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1.
This paper proposes a new node centrality measurement index (c-index) and its derivative indexes (iterative c-index and cg-index) to measure the collaboration competence of a node in a weighted network. We prove that c-index observe the power law distribution in the weighted scale-free network. A case study of a very large scientific collaboration network indicates that the indexes proposed in this paper are different from other common centrality measures (degree centrality, betweenness centrality, closeness centrality, eigenvector centrality and node strength) and other h-type indexes (lobby-index, w-lobby index and h-degree). The c-index and its derivative indexes proposed in this paper comprehensively utilize the amount of nodes’ neighbors, link strengths and centrality information of neighbor nodes to measure the centrality of a node, composing a new unique centrality measure for collaborative competency. 相似文献
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Amber N. W. Raile Rachel K. Kim Jounghwa Choi Kim B. Serota Hee Sun Park Dong Wook Lee 《Communication Research Reports》2013,30(2):168-178
Previous research in the association between network centrality and job satisfaction has not established a consistent relationship between the two. Considering a specific type of network and multiple measures of centrality may clarify this relationship. Thus, the current study examined the association between various types of centrality in workplace friendship networks and job satisfaction in a Korean construction company. Friendship network centrality measured as closeness was positively related to job satisfaction. However, friendship centrality measured as betweenness and degree was not related to job satisfaction. The results suggest that distinguishing among measures of centrality and network type is vital for future research. 相似文献
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Q-measures are network indicators that gauge a node's brokerage role between different groups in the network. Previous studies have focused on their definition for different network types and their practical application. Little attention has, however, been paid to their theoretical and mathematical characterization. In this article we contribute to a better understanding of Q-measures by studying some of their mathematical properties in the context of unweighted, undirected networks. An external Q-measure complementing the previously defined local and global Q-measure is introduced. We prove a number of relations between the values of the global, the local and the external Q-measure and betweenness centrality, and show how the global Q-measure can be rewritten as a convex decomposition of the local and external Q-measures. Furthermore, we formally characterize when Q-measures obtain their maximal value. It turns out that this is only possible in a limited number of very specific circumstances. 相似文献
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阐述科研合作网络弹性的概念、研究意义与应用,并以全球100所高校在图书情报学领域所组成的科研合作网络为例,选取网络最大簇规模和网络效率作为网络弹性测度指标,讨论节点点度失效、介数失效以及随机失效策略下该科研合作网络的弹性。结果显示,科研合作网络对随机节点失效具有较强的鲁棒性,其网络容错能力较强;对选择性节点失效的网络抗攻击能力较弱;网络效率相对于网络最大簇规模更适合作为科研合作网络弹性的测度指标。 相似文献
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《Journal of Informetrics》2023,17(1):101358
Extant studies suggest that the proximity between the researchers and their structural positioning in the collaboration network may influence productivity and performance in collaboration research. In this paper, we analyze the co-authorship networks of the three countries, viz. the USA, China, and India, constructed in consecutive non-overlapping 5-year long time windows from bibliometric data of research papers published in the past decade in the rapidly evolving area of Artificial Intelligence and Machine Learning (AI&ML). Our analysis relies on the observations ensued from a comparison of the statistical properties of the evolving networks. We consider macro-level network properties which describe the global characteristics, such as degree distribution, assortativity, and large-scale cohesion etc., as well as micro-level properties associated with the actors who have assumed central positions, defining a core in the network assembly with respect to closeness centrality measure. For the analysis of the core actors, who are well connected with a large number of other actors, we consider share of their affiliations with domestic institutes. We find dominant representation of domestic affiliations of the core actors for high productivity cases, such as China in the second time window and the USA in the first and second both. Our study, therefore, suggests that the domestic affiliation of the core actors, who could access network resources more efficiently than other actors, influences and catalyzes the collaborative research. 相似文献
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Studies of social networks highlight the importance of network structure or structural properties of a given network and its impact on performance outcome. One of the important properties of this network structure is referred to as social capital, which is the network of contacts and the associated values attached to these networks of contacts. This study provides empirical evidence of the influence of social capital and performance within the context of academic collaboration (coauthorship) and suggests that the collaborative process involves social capital embedded within relationships and network structures among direct coauthors. Association between scholars' social capital and their citation-based performance measures is examined. To overcome the limitations of traditional social network metrics for measuring the influence of scholars' social capital within coauthorship networks, the traditional social network metrics is extended by proposing two new measures, of which one is non-weighted (the power–diversity index) and the other (power–tie–diversity index) is weighted by the number of collaboration instances. The Spearman's correlation rank test is used to examine the association between scholars' social capital measures and their citation-based performance. Results suggest that research performance of authors is positively correlated with their social capital measures. The power–diversity index and power–tie–diversity index serve as indicators of power and influence of an individual's ability to control communication and information. 相似文献
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This paper builds an index family, named bi-directional h-index, to measure node centrality in weighted directed networks. Bi-directional h-index takes the directed degree centrality as the initial value and iteratively uses more network information to update the node’s importance. We prove the convergence of the iterative process after finite iterations and introduce an asynchronous updating process that provides a decentralized, local method to calculate the bi-directional h-index in large-scale networks and dynamic networks. The theoretical analysis manifests that the bi-directional h-index is feasible and significant for establishing a greater conceptual framework that includes some existing index concepts, such as lobby index, node’s h-index, c-index and iterative c-index. An example using journal citation networks indicates that the bi-directional h-index is different from directed degree centrality, directed node strength, directed h-degree and the HITS algorithm in ranking node importance. It is irreplaceable and can reflect these measures of node’s importance. 相似文献
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Daniel Schall 《Journal of Informetrics》2013,7(3):730-736
Scientific collaboration commonly takes place in a global and competitive environment. Coalitions and consortia are formed among universities, companies and research institutes to apply for research grants and to perform joint projects. In such a competitive environment, individual institutes may be strategic partners or competitors. Measures to determine partner importance have practical applications such as comparison and rating of competitors, reputation evaluation or performance evaluation of companies and institutes. Many network-centric metrics exist to measure the important of individuals or companies in social and collaborative networks. Here we present a novel context-based metric to measure the importance of partners in scientific collaboration networks. Well-established graph models such as the notion of hubs and authorities provide the basis for this work and are systematically extended to a flexible, context-aware network importance measure. 相似文献
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[目的/意义] 针对复杂网络中的重要节点的识别,设计一种节点中心性算法,在传染病防控、舆情监控、产品营销、人才发现等方面发挥作用。[方法/过程] 同时考虑节点的高影响力邻居的数量及其总体影响,提出HHa节点中心性算法,在真实网络和人工网络上,使用SIR传染病模型模拟信息传播过程,采用单调函数M和肯德尔相关系数作为评价指标验证HHa中心性算法的有效性、准确性以及稳定性。[结果/结论] 实验表明,与7种经典的中心性算法相比,HHa中心性算法得出的排序结果M值为0.999等,排名第2;肯德尔系数为0.845等,高于其他算法0.15左右,排名第1且表现稳定。采用HHa中心性算法识别网络中的重要节点具备可行性。 相似文献
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This article reports a comparative study of five measures that quantify the degree of research collaboration, including the collaborative index, the degree of collaboration, the collaborative coefficient, the revised collaborative coefficient, and degree centrality. The empirical results showed that these measures all capture the notion of research collaboration, which is consistent with prior studies. Moreover, the results showed that degree centrality, the revised collaborative coefficient, and the degree of collaboration had the highest coefficient estimates on research productivity, the average JIF, and the average number of citations, respectively. Overall, this article suggests that the degree of collaboration and the revised collaborative coefficient are superior measures that can be applied to bibliometric studies for future researchers. 相似文献
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We analyze whether preferential attachment in scientific coauthorship networks is different for authors with different forms of centrality. Using a complete database for the scientific specialty of research about “steel structures,” we show that betweenness centrality of an existing node is a significantly better predictor of preferential attachment by new entrants than degree or closeness centrality. During the growth of a network, preferential attachment shifts from (local) degree centrality to betweenness centrality as a global measure. An interpretation is that supervisors of PhD projects and postdocs broker between new entrants and the already existing network, and thus become focal to preferential attachment. Because of this mediation, scholarly networks can be expected to develop differently from networks which are predicated on preferential attachment to nodes with high degree centrality. 相似文献
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顾东蕾 《中华医学图书馆杂志》2008,(6):7-10
药物基因组学是一门年轻的学科领域。该领域内相关科学研究工作者之间形成的科研合作关系网络,也具有类似许多大型合作关系网络数据库所具有的无尺度网络特性,其非连通合作网络内部具有较大连通组群的聚类特性和小世界特征等相关性质。 相似文献
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将复杂网络的研究方法引入到科研合作网络的研究中,为分析和评价科研人员提供了一个新途径.文章以合作网络为背景,微观深入地研究了科研合作网络中个体成员的网络生命特性,从全局网络和局部社团演化的角度定量分析个体网络生命过程对网络发展的影响.文章以大量的科技文献数据为实验数据集,以网络演化为线索跟踪个体成员的生命过程,同时考虑网络演化中的普通成员和核心成员对社团演化的不同影响,定量分析演化特性和个体网络生命过程.数据分析证实了科研团队要持续不断地发展,既要不断吸纳新成员为科研团队注入新活力,同时又要有相对稳定的中坚力量维持着团队的科研方向.更进一步地,文章的研究方法可以扩展到对其他社会组织分析,追踪分析相关组织的发展趋势及关键人物对组织演化的影响. 相似文献
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《Journal of Informetrics》2022,16(4):101345
The rapid growth of scientific collaboration and its significant role in promoting academic productivity has attracted increasing scientific community attention. The collaboration networks have become a powerful tool for studying scientific collaboration. Collaboration networks commonly used in research treat the collaborators as equal in status. However, the roles and contributions of different collaborators are not the same. Those differences are usually reflected through the signature order of academic achievements. This paper expands the construction of scientific collaboration networks with a directed collaboration network (DCN) to describe the different roles of collaborators and the connectivity and strength of collaborations. We analyzed the theoretical properties of the DCN and constructed evaluation indexes describing the diversity of collaboration order. Based on a case study of published papers in the business field, we discuss the value of the DCN in the characterization and evaluation of scientific collaboration and compare the DCN with two other collaboration networks. We found that the DCN provides a powerful new approach for investigating collaboration laws and patterns. 相似文献
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We analyze the advent and development of eight scientific fields from their inception to maturity and map the evolution of their networks of collaboration over time, measured in terms of co-authorship of scientific papers. We show that as a field develops it undergoes a topological transition in its collaboration structure between a small disconnected graph to a much larger network where a giant connected component of collaboration appears. As a result, the number of edges and nodes in the largest component undergoes a transition between a small fraction of the total to a majority of all occurrences. These results relate to many qualitative observations of the evolution of technology and discussions of the “structure of scientific revolutions”. We analyze this qualitative change in network topology in terms of several quantitative graph theoretical measures, such as density, diameter, and relative size of the network's largest component.To analyze examples of scientific discovery we built databases of scientific publications based on keyword and citation searches, for eight fields, spanning experimental and theoretical science, across areas as diverse as physics, biomedical sciences, and materials science. Each of the databases was vetted by field experts and is the result of a bibliometric search constructed to maximize coverage, while minimizing the occurrence of spurious records. In this way we built databases of publications and authors for superstring theory, cosmic strings and other topological defects, cosmological inflation, carbon nanotubes, quantum computing and computation, prions and scrapie, and H5N1 influenza. We also built a database for a classical example of “pathological” science, namely cold fusion. All these fields also vary in size and in their temporal patterns of development, with some showing explosive growth from an original identifiable discovery (e.g. carbon nanotubes) while others are characterized by a slow process of development (e.g. quantum computers and computation).We show that regardless of the detailed nature of their developmental paths, the process of scientific discovery and the rearrangement of the collaboration structure of emergent fields is characterized by a number of universal features, suggesting that the process of discovery and initial formation of a scientific field, characterized by the moments of discovery, invention and subsequent transition into “normal science” may be understood in general terms, as a process of cognitive and social unification out of many initially separate efforts. Pathological fields, seemingly, never undergo this transition, despite hundreds of publications and the involvement of many authors. 相似文献
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在系统调研跨地域科研协作现状基础上,本研究提出跨地域科研协作模式分析框架,以信息搜寻与信息检索融合（IS&R）等为测试主题,构建跨地域科研协作网络;计算无向加权科研协作网络节点中心性,发现各主题研究热点国家、城市和机构;模拟有向加权科研协作网络连接强度,描绘科研协作关系中知识流动方向;识别科研协作过程中节点角色,发掘城市科研协作主流模式;通过QAP分析,测度地理距离对节点间科研协作强度的影响,剖析节点科研实力与节点间科研协作强度的相关关系;借助演化分析,厘清科研协作网络发展历程及节点角色迁移情况。结果显示,上述主题在跨地域科研协作过程中既存在共性的节点分布、网络连接和扩展模式,又表现出一定的学科差异。图5。表11。参考文献23。 相似文献
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Many, if not most network analysis algorithms have been designed specifically for single-relational networks; that is, networks in which all edges are of the same type. For example, edges may either represent “friendship,” “kinship,” or “collaboration,” but not all of them together. In contrast, a multi-relational network is a network with a heterogeneous set of edge labels which can represent relationships of various types in a single data structure. While multi-relational networks are more expressive in terms of the variety of relationships they can capture, there is a need for a general framework for transferring the many single-relational network analysis algorithms to the multi-relational domain. It is not sufficient to execute a single-relational network analysis algorithm on a multi-relational network by simply ignoring edge labels. This article presents an algebra for mapping multi-relational networks to single-relational networks, thereby exposing them to single-relational network analysis algorithms. 相似文献