Dynamics in large scale networks

In this thesis we study the properties of two large dynamic networks, the competition network of advertisers on the Google and Bing search engines and the dynamic network of friend relationships among avatars in the massively multiplayer online game (MMOG) Planetside 2. We are particularly interested in removal patterns in these networks. Our main finding is that in both of these networks the nodes which are most commonly removed are minor near isolated nodes. We also investigate the process of merging of two large networks using data captured during the merger of servers of Planetside 2. We found that the original network structures do not really merge but rather they get gradually replaced by newcomers not associated with the original structures. In the final part of the thesis we investigate the concept of motifs in the Barabási-Albert random graph. We establish some bounds on the number of motifs in this graph.

A Scalability Study and New Algorithms for Large-Scale Many-Objective Optimization

Many real-world optimization problems contain multiple (often conflicting) goals to be optimized concurrently, commonly referred to as multi-objective problems (MOPs). Over the past few decades, a plethora of multi-objective algorithms have been proposed, often tested on MOPs possessing two or three objectives. Unfortunately, when tasked with solving MOPs with four or more objectives, referred to as many-objective problems (MaOPs), a large majority of optimizers experience significant performance degradation. The downfall of these optimizers is that simultaneously maintaining a well-spread set of solutions along with appropriate selection pressure to converge becomes difficult as the number of objectives increase. This difficulty is further compounded for large-scale MaOPs, i.e., MaOPs possessing large amounts of decision variables. In this thesis, we explore the challenges of many-objective optimization and propose three new promising algorithms designed to efficiently solve MaOPs. Experimental results demonstrate the proposed optimizers to perform very well, often outperforming state-of-the-art many-objective algorithms.

Hidden Motives: An Analysis of Online ESL Teacher Hiring Practices in Japan and Hong Kong

Hidden Motives: An Analysis of Online English as a Second Language (ESL) Teacher Hiring Practices in Japan and Hong Kong is a qualitative research paper examines and compares two large-scale Asian English language teaching programs: Japan’s Japan Exchange and Teaching (JET) Programme (JET Programme, 2010) and Hong Kong’s Native-speaking English Teacher (NET) Scheme (NET Scheme, 2013). Both government sponsored programs recruit internationally and invite participants to work within each country’s public schools while living amongst local communities and both programs utilize their online presence to attract, inform, and recruit individuals. The purpose of this research is to investigate whether the JET and NET websites are transparent with their governmental motives aside from improving their students’ English language abilities. While JET and NET websites were interrogated, the research questions were regularly revisited to determine if the two sites made any underlying motives clear to the candidates. The research, supported by academic literature, exposed the JET Programme website to be a branch of the Japanese government’s soft power campaign, whereby JET teachers were hired firstly as potential advocates for Japan and Japanese culture rather than English teachers. Conversely, the NET Scheme appeared to be solely commissioned for English language improvement as reflected by their website. Findings from the research can provide insight to applicants to help them decide if they want to participant in these programs. Without clearly understanding the background that motivates these programs, participants may unknowingly be used to support the host government’s agendas.