基于子图的科学家合作网络家族辨识
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摘要
对科学家合作网络宏观全局结构的研究多表明其小世界和无标度共性特征,而就其微观子图分布特性即网络家族的认知较少。研究基于复杂网络家族辨识的子图比剖面方法、子图组合强度和子图浓度排序方法,系统辨识若干规模和不同领域的科学家合作网络的家族分类及特征。研究表明,具有宏观全局结构共性的上述科学家合作网络,在微观子图的分布、组合或排序上依次具有1种形式、3种规则及5种模式的特性,因而划分为相应家族类别并分别表征理论型、实验型及理论实验型合作网络家族特征。相关方法可用于系统分析和比较科研合作家族行为特性和演化机理。
The research on the macro-global structure of the cooperative network of scientists mostly shows its smallworld and scale-free commonality, while its micro subgraph distribution characteristic, that is, the network family, has less cognition. This paper studies the subgraph ratio profile method based on complex network family identification, the sub-graph combination strength and sub-graph concentration ranking method, and systematically identifies the family classification and characteristics of scientists' cooperative networks of several scales and different fields. The results show that the cooperative networks of scientists have the characteristics of one form, three rules and five patterns in the distribution, combination or ordering of micrographs, which have the commonness of macroscopical and global structure, therefore, it is divided into corresponding family categories and characterizes the family characteristics of theoretical, experimental and theoretical experimental cooperative networks respectively. The related methods can be used to systematically analyze and compare the behavior characteristics and evolution mechanism of cooperative families in scientific research.
The research on the macro-global structure of the cooperative network of scientists mostly shows its smallworld and scale-free commonality, while its micro subgraph distribution characteristic, that is, the network family, has less cognition. This paper studies the subgraph ratio profile method based on complex network family identification, the sub-graph combination strength and sub-graph concentration ranking method, and systematically identifies the family classification and characteristics of scientists' cooperative networks of several scales and different fields. The results show that the cooperative networks of scientists have the characteristics of one form, three rules and five patterns in the distribution, combination or ordering of micrographs, which have the commonness of macroscopical and global structure, therefore, it is divided into corresponding family categories and characterizes the family characteristics of theoretical, experimental and theoretical experimental cooperative networks respectively. The related methods can be used to systematically analyze and compare the behavior characteristics and evolution mechanism of cooperative families in scientific research.
引文
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[2]JONES B F,WUCHTY S,UZZI B.Multi-university research teams:shifting impact,geography,and stratification in science[J].Science,2008,322(5905):1259-1262.
[3]KATZ J S,MARTIN B R.What is research collaboration?[J].Research Policy,1997,26(1):1-18.
[4]NEWMAN M E J.The structure of scientific collaboration networks[J].Proceedings of the National Academy of Sciences of the United States of America,2001,98(2):404-409.
[5]WATTS D J,STROGATZ S H.Collective dynamics of smallworldnetworks[J].Nature,1998,393:440-442.
[6]BARABáSI A L,ALBERT R.Emergence of scaling in random networks[J].Science,1999,286:509-512.
[7]NEWMAN M E J.Scientific collaboration networks:I:network construction and fundamental results[J].Physical Review E,2001,64:016131(1-8).
[8]NEWMAN M E J.Scientific collaboration networks:II:Shortest paths,weighted networks,and centrality[J].Physical Review E,2001,64:016132(1-7).
[9]BARABáSI A L,JEONG H,NEDA Z,et al.Evolution of the social network of scientific collaborations[J].Physica A:Statistical Mechanics and its Applications,2002,311:590-614.
[10]胡枫,赵海兴,何佳倍,等.基于超图结构的科研合作网络演化模型[J].物理学报,2013,62(19):539-546.
[11]曹志鹏,潘启亮.我国高校间科研创新合作现状:基于2014年合著论文的社会网络分析[J].科技管理研究,2017,37(1):93-98.
[12]李纲,刘先红.基于合作网络中心性指标的科研团队学术带头人识别研究[J].科技管理研究,2016,38(8):127-132.
[13]宋志红,史玉英,李冬梅.学术论文质量特征对明星作者网络位置的影响:以1990-2012年“创新网络”领域的文献为例[J].科学学研究,2014,32(5):660-668.
[14]刘璇,张朋柱,胡海波.国内知识管理领域科研合作网络研究[J].科技进步与对策,2013(15):138-145.
[15]朱云霞,魏建香.我国高校社会科学领域科研合作网络分析[J].情报科学,2014(3):144-149.
[16]谢彩霞.国际科学合作研究状况综述[J].科研管理,2008,29(3):179-186.
[17]SHEN-ORR S,MILO R,MANGAN S,et al.Network motifs in the transcriptional regulatory network of escherichia coli[J].Nature Genetics,2002,31(1):64-68.
[18]MILO R,SHEN-ORR S,ITZKOVITZ N,et al.Network motifs:simple building blocks of complex networks[J].Science,2002,298(5594):824-827.
[19]MILO R,ITZKOVITZ S,KASHTAN N,et al.Superfamilies of evolved and designed networks[J].Science,2004,303(5563):1538-1542.
[20]缪莉莉,韩传峰,刘亮,等.基于模体的科学家合作网络基元特征分析[J].科学学研究,2012,30(10):1468-1475.
[21]刘亮,韩传峰,缪莉莉,等.基于子图结构对等的科学家合作网络角色辨识[J].科学学研究,2013,31(8):1128-1135.
[22]KASHTAN N,ITZKOVITZ S,MILO R,et al.Efficient sampling algorithm for estimating subgraph concentrations and detecting network motifs[J].Bioinformatics,2004,20(11):1746-1758.
[23]WERNICKE S.Efficient detection of network motifs[J].IEEE Transaction on Computational Biology and Bioinformatics,2006,3(4):347-359.
[24]NEWMAN M E J.Assortative mixing in networks[J].Physical Review Letters,2002,89(20/21):109-113.
[25]NORIKO H,PAUL S,SEUNG K,et al.An emerging view of scientific collaboration:scientists'perspectives on collaboration and factors that impact collaboration[J].Journal of the American Society for Information Science and Technology,2003,54(10):952-965.