Network Similarity Measures and Automatic Construction of Graph Models using Genetic Programming

A complex network is an abstract representation of an intricate system of interrelated elements where the patterns of connection hold significant meaning. One particular complex network is a social network whereby the vertices represent people and edges denote their daily interactions. Understanding social network dynamics can be vital to the mitigation of disease spread as these networks model the interactions, and thus avenues of spread, between individuals. To better understand complex networks, algorithms which generate graphs exhibiting observed properties of real-world networks, known as graph models, are often constructed. While various efforts to aid with the construction of graph models have been proposed using statistical and probabilistic methods, genetic programming (GP) has only recently been considered. However, determining that a graph model of a complex network accurately describes the target network(s) is not a trivial task as the graph models are often stochastic in nature and the notion of similarity is dependent upon the expected behavior of the network. This thesis examines a number of well-known network properties to determine which measures best allowed networks generated by different graph models, and thus the models themselves, to be distinguished. A proposed meta-analysis procedure was used to demonstrate how these network measures interact when used together as classifiers to determine network, and thus model, (dis)similarity. The analytical results form the basis of the fitness evaluation for a GP system used to automatically construct graph models for complex networks. The GP-based automatic inference system was used to reproduce existing, well-known graph models as well as a real-world network. Results indicated that the automatically inferred models exemplified functional similarity when compared to their respective target networks. This approach also showed promise when used to infer a model for a mammalian brain network.

Instagram, Social Media, and the "Like": Exploring Virtual Identity's Role in 21st Century Students' New Socialization Experience

Personal technologies and social media use have changed the socialization experience of our 21st century learners. As learners have a new, embodied, virtual identity that is an omnipresent force within their social interactions, this study sought to examine how virtual identity influences student relationships both within and outside of a school context. This study also explored how personal technologies and social media use have influenced learners’ perceptions of their own 21st century learning. Using a qualitative inquiry, purposeful sampling was employed to recruit 6 participants between the ages of 15 to 19 to examine their social networking site use and education experience. Data were collected from single, one-on-one semi-structured interviews in which participants discussed their experiences using social media. Data were also collected from the teens’ personal Instagram accounts, and a personal reflexive researcher’s journal was kept for triangulation of data. Open and axial coding strategies alongside constant comparative methods were used to analyze data. Participants shared how they and their peers use social media, the pressures and expectations from other users, social media’s influence on peer relationships, and how social media influences their choices in the physical realm. All 6 participants explained that their teachers do not talk to them about their social media use, and even offered critiques of the school system itself and its inability to prepare students for the new realities of a digital world. This study concludes that while social media is very influential on students’ socialization, educators should be more concerned about the lack of guidance and support that students receive in school in terms of appropriate social media use and the navigation of virtual identity.

An Exploration of Professors’ Use of Twitter in Higher Education

Scant research has explored how professors in Canadian universities use Twitter as a teaching tool or to augment knowledge about their subject disciplines. This case study employed a mixed-method approach to examine how professors in an Ontario university use Twitter. Using a variation of the technology acceptance model, the survey (n = 17) found that professor participants—41.2% of whom use Twitter—perceive Twitter as somewhat useful as a teaching tool, not useful for finding and sharing information, and not useful for personal use. Participants’ gender and number of years teaching are not indicators of Twitter use. Furthermore, the level of support from peers and the university may be reasons why some do not use Twitter or have stopped using Twitter. Face-to-face interviews (n = 3) revealed that Twitter is not used in classrooms or lecture halls, but predominantly as a means of sharing information with students and colleagues. Another deterrent to using Twitter is not knowing who to follow. Findings indicate that some professors at this university embrace Twitter, but not necessarily as an in-class teaching tool. The challenge and the advantage of using Twitter is to discover and follow people who tweet material and to select relevant material to pass along to students and colleagues. Professor participants in the study found a use for the social network as a means to increase student engagement, create virtual information-exchange communities, and enrich their own learning.

When the Beautiful Game Turns Ugly: A Study of Fan Experiences of Perceived Match Fixing in Soccer

Limited academic attention has been given to the nexus between corruption in soccer and its impact on fandom. Consequently, the purpose of this qualitative study was to better understand the lived experiences of highly identified soccer fanatics living through this era of match fixing in the sport. Social networking site Twitter was utilized to recruit participants from three continents – Africa, Europe, and North America – based on submissions to the site in response to a perceived fix from a high-profile March, 2013 match. A total of 12 semi-structured interviews were conducted with highly identified soccer fans in accordance with Funk and James’ (2001) Psychological Continuum Model (PCM). Despite the majority of participants feeling skepticism about the purity of soccer today, half of the participants’ fandom remained unchanged in the face of perceived match fixing. Directions for future research and recommendations are considered and discussed.

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.