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.

Exploring Healthcare Experiences of Transgender Individuals

The purpose of this study was to explore how transgender individuals were supported to navigate the healthcare system to achieve positive healthcare experiences. A single case study was conducted in Southern Ontario, which included ten individual interviews. Data was analyzed through thematic analysis, allowing for seven themes to emerge within macro (large-scale system), meso (local/interpersonal), and micro (individual/internal) levels of healthcare system support. Themes that emerged within the levels of system support included: 1) existing deficits with hope for change; 2) significant external supports; 3) importance of informal networking; 4) support from local area family physicians and walk-in clinics; 5) navigating the healthcare system alone; 6) personality traits for successful healthcare experiences; and 7) the development of strategies to achieve positive healthcare experiences. This study outlined factors that contributed to positive healthcare experiences for transgender individuals, showing that meso and micro level support are compensating for large-scale healthcare system deficits.

Genetic programming for the RoboCup Rescue Simulation System

The Robocup Rescue Simulation System (RCRSS) is a dynamic system of multi-agent interaction, simulating a large-scale urban disaster scenario. Teams of rescue agents are charged with the tasks of minimizing civilian casualties and infrastructure damage while competing against limitations on time, communication, and awareness. This thesis provides the first known attempt of applying Genetic Programming (GP) to the development of behaviours necessary to perform well in the RCRSS. Specifically, this thesis studies the suitability of GP to evolve the operational behaviours required of each type of rescue agent in the RCRSS. The system developed is evaluated in terms of the consistency with which expected solutions are the target of convergence as well as by comparison to previous competition results. The results indicate that GP is capable of converging to some forms of expected behaviour, but that additional evolution in strategizing behaviours must be performed in order to become competitive. An enhancement to the standard GP algorithm is proposed which is shown to simplify the initial search space allowing evolution to occur much quicker. In addition, two forms of population are employed and compared in terms of their apparent effects on the evolution of control structures for intelligent rescue agents. The first is a single population in which each individual is comprised of three distinct trees for the respective control of three types of agents, the second is a set of three co-evolving subpopulations one for each type of agent. Multiple populations of cooperating individuals appear to achieve higher proficiencies in training, but testing on unseen instances raises the issue of overfitting.

The green movement in Canada

The green movement has evolved over the last twenty years from various social, peace and ecology activist organizations into direct political participation in parliamentary institutions through the Green Party. Although there is no definable theory of green politics, the culmination of interacting social movements as well as feminist, decentralist and in many cases, left wing political ideology, has produced a specific kind of political direction for the Greens internationally. As a result of the increased a ttention and awareness given to ecological issues , combined with the heightened evidence of large scale environmental deterioration, public attitudes and government decisions on development and natural resource management have been significantly altered. The Green Party of Canada is still r elatively young in comparison to its European counterparts, although ecologica l awareness and interest in t he green movement in Canada is strong, as reflected not only in support on a political level for the Canadian Greens I but for environmental issues and a ctivism in general. For this reason it s important to determine whether or not the Green Party is a significant aspect of the Canadian green movement, and if in fact its representation is necessary as an active participant in the Canadian political system . The Green Party of Canada, as a vital aspect of the Canadian green movement, and its connection to international green organizations can be examined primarily through the examp l es of both the Canadian Greens and the Green party of Ontario , by using original party documents and literature, information gained through Green party meetings and discussions with members, and commentary by Green theorists where app licable. As well, the influence on the Canadian green movement by the German Green Party is outlined , again mainly through party literature, documents and critiques of the party's experiences. This study reveals several existing and potential problems fo r t he Green Party in Canada, and the political fut ure of the Canadian green movement in general. Some, such as the real i ties of the Canadian political system are external to the movement, and may be overcome with adjustments in goals and methods, and a realization of the changing attitude towards environmental issues in a political context . On the other hand, internal party disfunctions in both organization and direction, caused mainly by the indefinite parameters of green ideology, threaten to exploit the already problematic aspects evident in t he Green Party . Aside from its somewhat slow beginnings, the Green Party in Canada has developed into a strong grassroots social movement, not however from its political visibility but from the steady growth in the popul ari ty of ecological pol i t ics in Canada. Due to the seeming enormity of the obstacles facing the Greens in their effort 4 to achieve electoral success, it is doubtful that Parliamentary representation will be achieved without a major re-orientation of party organization and methods. UI timately the strength of the Green Party in Canada will be based upon its ability to survive as a significant movement, and its willingness to continue to challenge political thought and practice.

Heuristics for the Critical Node Detection Problem in Large Complex Networks

Complex networks have recently attracted a significant amount of research attention due to their ability to model real world phenomena. One important problem often encountered is to limit diffusive processes spread over the network, for example mitigating pandemic disease or computer virus spread. A number of problem formulations have been proposed that aim to solve such problems based on desired network characteristics, such as maintaining the largest network component after node removal. The recently formulated critical node detection problem aims to remove a small subset of vertices from the network such that the residual network has minimum pairwise connectivity. Unfortunately, the problem is NP-hard and also the number of constraints is cubic in number of vertices, making very large scale problems impossible to solve with traditional mathematical programming techniques. Even many approximation algorithm strategies such as dynamic programming, evolutionary algorithms, etc. all are unusable for networks that contain thousands to millions of vertices. A computationally efficient and simple approach is required in such circumstances, but none currently exist. In this thesis, such an algorithm is proposed. The methodology is based on a depth-first search traversal of the network, and a specially designed ranking function that considers information local to each vertex. Due to the variety of network structures, a number of characteristics must be taken into consideration and combined into a single rank that measures the utility of removing each vertex. Since removing a vertex in sequential fashion impacts the network structure, an efficient post-processing algorithm is also proposed to quickly re-rank vertices. Experiments on a range of common complex network models with varying number of vertices are considered, in addition to real world networks. The proposed algorithm, DFSH, is shown to be highly competitive and often outperforms existing strategies such as Google PageRank for minimizing pairwise connectivity.