共查询到16条相似文献，搜索用时 250 毫秒
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北京师范大学图书借阅系统的网络分析 总被引：2，自引：0，他引：2
本文通过对2005年北京师范大学图书借阅记录的分析,构建了单顶点网和二分网两种网络,给出了图书借阅数据在这两个网络上的度分布,通过比较得到这两个网络的度分布具有时间演化不变性.然后在单顶点网络上抽取了部分数据进行分析,发现各子网络的平均路径长度L和网络的平均集聚系数C随网络规模增大总体上有减小的趋势;并且网络具有相对较高的平均集聚系数、较小的"平均最短路径长度",表现出明显的"小世界效应".最后在二分网上基于同类节点的相似性提出了基于三元组的集聚系数的定义,给出了该集聚系数在北师大图书借阅网络上的分布. 相似文献
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国外网络信息计量学领域合作网络特性分析 总被引：1，自引：0，他引：1
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科研合作网络中的模体涌现模型 总被引：1，自引：1，他引：0
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. 相似文献
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[目的/意义]探索领域知识发展过程中的聚类演化问题有助于揭示知识聚类的特征和规律,对于掌握知识生长演进过程中关联知识的聚集具有重要意义。[方法/过程]以复杂网络的思想为基础,基于标签邻接关系的发生值构建时间序列领域知识网络。即依据网络模体的理论,采用网络聚类系数的分析方法,对领域知识网络进行动态跟踪与分析;结合网络密度、特征路径长度、节点度值、封闭三元组等指标,从随机因素、度相关性、邻近关联3个方面对领域知识发展过程中的聚类演化现象进行分析。[结果/结论]研究结果表明：①领域知识在发展进程中始终保持较高的聚类性;②领域知识的聚类性同时包含随机性与结构性（非随机性）两方面因素; ③领域知识聚类的动态状态在小世界网络和无标度网络之间摇摆演化; ④领域知识的聚类状态在网络全局和局部节点之间表现出一定的差异性。 相似文献
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为揭示Wiki系统中知识网络的内在特性与演化规律,从知识协同视角出发,以Wikipedia(维基百科)为研究对象,结合复杂网络理论,采用实证研究方法,在构建Wiki系统有向的词条网络和主题参考网络基础上,对两个网络的节点度与度分布、平均路径长度、聚类系数等方面进行了实证分析.实证结果表明Wiki知识网络的入度服从幂率分布,出度服从广延指数分布,网络具有异配性和小世界效应等特性,且词条网络比主题参考网络具有更强的扩散能力和更快的传播速度.根据揭示的Wiki知识网络的特性,提出了更好地利用Wiki系统支持知识协同与共享的相关建议与策略. 相似文献
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[目的/意义]旨在研究肿瘤疫苗领域技术创新合作网络演化特征,为我国肿瘤疫苗领域的技术突破和产业发展提供思路。[方法/过程]提出一种产业技术创新合作网络演化特征分析方法和框架。首先引入专利指标法,通过技术生长率、技术成熟系数、技术衰老系数和新技术特征系数等指数的定量计算对肿瘤疫苗产业技术生命周期进行划分;其次利用社会网络分析法构建技术生命周期各阶段合作网络,从节点数、网络密度、中心势、核心-边缘分析等维度对合作网络的拓扑结构演化规律进行分析;从中心度、凝聚子群分析、创新机构等维度对核心个体演化规律进行分析,进而揭示肿瘤疫苗产业技术创新合作网络的动态演化特征。[结果/结论]以肿瘤疫苗战略性新兴产业为案例进行研究,验证方法的有效性和可行性,为肿瘤疫苗领域技术发展战略和技术合作提供理论依据。 相似文献
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高层次科技人才学术合作网络研究 总被引：1，自引：0，他引：1
对2009 年获得自然科学奖项目主要完成人的学术合作进行调研,采用社会网络分析方法,对主要完成人之间
的合作关系、SCI 论文合著情况、合作的规模和范围进行分析,并通过合作网络平均路径和直径、网络合作度和度分布、
聚类系数、网络密度和中心性、无标度特性等定量学术合作网络特性分析,得出以下结论：重大科学发现需要较长时间
的沉淀;整体合作网络为松散网络,合作以机构内为主,随着地域的扩展逐渐减弱;学术合作网络存在三八原则;合作
网络度服从幂率分布。提出重视团队建设,鼓励合作,以及科研机构和科技人才不能急功近利, 相似文献
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How does the collaboration network of researchers coalesce around a scientific topic? What sort of social restructuring occurs as a new field develops? Previous empirical explorations of these questions have examined the evolution of co-authorship networks associated with several fields of science, each noting a characteristic shift in network structure as fields develop. Historically, however, such studies have tended to rely on manually annotated datasets and therefore only consider a handful of disciplines, calling into question the universality of the observed structural signature. To overcome this limitation and test the robustness of this phenomenon, we use a comprehensive dataset of over 189,000 scientific articles and develop a framework for partitioning articles and their authors into coherent, semantically related groups representing scientific fields of varying size and specificity. We then use the resulting population of fields to study the structure of evolving co-authorship networks. Consistent with earlier findings, we observe a global topological transition as the co-authorship networks coalesce from a disjointed aggregate into a dense giant connected component that dominates the network. We validate these results using a separate, complimentary corpus of scientific articles, and, overall, we find that the previously reported characteristic structural evolution of a scientific field's associated co-authorship network is robust across a large number of scientific fields of varying size, scope, and specificity. Additionally, the framework developed in this study may be used in other scientometric contexts in order to extend studies to compare across a larger range of scientific disciplines. 相似文献
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作者合著网络中研究兴趣相似性实证研究 总被引：2，自引：0，他引：2
[目的/意义]从作者微观个体研究兴趣角度出发,通过对作者合著网络中作者关联关键词集的研究,定量地验证研究兴趣相似是作者合作的一个动机。[方法/过程]收集WOS中检索领域相关文献题录信息,构建作者合著网络,并利用Louvain算法划分社区,实现了Jaccard系数及余弦相似性系数的计算指标,统计与对比分析整体网络及社区内部作者研究兴趣的相似性。[结果/结论]在网络整体层次,作者合著网络中作者的研究兴趣相似性较高,但也存在一定比例的差异性即互补性;在科研社区内部,合著作者平均研究兴趣相似性及互补性均高于网络整体层次,科研社区的形成受到作者研究兴趣的影响。两个层次的兴趣相似性反映了研究兴趣相似是作者合作的一个重要动机。 相似文献
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Research topics and research communities are not disconnected from each other: communities and topics are interwoven and co-evolving. Yet, scientometric evaluations of topics and communities have been conducted independently and synchronically, with researchers often relying on homogeneous unit of analysis, such as authors, journals, institutions, or topics. Therefore, new methods are warranted that examine the dynamic relationship between topics and communities. This paper examines how research topics are mixed and matched in evolving research communities by using a hybrid approach which integrates both topic identification and community detection techniques. Using a data set on information retrieval (IR) publications, two layers of enriched information are constructed and contrasted: one is the communities detected through the topology of coauthorship network and the other is the topics of the communities detected through the topic model. We find evidence to support the assumption that IR communities and topics are interwoven and co-evolving, and topics can be used to understand the dynamics of community structures. We recommend the use of the hybrid approach to study the dynamic interactions of topics and communities. 相似文献
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This work maps and analyses cross-citations in the areas of Biology, Mathematics, Physics and Medicine in the English version of Wikipedia, which are represented as an undirected complex network where the entries correspond to nodes and the citations among the entries are mapped as edges. We found a high value of clustering coefficient for the areas of Biology and Medicine, and a small value for Mathematics and Physics. The topological organization is also different for each network, including a modular structure for Biology and Medicine, a sparse structure for Mathematics and a dense core for Physics. The networks have degree distributions that can be approximated by a power-law with a cut-off. The assortativity of the isolated networks has also been investigated and the results indicate distinct patterns for each subject. We estimated the betweenness centrality of each node considering the full Wikipedia network, which contains the nodes of the four subjects and the edges between them. In addition, the average shortest path length between the subjects revealed a close relationship between the subjects of Biology and Physics, and also between Medicine and Physics. Our results indicate that the analysis of the full Wikipedia network cannot predict the behavior of the isolated categories since their properties can be very different from those observed in the full network. 相似文献