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首页> 外文学位 >Missing links: Predicting interactions based on a multi-relational network structure with applications in informetrics.
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Missing links: Predicting interactions based on a multi-relational network structure with applications in informetrics.

机译：缺少链接：预测基于多关系网络结构的交互以及信息计量学中的应用。

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摘要

The aim of this dissertation is to develop methods to predict new or missing interactions based on information in the form of a multi-relational network. It is based on two case studies of collaboration between researchers: collaboration at a mid-sized institute (the University of Antwerp) and collaboration within a research specialty (informetrics). The case studies can be interpreted as networks, such that each node represents an author and each link represents co-authorship.;After providing a general overview of methods and techniques in (social) network analysis, we introduce Q-measures, indicators that characterise the extent to which a node plays a bridging role between groups in the network.;When viewed dynamically (over time), networks evolve. The link prediction problem is the question to what extent changes in link structure can be predicted. We introduce a general framework for link prediction, consisting of five steps. Empirical examination reveals that path-based predictors offer the best overall performance and that weighted predictors typically perform worse than their unweighted counterparts. Furthermore, we show that network reconstruction (reconstructing a damaged network) can be used to assess a method's applicability to link prediction.;In a following step, we broaden our approach to multi-relational (or 'semantic') networks, i.e. networks that consist of links and nodes of different types. We discuss how these concepts are related to semantics and provide an introduction to Semantic Web technologies. This raises the question whether an RDF-based knowledge representation can be created that is suitable for informetric research. We discuss the main difficulties and argue that existing ontologies provide the bulk of the vocabulary needed for informetrics. Where necessary, we suggest additions to the existing infrastructure, e.g. to capture term usage.;We show that the bipartite author–paper network is a better training network than the derived co-authorship network for specific predictors. Next, we substitute the author–paper network with an author–term network. Finally, we introduce the association network model, a general model that can be used to identify and study patterns of interest in a multi-relational network. This model's main value lies in the context of recommendation.
机译：本文的目的是开发一种基于信息的多关系网络来预测新的或缺少的交互的方法。它基于研究人员之间合作的两个案例研究：中型机构（安特卫普大学）的合作和研究专业内的合作（信息计量）。案例研究可以解释为网络，每个节点代表一个作者，每个链接代表共同作者。在对（社会）网络分析的方法和技术进行了总体概述之后，我们介绍了Q度量，这些度量是表征节点在网络中各组之间起桥接作用的程度。；动态（随时间推移）观察时，网络会不断发展。链接预测问题是可在多大程度上预测链接结构变化的问题。我们介绍了一个用于链接预测的通用框架，该框架包含五个步骤。实证检验表明，基于路径的预测变量可提供最佳的整体性能，而加权的预测变量通常比未加权的预测变量表现更差。此外，我们证明了网络重建（重建受损的网络）可用于评估方法对链接预测的适用性。在接下来的步骤中，我们将方法扩展到多关系（或“语义”）网络，即由不同类型的链接和节点组成。我们讨论了这些概念如何与语义相关，并提供了语义Web技术的介绍。这就提出了一个问题，即是否可以创建基于RDF的知识表示形式，以适合于信息学研究。我们讨论了主要的困难，并认为现有的本体论提供了信息计量学所需的大量词汇。如有必要，我们建议对现有基础架构进行补充，例如来捕获术语使用情况。；我们证明，针对特定预测变量，两方作者-论文网络比派生的共同作者网络是更好的培训网络。接下来，我们将作者-论文网络替换为作者-术语网络。最后，我们介绍了关联网络模型，这是一个通用模型，可用于识别和研究多关系网络中感兴趣的模式。该模型的主要价值在于推荐。

著录项

  • 作者

    Guns, Raf.;

  • 作者单位

    Universiteit Antwerpen (Belgium).;

  • 授予单位 Universiteit Antwerpen (Belgium).;
  • 学科 Library Science.;Information Science.;Computer Science.
  • 学位 Ph.D.
  • 年度 2012
  • 页码 297 p.
  • 总页数 297
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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