科研合作网络的知识扩散机理研究

知识扩散是知识生产过程中的核心环节,对知识继承和知识创新具有重要作用。本文结合知识在科研合作网络中的流动特征,引入复杂网络理论,构建知识扩散模型,模拟知识在科研合作网络中的扩散过程。考虑不同个体的知识自我增长以及知识吸收能力,采用网络平均知识水平、知识扩散速率、知识均衡程度等作为衡量知识扩散效果的评价指标,探究不同合作网络结构、知识遗传继承和知识变异重组与知识扩散的动态关系。研究显示:知识在合作网络中的知识水平、扩散速率、分布均衡程度很大程度上取决于网络拓扑结构的动态变化,网络的随机化程度越大,知识扩散的速度越快,知识的分布越均匀;合作网络的规模越小,专家高知识溢出效应越显著,越能促进知识的有效扩散;知识继承吸收和知识自我创新对知识扩散的影响在某一时刻可达到最佳均衡状态。研究合作网络中各影响因素对知识扩散的震荡作用,有利于形成更稳健的合作模式,发挥科研合作的最大效能。图9。表1。参考文献22。     

我国“985工程”高校科研合作网络研究

高校已经成为国家科技创新体系中的重要组成部分,随着科研活动集体化趋势不断增强,高校之间的科研合作日益增多,有关高校科研合作问题的研究具有重要的理论价值和现实意义。本文选取我国39所985工程高校作为研究样本,借鉴社会网络分析工具和方法,从整体网络分析、网络密度分析、核心-边缘结构分析、网络节点中心性分析等几个方面对高校之间的科研合作关系进行全方位、多角度的计量分析。研究结果表明,我国39所985工程高校之间已经初步建立了广泛的科研合作关系,但合作强度还有待于提高。另外,各个高校的发文量与其度数中心性存在显著的正相关关系,表明加强科研合作能够在一定程度上提高高校的科研产出数量。     

基于科研网络的潜在合作关系预测研究综述

[目的/意义]合理预测科研领域的潜在合作关系有助于优化资源配置,提升科研产出效率。从科研网络出发的潜在合作预测研究日益增长,需要系统总结。[方法/过程]在CNKI和Web of Science中检索并筛选出基于科研网络的潜在合作关系预测方法的研究,从年发文量、期刊分布对目标文献集进行统计分析。使用内容分析法,梳理出预测潜在合作关系的一般流程,描述步骤中的方法。[结果/结论]潜在合作关系预测一般流程为网络构建、特征提取与表示、合作预测和预测结果评价,其中构建的网络可分为同质网络、异质网络和二分网络,特征提取和表示可分为节点内容特征和网络结构特征,合作预测的方法主要有基于相似性的方法和基于机器学习的方法,预测结果评价的指标为AUC、Precision和Ranking Score;现有方法的局限性启示了未来潜在合作关系预测的发展方向。     

科研合作网络弹性研究与实证

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。     

复杂网络理论的情报学意义探讨——以科研合作网络和引文网络为例

文章选择复杂网络研究的科研合作网络和引文网络为切入点,探讨了它们与情报学三大经典定律的区别与联系,并从情报学的视角分析了复杂网络的性能指标有哪些情报学意义.     

作者科研合作网络模型与实证研究

基于科研论丈作者合作方式，建立一个作者科研合作网络模型。通过理论分析和仿真验证，网络模型节点的度分布（作者合作人数）符合幂率分布，该网络是一种无尺度网络模型。为了说明作者合作网络模型的有效性，对2001年1月至2006年12月期间发表在“图书情报工作》期刊上的科研论文进行统计，建立作者合作网络。对作者合作网络进行数据分析，结果与网络模型结论一致，因此该模型可以很好地描述作者合作网络的演化过程。     

科研合作网络中的模体涌现模型

基于科研论文的作者合作关系的演化特性,建立了一个科研合作网络的模体涌现模型.通过分析网络模型的内部特性,发现该类网络是一个无尺度网络,而且网络的模体度分布和节点连接强度分布具有相同的幂指数.最后应用计算机仿真实验和作者合作网络实证分析,发现实验结论和实证结论与模体涌现模型的理论分析结论一致,对本文结论提供了强有力的支持.     

情报学期刊科研论文与作者合作网络模型

基于情报学期刊科研论文信息,建立了一个作者科研合作网络模型.通过理论分析和仿真验证,网络模型节点的度分布(作者合作人数)符合幂率分布,该网络是一种无尺度网络模型.为了说明作者合作网络模型的有效性,对2001年1月至2006年12月期间发表在<情报学报>、<情报科学>、<现代图书情报技术>、<图书情报工作>期刊上的科研论文进行统计,分别建立了作者合作网络.根据实际数据计算出网络模型的参数设置,网络模型的计算机仿真和理论结果与实证统计数据结论一致.     

数据挖掘领域的科研合作网络分析

基于SCI和SSCI数据库中以“数据挖掘”为主题的文献题录信息,构建三个科研合作网络（高校间、公司间、国家间）,利用社会网络分析方法对这三个不同类型的网络特征进行对比分析。结果显示,数据挖掘领域的研究成果涉及众多研究方向,不同的机构实体有不同的研究重点,所构建的三个不同类型的科研合作网络在诸多网络特征上存在较大的差异,包括合作网络的密度、节点的平均度、最大成分的平均最短路径、最大成分的比重等。最后对部分高校与公司的研究重点进行具体分析。
     

Measuring contextual partner importance in scientific collaboration networks

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.     

Convexity in scientific collaboration networks

Convexity in a network (graph) has been recently defined as a property of each of its subgraphs to include all shortest paths between the nodes of that subgraph. It can be measured on the scale [0, 1] with 1 being assigned to fully convex networks. The largest convex component of a graph that emerges after the removal of the least number of edges is called a convex skeleton. It is basically a tree of cliques, which has been shown to have many interesting features. In this article the notions of convexity and convex skeletons in the context of scientific collaboration networks are discussed. More specifically, we analyze the co-authorship networks of Slovenian researchers in computer science, physics, sociology, mathematics, and economics and extract convex skeletons from them. We then compare these convex skeletons with the residual graphs (remainders) in terms of collaboration frequency distributions by various parameters such as the publication year and type, co-authors’ birth year, status, gender, discipline, etc. We also show the top-ranked scientists by four basic centrality measures as calculated on the original networks and their skeletons and conclude that convex skeletons may help detect influential scholars that are hardly identifiable in the original collaboration network. As their inherent feature, convex skeletons retain the properties of collaboration networks. These include high-level structural properties but also the fact that the same authors are highlighted by centrality measures. Moreover, the most important ties and thus the most important collaborations are retained in the skeletons.     

科研合作网络的可视化及其在文献检索服务中的应用

科学研究论文作者合作研究所形成的网络具有复杂性网络的特征,对于复杂网络的可视化展现可以利用可视化技术的交互性、多维性和可视性生动、形象地展示出复杂网络的种种特性。本文则基于万方数据生物医学期刊(1062种,150万篇)作者在合作研究中形成的复杂网络可视化后应用在文献检索的结果展现上,为复杂网络及可视化在文献检索的增值服务做出了有意的探索。     

基于科学数据的合作网络研究——以ClinicalTrials.gov临床试验数据为例

[目的/意义]基于科学数据构建合作网络,并与传统出版物合作网络进行比较,从网络分析层面解读两个合作网络的差异,为科学数据管理工作提供借鉴。[方法/过程]以ClinicalTrials.gov网站的临床科学数据库为例,利用爬虫抓取该网站上传统论文题录信息以及临床试验信息的元数据并分别构建合作网络,通过复杂网络分析比较试验合作机构网络与论文合作机构网络之间的异同。[结果/结论]基于科学数据集和论文数据集的元数据构建的合作网络,与仅从论文数据集中提取元数据构建的网络相比,前者能够展现更丰富准确的合作信息,从而揭示科学数据管理和开放共享的重要性。     

Predicting scientific research trends based on link prediction in keyword networks

The rapid development of scientific fields in this modern era has raised the concern for prospective scholars to find a proper research field to conduct their future studies. Thus, having a vision of future could be helpful to pick the right path for doing research and ensuring that it is worth investing in. In this study, we use article keywords of computer science journals and conferences, assigned by INSPEC controlled indexing, to construct a temporal scientific knowledge network. By observing keyword networks snapshots over time, we can utilize the link prediction methods to foresee the future structures of these networks. We use two different approaches for this link prediction problem. First, we have utilized three topology-based link prediction algorithms, two of which are commonly used in literature. We have also proposed a third algorithm based on nodes (keywords) clustering coefficient, their centrality measures like eigenvector centrality, and nodes community information. Then, we used nodes topological features and the outputs of aforementioned topology-based link prediction algorithms as features to feed five machine learning link prediction algorithms (SVM, Random Forest Classifier, K-Nearest Neighbors, Gaussian Naïve Bayes, and Multinomial Naïve Bayes). All tested predictors have shown considerable performance and their results are discussed in this paper.     

International collaboration in science and the formation of a core group

International collaboration as measured by co-authorship relations on refereed papers grew linearly from 1990 to 2005 in terms of the number of papers, but exponentially in terms of the number of international addresses. This confirms Persson et al.'s [Persson, O., Glänzel, W., & Danell, R. (2004). Inflationary bibliometrics values: The role of scientific collaboration and the need for relative indicators in evaluative studies. Scientometrics, 60(3), 421–432] hypothesis of an inflation in international collaboration. Patterns in international collaboration in science can be considered as network effects, since there is no political institution mediating relationships at that level except for the initiatives of the European Commission. Science at the international level shares features with other complex adaptive systems whose order arises from the interactions of hundreds of agents pursuing self-interested strategies. During the period 2000–2005, the network of global collaborations appears to have reinforced the formation of a core group of fourteen most cooperative countries. This core group can be expected to use knowledge from the global network with great efficiency, since these countries have strong national systems. Countries at the periphery may be disadvantaged by the increased strength of the core.     

Promoting collaboration: the role of relational multiplexity in an interorganizational health justice network

Organizations need to collaborate to achieve complex goals. Although interorganizational relations often take the form of multiplex ties, our understanding of how multiplexity itself may facilitate interorganizational collaboration is limited. We use dynamic network analysis (SIENA) to test the role of relational multiplexity – specifically, relationships involving communication outside of coalition meetings and expertise-seeking – in promoting collaboration in a health justice coalition over three years. The results offer strong support for the role of multiplexity in the formation of interorganizational collaboration, indicating that having multiple ties between organizations facilitates collaboration, and that certain types of ties (i.e. communication relationships outside of coalition meetings) are more influential than others. We conclude that coalitions hoping to support successful interorganizational collaboration will benefit from offering opportunities for member organizations to communicate outside of group meetings, because such small acts of dyadic interaction can build into deeper levels of engagement. Additionally, our study demonstrates how network analysis can help organizational coalitions to track and suggest potential partnerships between member organizations.