@PHDTHESIS\{IMM2007-05124,
    author       = "S. L. J{\o}rgensen",
    title        = "The Structure of Complex Networks",
    year         = "2007",
    school       = "Informatics and Mathematical Modelling, Technical University of Denmark, {DTU}",
    address      = "Richard Petersens Plads, Building 321, {DK-}2800 Kgs. Lyngby",
    type         = "",
    note         = "Supervised by Prof. Lars Kai Hansen, {IMM,} {DTU}.",
    url          = "http://www2.compute.dtu.dk/pubdb/pubs/5124-full.html",
    abstract     = "This dissertation regards the structure of large complex networks. The dissertation is divided into three main parts. Part I contains chapters 1–3. Part {II} contains chapters 4–6. Part {III} consists of the concluding chapter 7 and the bibliography.

Part I serves as an introduction. Chapters 1 and 2 directed toward enabling a general reader to understand the concepts and nomenclature used in the research, presented in Part {II} of the dissertation. These chapters also motivate and explain the unifying idea behind the research contained in this dissertation. Chapter 3 describes the origin and general structure of the data set used in the subsequent chapters.

Part {II} contains five papers that chronicle my research as a Ph.D.-student.

• Chapter 4 consists of two papers: Life, Death, and Preferential Attachment [54] and Live and Dead Nodes [53] where a mathematical model of the network of scientific papers is motivated empirically and solved analytically. The model is an augmentation of the growing networks model, first introduced by Barab\'{a}si and Albert [10]. In [53,54], the idea that network nodes can ‘die’, is introduced, and the associated consequences for the growth model are explored. Further, it is demonstrated that the mechanism for ‘node-death’, alone, can create networks with power-law degree distributions.

• Chapter 5 concerns the longitudinal correlations, in the citation network, that is induced by the authors of the scientific papers. This chapter also contains two papers: Measures for Measures [56] and A Quantitative Analysis of Measures of Quality [57]. Here, Bayesian statistics are employed to analyze several different measures of quality. Using scaling arguments, it is demonstrated how many papers are needed to draw conclusions, regarding long-term scientific performance with usefully small statistical uncertainties. Further, the approach described here permits the value-free (i.e., statistical) comparison of scientists working in distinct areas of science.

• Chapter 6 discusses the detection of communities in large networks, using deterministic mean field methods. Further, the paper, Deterministic Community Detection [52] presents an analytical analysis of a simple class of random networks, with adjustable community structure.

Part {III} consists of a final, concluding chapter that recapitulates the main ideas, presented in this dissertation, and points towards new avenues for further research. Additionally, part {III} also contains the bibliography."
}