“人以群分”适用于大学生的学业成就吗——基于社会网络分析和Meta分析的方法

嵌入班级网络中的大学生在交往中形成不同的小群体,这些小群体间的学业成就是否具有显著性差异?研究者将社会网络分析方法中的整体网分析与元分析(Meta分析)相结合,在对大学生班级内小群体的学业成就差异进行多项独立研究的基础上,运用元分析技术将29项研究(N=1076)的结果予以综合分析,得到如下结论:(1)每个班级的大学生都在互动过程中形成了不同数量的小群体,且每个小群体中的学生数量不同;(2)大学生学业成就存在着“人以群分”现象,即同一班级的大学生在互动中产生的小群体在学业成就上存在显著性差异。因此,大学生应加强与高学业成就水平的小群体之间的互动以提高学业成就;而高校管理者也应采用科学的方法识别班级中的不同小群体,从而采取相应的学业成就提升策略。     

企业商务网站评价体系的理论研究

本文讨论了企业商务网站建设评价的目标，构建网站评价体系的功能、原则、难点。以及评价指标的具体选择等方面问题，并建立了三层企业商务网站指标体系。     

元数据在网络信息资源组织与检索中的作用

本文从元数据的定义、本质、结构入手，探讨了元数据在网络信息资源组织和检索方面的作用．并对元数据的未来发展趋势进行了分析。     

基于B/S模式的海量数据报表的实现

本文基于海量数据和网络打印速度之间的矛盾问题,通过几种报表实施方案的对比,提出了利用操纵Excel引擎方式解决网络打印海量数据报表问题的方法。以某煤矿企业考勤综合管理系统的考勤报表为应用背景,研究了基于B/S模式的海量数据报表的实现过程,讨论了客户端与服务器端的设置方式。应用证明该方法切实可行,可以有效地解决网络打印海量数据报表效率低的问题。     

网络安全的现状与展望

随着网络技术的飞速发展，网络安全问题日益重要。概述了网络安全所面临的威胁，探讨了我国网络安全存在的问题，并展望了我国网络安全的未来发展趋势。     

非线性规划法在供水管网优化中的应用研究

文章分析了非线性规划法的基本原理,并在此基础上建立供水管网优化模型。通过非线性规划模型的理论分析和实例计算,验证其在工程实践中的应用价值。非线性规划法在供水管网优化中的应用研究,为进一步改进管网优化设计方法提供了新的思路。     

浅析基于XML技术的计算机网络课件的设计

远程教学作为一种全新的教学方式,归根结底还是以学校的教学活动为基础,它应该包括学校教学活动的全部服务。本文在密切跟踪远程教学的发展方向——个性化网络教学的基础上,提出了基于XML技术的、非实时的、具有较好交互性的个性化远程教学系统的一种设计。     

中国数字化期刊网络系统比较分析

本文针对我国三大学术期刊网络检索系统从收录范围、全文阅读、标引质量、检索系统、检索效果等方面进行比较分析，指出我国数字化期刊网络系统还有待于完善和发展，并就此提出了自己的建议。     

Temporal bibliographic networks

We present two ways (instantaneous and cumulative) to transform bibliographic networks, using the works’ publication year, into corresponding temporal networks based on temporal quantities. We also show how to use the addition of temporal quantities to define interesting temporal properties of nodes, links and their groups thus providing an insight into evolution of bibliographic networks. Using the multiplication of temporal networks we obtain different derived temporal networks providing us with new views on studied networks. The proposed approach is illustrated with examples from the collection of bibliographic networks on peer review.     

Effect of class imbalance in heterogeneous network embedding: An empirical study

Network science has been extensively explored in solving various bibliometrics tasks such as Co-authorship prediction, Author classification, Author clustering, Author ranking, Paper ranking, etc. While majority of the past studies exploit homogeneous bibliographic network (consists of singular type of nodes and edges), in recent past there is a surge in using heterogeneous bibliographic entities and their inter-dependencies using heterogeneous information networks (HIN). Unlike homogeneous bibliographic networks, a bibliographic HIN consists of multi-typed nodes such as Author, Paper, Venue, etc. and corresponding relations. Thus bibliographic HIN is more complex and captures rich semantics of underlying bibliographic data as well as poses more challenges. Since a real-world HIN may have different number of instances for different node types, class imbalance is ubiquitous. Recent studies discuss class imbalance in brief and exploit meta-path-based strategies to address the issue. However, there is no work which quantitatively study the effect of class imbalance in regards to solving real-world bibliometrics tasks. Therefore, this paper first proposes a metric to estimate class imbalance in HIN and study the effects of class imbalance over two bibliometrics tasks, namely (i) Co-authorship prediction and (ii) Author's research area classification, using node features generated by network embedding-based frameworks for DBLP dataset. From various experimental analysis, it is evident that class imbalance in bibliographic HIN is an inherent characteristic and for better performance of the above-mentioned bibliometrics tasks, the bibliographic HINs must consider Author, Paper, and Venue as node types.