Interdisciplinary Research (IDR) has received a lot of attention from academics, policy-makers, and decision-makers alike. RCUK invests £3 billion in research grants each year (RCUK 2017); half of the grants are provided to investigators who hail from different departments. There is mounting awareness of the challenges facing IDR, and a large body of literature trying to establish how IDR can be analysed (Davidson 2015, Yegros-Yegros, Rafols et al. 2015). Of these, the majority have been qualitative studies and it has been noticed that there is a distinct lack of quantitative studies that can be used to identify how to enable IDR. The literature shows that many of the barriers to IDR can be classified as either cultural or administrative (Katz and Martin 1997, Cummings and Kiesler 2005, Rafols 2007, Wagner, Roessner et al. 2011), neither of which are easily changed over a short period of time. The perspective taken in this research is that change can be affected by enabling the individuals who conduct IDR. Herein lies the main challenge; how can these future leaders of IDR be identified so that they can be properly supported. No existing datasets were deemed suitable for the purpose, and a new dataset was created to analyse IDR. To isolate dynamics within an organisation, hard boundaries were drawn around research-organisations. The University of Bath journal co-authorship dataset 2000-2017 was determined to be suitable for this purpose. From this dataset a co-authorship network was created. To analyse this, established models from literature were adapted and used to identify differences in disciplinary and interdisciplinary archetypes. This was done through a correlational study. No statistically significant differences between such author archetypes were found. It was therefore concluded that an alternative approach was necessary. By adapting the networks framework to account for different types of links between edges, a multilayer perspective was adopted. This resulted in a rank-3 tensor, node-aligned framework being proposed, allowing disciplines to be represented in the network. By using this framework to construct the University of Bath multiplex co-authorship network, an exemplar structure was established through use of a series of proposed structural metrics. A growth model was proposed and successfully recreated the structure and thereby uncovered mechanics affecting real-world multiplex networks. This highlighted the importance of node entities and the layer closeness centrality. This implies that it is very difficult to carry over benefits across disciplines, and that some disciplines are better suited to share and adapt knowledge than others. The growth model also allowed an analytical expression for the rate of change of disciplinary degree, thereby providing a model for who is most likely to enable and sustain IDR.

This thesis focuses on a new graphon-based approach for fitting models to large networks and establishes a general framework for incorporating nodal attributes to modeling. The scale of network data nowadays, renders classical network modeling and inference inappropriate. Novel modeling strategies are required as well as estimation methods. Depending on whether the model structure is specified a priori or solely determined from data, existing models for networks can be classified as parametric and non-parametric. Compared to the former, a non-parametric model often allows for an easier and more straightforward estimation procedure of the network structure. On the other hand, the connectivities and dynamics of networks fitted by non-parametric models can be quite difficult to interpret, as compared to parametric models. In this thesis, we first propose a computational estimation procedure for a class of parametric models that are among the most widely used models for networks, built upon tools from non-parametric models with practical innovations that make it efficient and capable of scaling to large networks. Extensions of this base method are then considered in two directions. Inspired by a popular network sampling method, we further propose an estimation algorithm using sampled data, in order to circumvent the practical obstacle that the entire network data is hard to obtain and analyze. The base algorithm is also generalized to consider the case of complex network structure where nodal attributes are involved. Two general frameworks of a non-parametric model are proposed in order to incorporate nodal impact, one with a hierarchical structure, and the other employs similarity measures. Several simulation studies are carried out to illustrate the improved performance of our proposed methods over existing algorithms. The proposed methods are also applied to several real data sets, including Slashdot online social networks and in-school friendship networks from the National Longitudinal Study of Adolescent to Adult Health (AddHealth Study). An array of graphical visualizations and quantitative diagnostic tools, which are specifically designed for the evaluation of goodness of fit for network models, are developed and illustrated with these data sets. Some observations of using these tools via our algorithms are also examined and discussed.

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Craft's role in its traditional sites is changing. Using Actor-network theory the thesis explores this contention in three sites of craft representation, a craft organisation, a university craft workshop and a craft exhibition. It was found that although craft remained in the operations and practices of these sites it was transformed contextually to perform roles beyond the skills of the hand and the material limits of the object. The research, summed up as The Hidden Hand and the Fluid Object, is significant for craft organisation, craft education and museum administration. The thesis also expands the applications of Actor-network theory as a research tool beyond its roots in science. / thesis (PhDEducation)--University of South Australia, 2004.

iii, 122 leaves : ill., tables ; 30 cm. / Title page, contents and abstract only. The complete thesis in print form is available from the University Library. / Thesis (Ph.D.)--University of Adelaide, Dept. of Applied Mathematics, 1977

69 leaves : tables ; 30 cm. / Title page, contents and abstract only. The complete thesis in print form is available from the University Library. / Thesis (Ph.D.)--University of Adelaide, Dept. of Applied Mathematics, 1979

Information networks represent relations in data, relationships typically ignored in iid (independent and identically distributed) data. Such networks abound, like coauthorships in bibliometrics, cellphone call graphs in telecommunication, students interactions in Education, etc. A large body of work has been devoted to the analysis of these networks and the discovery of their underlying structure, specifically, finding the communities in them. Communities are groups of nodes in the network that are relatively cohesive within the set compared to the outside. This thesis proposes Top Leaders, a fast and accurate community mining approach for both weighted and unweighted networks. Top Leaders regards a community as a set of followers congregating around a potential leader and works based on a novel measure of closeness inspired by the theory of diffusion of innovations. Moreover, it proposes Meerkat-ED, a specific and practical toolbox for analyzing students interactions in online courses. It applies social network analysis techniques including community mining to evaluate participation of students in asynchronous discussion forums.

Thesis (master's)--Dalhousie University (Canada), 2003. / Adviser: Uday Venkatadri. Includes bibliographical references.