Dynamic Co-authorship Network Analysis with Applications to Survey Metadata

University essay from Linnéuniversitetet/Institutionen för datavetenskap och medieteknik (DM)

Abstract: Co-authorship networks are a particular sort of social networks representing authors collaborating on joint publications. Such networks are studied within the fields of bibliometrics and scientometrics. While it is possible to analyze co-authorship networks in their entirety, certain analytical tasks would benefit from representing such networks as dynamic graphs, which incorporate a temporal dimension and capture structural transformations unfolding over time. The importance of dynamic graphs has emerged in recent years, in graph theory at large as well as within application domains such as social sciences, for instance.Research regarding dynamic graphs has been identified as one of the major challenges within network theory since they are particularly useful for describing real-world systems.This thesis project revolves around dynamic co-authorship network analysis algorithms, which aim to extract various temporal aspects regarding author collaborations.It is the result of a proposal by the ISOVIS group at Linnaeus University, which is active within the fields of exploratory data analysis and information visualization, including the problem of visual analysis of scientific publication data. The algorithms developed in this project extract analytical data such as (1) joint publications among pairs of authors, (2) temporal trends on connected components (groups of authors) along with network centrality measurements, and (3) major events regarding emergence, mergers, and splits of connected components over time. Together with domain experts, the analysis regarding usability, performance, and scalability of the algorithms took place as part of the evaluation process to assure that the result met the needs which instigated this thesis project. The application of the algorithms on real data sets provided by the ISOVIS group was useful concerning the evaluation of the usability domain. In contrast, customized synthetic data sets was an excellent tool for evaluating performance and scalability.

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