的58.10%；8-core及以下合著网络中的作者占总数的90%；3-slice及以下的合著网络中的作者占总数的92.25%；11-slice及以上的合著网络包含480位作者（约占全部作者人数的1%）；通过聚类分析发现了该领域12个合作密切的科研团队；通过多维尺度分析发现了该领域6个合作密切的科研团队。
国外：2001-2010年期间，国际肿瘤研究领域最具代表性的作者合著网络形成36个较为密集的凝聚子群；产量最高的作者为Nakamura Y，发表论文117篇；最大的学术共同体由8位作者组成，主要来自哈佛大学医学院Dana Farber癌症研究所和波士顿中心的杰罗姆理柏多发性骨髓瘤研究中心，第二大聚类由5位作者组成，大都来自皮奥里亚大学、伊利诺伊大学医学院神经外科部，第三大聚类由4位作者组成，来自美国德州大学MD安德森癌症中心；36个大型合作网络群体的研究主要集中在多发性骨髓瘤、血管生成和急性淋巴细胞白血病等前沿领域。
（3）健康管理研究领域
国内：2007-2012年期间，《中华健康管理学杂志》期刊中共有1933位作者和625所机构参与了657篇论文的创作工作，平均约每3位作者发表1篇论文；发文量不小于4篇或中心度大于0的作者共有54位，其中学者黄建始、武留信和曾强的发文量及中心度都位于前列，并与白书忠、田京发、韩静等学者形成了影响力较为广泛的合作区域网络，王培玉、杜兵等学者也组成了各自的合作团队；发文量较高的作者多集中于解放军空军航空医学研究所、北京大学医学部、北京市体检中心、中国医学科学院等机构。
国际：1999-2011年期间，国际健康管理领域的研究无论在科研产量，还是在作者、机构和国家层面的科研合作程度都呈上升趋势；通过中心度指标遴选出以O'Toole T1为代表的17名高影响力作者和以明尼苏达大学为代表的37所高影响力研究机构；通过Component分析在作者合著网络中发现了22个具有固定合作关系的科研团队，从机构合著网络中发现了8个具有固定合作关系的机构合作团队；美国、英国、澳大利亚等国处于国际研究合作的中心位置；高频关键词中的Care、Disease、System和Model均与健康管理研究密切相关。
2．生物医学领域科研绩效与国际合作关系研究
科研人员科研绩效与国际合作的关系：心血管病研究领域国际合作论文的被引频次最高，机构间合作论文的被引频次次之，机构内合作论文的被引频次最低；科研人员的科研产量和质量在决定其国际合作度时具有正作用；科研人员的国际合作率与其科研产量和质量之间也存在正依赖性。
国家科研绩效与国际合作关系：国际冠心病研究领域中，高产国家为美国、日本、德国和英国等；高影响力国家为美国、英国和德国等；而国际合作程度最高的国家为匈牙利、瑞士、丹麦、奥地利、芬兰和挪威等。
3．生物医学领域科研合作关系预测研究
国际冠心病研究领域科研合作预测结果显示，LR模型和SVM模型在四个准确度指标上的得分都很高，其中SVM模型在Precision率、Recall率和AUC三个指标上优于LR模型；两个模型对于产量高的作者集预测结果的准确性较高，而对于产量低的作者集预测结果的准确性较低；通过选取的最优特征值对上述两个模型检验发现，二模型的测试结果均有一定的提高，且SVM模型优于LR模型；各个拓扑特征单独预测的测试结果低于综合各个拓扑特征测试的结果。
研究结论
1．生物医学领域的科研合作分布不均衡
虽然我国心血管病研究领域中科研人员的合作趋势、国际冠心病研究领域各层面的合作趋势在不断加强，但是我国心血管病研究领域科研合作的区域合作分布却极不平衡，东部地区科研合作较为密切，且区域内部的合作远远大于区域之间的合作。同时，西方发达国家在冠心病研究领域的国际合作活动中处于核心地位。此外，高收入国家的首选合作对象为高收入国家，其次才是中等
收入国家和低收入国家。2．中心度指标可用于遴选生物医学领域学科带头人
Degree中心度、Closeness中心度和Betweenness中心度可作为衡量作者产量的指标，帮助科研管理部门遴选生物医学领域的学科带头人。
3．凝聚子群可用于发现生物医学领域的优秀科研合作团队
凝聚子群分析，如Compnent分析、K-core分析、M-slice分析和Clique分析等，可以帮助科研管理部门发现生物医学领域的优秀科研合作团队。
4．科研人员的科研绩效与国际科研合作水平存在正相关性
论文质量受科研人员科研合作水平的影响；科研人员的科研产量和质量与其国际科研合作程度之间存在显著的正相关性。
5．利用合著网络中的拓扑特征进行科研合作关系的预测具有可行性
传统的LR模型与新兴的SVM模型在预测合作关系时性能都很好；高产作者之间的合作关系比低产作者之间的合作关系更容易预测；通过拓扑特征的选取可使模型的准确率得以提升；应当综合多个拓扑特征进行链接预测，避免仅选择单一的拓扑特征进行预测。
对策建议
1．通过加强科研合作提高科研人员的科研绩效
鼓励生物医学领域的国际科研合作可以提高该领域的科研产量和质量。而研究结果显示，虽然生物医学领域科研合作程度呈现上升趋势，但是科研合作分布却极不均衡。因此，应当一方面鼓励我国各区域之间进行科研合作研究，另一方面鼓励与发达国家之间进行国际合作研究，促进生物医学科研水平协调发展。
2．社会网络分析法应有效用于学术网络的分析
本研究将社会网络分析法应用于大型合著网络的分析，发现其中心性指标可以帮助科研管理部门遴选学科带头人和科研团队，而凝聚子群方法可以帮助科研管理部门挖掘优秀科研创新团队，再一次证明了社会网络分析在科学计量学领域的适用性和可行性。
3．数据挖掘技术应作为科学计量学方法的有益补充
本研究对数据挖掘技术在科学计量学中的应用进行了初步尝试，通过监督式机器学习方法，利用合著网络中的拓扑结构特征建立链接预测模型，发现这些模型可以用于预测潜在合作关系，从而帮助组建和管理强大的科研团队，为科研管理工作和科技政策制定提供参考和指导。
主要创新点
1．开辟科研合作理论研究的新领域——科研合作预测研究
将科研合作的预测研究与传统的科研合作理论研究结合，形成完整的科研合作理论体系。科研合作关系预测问题的解决，可为组建和管理强大的科研创新团队提供帮助，并大大提高科研部门的工作效率。
2．实现了数据挖掘、机器学习、社会网络分析、多元统计以及计算机编程技术等多种方法在计量学研究领域的结合和合理应用
在研究方法方面引入了数据挖掘技术、机器学习、社会网络分析、多元统计等多种方法，大大拓展了科学计量学的方法体系。
利用计算机编程技术开发各种应用软件，用于CSV、TSV和XML文件向关系数据库格式的转化、数据关系矩阵的构建以及拓扑特征数据向机器学习软件所需格式的转化，实现数据处理完全自动化，提高研究效率以及结果的准确性。
3．高影响力个人和机构遴选结果和科研合作关系预测结果可为我国科研管理的科学化和政策制定提供决策支持
本研究证明了社会网络分析中心度指标与科研人员科研产量之间的高度相关性，验证了利用社会网络分析凝聚子群方法发现的具有稳定合作关系的科研团队在其所属领域的优秀科研绩效；证实了利用合著网络中的拓扑特征进行科研合作关系预测的可行性。这些成果有助于我国科研管理的科学化，并为我国科研管理政策的制定提供决策支持。
不足与展望
1．异构学术网络中的科研合作关系预测研究
在同构网络（合著网络）的科研合作预测研究基础上进行异构学术网络的链接预测问题，异构网络中的节点类型可以为多种（如作者和论文），链接类型也可以为多种（如撰写/被撰写、引用/被引用）。由于异构网络能够提供作者更多的拓扑结构特征，因此有可能会产生比同构网络更好的链接预测效果。
2．主题模型研究与作者合著网络分析的结合
下一步可以将目前常用的主题模型LDA应用于合著网络，从而摆脱仅从“人”的层面分析科研合作的困境，将科研人员的“研究主题”融入到合作关系的分析中。
本文系国家自然科学基金“基于知识网络的科技创新团队成员合作研究--以生物医学为例（批准号：71103114）”和国家自然科学基金“论文合著和专利合作视角的生物医学领域科技合作研究（批准号：71240006）”的组成部分和研究成果之一。
Purpose
By combining theories with empirical studies, as well as qualitative methods withquantitative methods, we aimed to apply scientometrics theories to the evaluation ofthe research collaboration in biomedical fields in the background of knowledgenetwork. We sorted out the traditional research collaboration theory system and madeattempts to establish a new one–the theory of collaboration relationship prediction.Then we confirmed the validity of this system through an empirical study. Theseefforts will help the research management departments of various levels andresearchers have a better understanding of the structure of China’s innovation systemand get a right picture of the importance and necessity of research collaboration. Webelieve this study will provide scientific evidences and suggestions for researchmanagement policymaking.
Research subjects
Three subfields of biomedicine were chosen for this study: Cardiology&Cardiovasology, Oncology, Health Management.
Data
1. Data in the fields of Cardiology&Cardiovasology (C&C)
(1) Chinese data
Five major journals in C&C field were chosen from ‘A Guide to the CoreJournals of China (2008Edition)’. Then, the bibliographic records published in these 5journals from the year2000through the year2010were collected from databases inChina National Knowledge Infrastructure (CNKI) and VIP Journal IntegrationPlatform (VIP).
(2) International data
a. All the bibliographic records containing ‘‘coronary’ in their title, abstract orkeywords from the year1981through the year2010were collected from ScienceCitation Index-expanded (SCI-E) in Web of Science (WoS).
b. Top four journals with5-year Journal Impact Factor greater than10werechosen from category “Cardiac&Cardiovascular System” in Journal Citation Report.Then, all the bibliographic records published in these journals from the year2001tothe year2010were collected from SCI-E.
2. Data in the fields of oncology
(1) Chinese data
We chose all the10Core Oncology journals from Chinese Science CitationDatabase. Then, all the bibliographic records published in these10journals from theyear2000through the year2009were collected from databases in CNKI and VIP.
(2) International data
Top10%(30) journals in terms of both Impact Factor and Citations were chosenfrom category “Oncology” in Journal Citation Report. Then, all the bibliographicrecords in these journals from the2001to the2010were collected from SCI-E,CPCI-SSH of Web of Science database.
3. Data in the fields of health management
(1) Chinese data
All the bibliographic records in “Chinese Journal of Health Management” from2007-2012were collected from VJIP, Wangfang Data and CD-ROM version ofChinese Journal of Health Management.
(2) International data
All the bibliographic records with the keyword “health management” from theyear1999to the year2011were collected from WoS.Methods
1. Literature analysis.
2. Scientomerics: Co-authorship and Co-word analysis.
3. Social network analysis: Component analysis, Centrality analysis, K-coreanalysis, M-slice analysis and Clique analysis.
4. Statistical methods: Mann–Whitney U test, Binary logistic regression, Orderedlogistic regression, Hierarchical Clustering and Multidimensional Scaling.
5. Machine learning: Logistic regression (LR), Support Vector Machine (SVM),Lpmade and WEKA.
6. Programming language: Java and Python; Database management tool: MySQL.
7. Empirical study.
Results
1. The status quo of research collaboration in biomedical domain
(1) The fields of C&C
China: From the year2000to the year2010, the percentage of co-authoredpapers and the average number of authors per paper in Chinese C&C field weregenerally increasing. The geographic distribution of the research collaborationactivity was extremely uneven.87authors and5institutions ranked in top1%of allthe three centralities.92.8%authors belonged to10-Core and below.90.93%authorsbelonged to3-slice and below.63cohesive research groups were found. Coronaryartery disease, myocardial infarction, etc. were the focuses of research collaboration.
The world: From the year1981to the year2010, research collaborations hadincreased at the author, institution and countries/regions level in international CHDresearch.3000most collaborative authors,572most collaborative institutions and52countries/regions were extracted from their corresponding collaboration network.766Cliques were found in the most collaborative authors.308Cliques were found in themost collaborative institutions. Western countries/regions represented the core of theworld's collaboration, while eastern countries/regions scattered at the periphery of thenetwork. The rate of economic development in the countries/regions affected themulti-national collaboration behavior as well.
(2) The fields of Oncology
China: From the year2000to the year2009, the research collaboration In thefield of Chinese oncology research were mostly distributed in eastern regions such asBeijing, Shanghai and Guangdong. Three Centrality measures correlated with therankings of author’s productivity. Most authors (90%) belonged to small K-Core(smaller than9).92.25%of all the authors belonged to M-Slices lower than four.480authors were in11-slice and above, about1%of all the authors.12groups weregenerated by using hierarchical clustering analysis.6groups were found by usingmultidimensional scaling analysis.
The world: From the year2001to the year2010,36most collaborative academiccommunities were indentified in international oncology research domain. NakamuraY was the most productive author, publishing117papers. The largest academiccommunity contains8authors who were mainly from the Harvard University Schoolof Medicine, the Division of Hematology and Oncology of the Dana Farber CancerInstitute, and the Jerome Lipper Multiple Myeloma Center of Boston. All the5authors in the second Component were affiliated with the Department ofNeurosurgery of the University of Illinois College of Medicine at Peoria. All the4authors in the third Component were from the Department of Leukemia of the MDAnderson Cancer Center at the University of Texas. The primary focuses of these36academic communities were multiple myeloma, angiogenesis and lymphocyticleukaemia, etc.
(3) The fields of Health Management
China: From the year2007to the year2012,1933authors and625institutionswere involved in the health management research activities. The average number ofauthors per paper was3. The number of authors with papers no less than4orcentrality larger than0was54. Hunang Jianshi, Wu Liuxin and Zeng Qiang toped theranking of productivity and centrality, and together with Bai Shuzhong, Tian Jingfaand Han Jing, they formed a broader collaboration network. Wang Peiyu and Du Bingformed their own collaboration groups as well. High productivity authors weremostly from Air Force Institute of Aeromedicin, Peking University Health Science Center, Beijing Physical Examination Center and Chinese Academy of MedicalScience&Peking Union Medical College.
The world: From the year1999to the year2011, both the productions andresearch collaboration at the author, institution and countries/regions levels wereincreasing in health managment research domain.17researchers (O'Toole T1, etc)can be seen as the academic leaders in this field.37research institutions (Universityof Minnesota, etc) played a vital role in the information dissemination and resourcescontrol in health management. The Component analysis found that22research groupscan be regarded as the backbone in this field. The8institution groups consisting of33institutions formed the core of this field. USA, UK and Australia lied in the center bycohesive subgroup analysis. High frequency keywords such as care, disease, systemand model were involved in the health management field.
2. The relationship between research collaboration and research performance inbiomedical domain
The relationship between research performance of scientists and internationalcollaboration: In the field of C&C, both international publications andinter-institutional publications were higher quality than those published by scientistswithin the same institution, and that there was a significant positive correlationbetween scientists’research performance and international collaboration.
The relationship between research performance of countries and internationalcollaboration: In international Coronary Heart Disease (CHD) research domain, themost productive countries were USA, Japan, German, British, etc. The mostinfluential countries included USA, British and Germain. However, Hungary,Switzerland, Danmark, Austria, Finland, and Norway led in the ranking measured bytheir proportion of collaborative output.
3. The research collaboration prediction in biomedical domain
The collaboration prediction in CHD research showed that: Both LR model andSVM model scored well for all the four evaluation measures. SVM model beated LRmodel in terms of3evaluation measures: precision rate, recall rate and AUC. Both learning models generally scored high for high productivity author sets in terms of allthe four evaluation measures, but scored low for less productivity author sets. Wetrained the two models with the selected features on the entire author set, and foundthat the testing results were improved for both the LR model and SVM model. TheLR model generally produced relatively lower accuracy rates when testingtopological features separately than it did when testing all the topological features asa whole.
Conclusions
1. The research collaboration in biomedical domain were unevenly distributed
The tendency to work in group among researchers in producing scientificpublications in Chinese C&C field was increasing over time. Collaboration amongscientists, institutios and countries in CHD research had significantly grown over thepast three decades.
However, the research collaboration activities in diffentent regions of China inC&C fields were unevenly distributed, and western countries were placing at the coreof the international CHD research collaboration network. Moreover, the primarycollaborators of high incomes countries were high incomes ones, then the middleincomes and low incomes ones.
2. Centrality analysis can help to choose the discipline academic leader
Degree centrality, Closeness centrality and Betweenness centrality can be goodindicators of author’s productivity, then help to indentify the academic leaders inbiomedical research domain.
3. Cohesive group analysis can help to extract best research groups in biomedicaldomain
Cohesive group analysis such as Component analysis, K-core analysis, M-sliceanalysis and Clique analyis can be used to extract the outstanding research groups inthe biomedical research domain.
4. There was a significant positive correlation between scientists’ researchperformance and international collaboration
The quality of research papers was influenced by the degree of reserachcollaboration, and there was a significant positive correlation between scientists’research performance and international collaboration.
5. The topological features in co-authorship network can be used to makecollaboration relationship prediction
Both the traditionally used algorithm LR and increasingly promising algorithmSVM model performed well in co-author relationship prediction. The collaborationrelationships for high productive authors were easier to predict than less productiveauthors in terms of all the four evaluation measures. Testing results for both modelswere improved through feature selection. The co-author relationship predictionshould be made by using these topological features as a whole instead of using asingle one.
Suggestions
1. Promoting the research performance of scientists by strengthening the researchcollaboration
The research quantity and quality of scientists in the field of biomedicine can bepromoted by strengthening the research collaboration. Results showed that theresearch collaboration degree in biomedical rearsch domain were increasing, howeverthe research collaborations were unevenly distributed. So we should encouragecross-regional research collaboration and international research collaboration inbiomedical research domain so that the biomedical research can develop in harmony.
2. Social network analysis should be applied to the scholarly network analysis
By applying social network analysis (SNA) to the coauthorship analysis,centrality analysis helped to choose the discipline academic leader, and cohesivegroup analysis helped to extract outstanding research groups. This again proved the applicability and feasibility of SNA in the field of scientometrics.
3. Data mining should be a good complement to scientometrics
We made an initial attempt to use data mining techniques in this study. Wepresented supervised machine learning methods for building link prediction modelsfrom topological features of node pairs in co-authorship networks. The models couldbe useful in identifying unrealized yet potentially sucessful collaboration, whichwould in turn faciliate the development of strong research groups, and provideevidence and guiding for research management and policy-making.
Innovation points
1. Developing a brand-new theory for research collaboration--The prediction ofresearch collaboration relationship
The theory of research collaboration evolution and prediction, together withtraditional research collaboration theories, composed a complete theory system ofresearch collaboration. The solving of research collaboration evolution and predictionproblem can help to organize and manage strong research team, and improve thework efficiency.
2. Applying data mining, machine learning social network analysis, multivariatestatistics and computer programming to scientometrics
Data mining, machine learning, prediction analysis, social network analysis, andmultivariate statistics were combined with scientometrics methods to explore theresearch collaboration in biomedical domain, and Java, Python and MySQL wereused to convert CSV, TSV and XML files into the data input for WEKA, Lpmade,MySQL, etc.
3. The results of extracting highly influential scientists and research groups andco-author relationship link prediction can provide scientific evidences for China’sresearch management policymaking
This study proved the significant positive correlation between scientists’ research performance and international collaboration, verified the excellent researchperformance of the research groups found by cohesive groups analysis, and confirmedthe practicability of the co-author relationship prediction by using topoligical featuresin co-authorship network. These facts can provide scientific evidences for China’sresearch management policymaking.
Conceptions for making further study
1. Research collaboration prediction in heterogeneous network
We will examine the collaboration predictions in heterogeneous network basedon the results of link prediction in homogeneous network. As an heterogeneousnetwork contain multiply types of nodes and edges, it can provide more topologicalfeatures, and make the link prediction more accurately.
2. The combination of Topic modeling with co-authorship network
Topic modeling has been widely used in the field of natural language processing.We will apply the LDA method to the co-authorship network so that the “researchsubject” can be incorporated into the collaboration analysis more properly.
The dissertation is done as part of the project ‘‘Cooperation Analysis ofTechnology Innovation Team Member Based on Knowledge Network-EmpiricalEvidence in the Biology and Biomedicine Field (No.71103114)’’ and the project‘‘Scientific and Technological Collaboration in the Field of Biomedicine-UsingCo-authorship and Co-inventorship Analysis (No.71240006)’’, both supported byNational Natural Science Foundation of China.
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