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.

Social physique anxiety across physical activity settings : a meta-analytical review

The purpose of this meta-analytic investigation was to review the empirical evidence specific to the effect of physical activity context on social physique anxiety (SP A). English language studies were located from computer and manual literature searches. A total of 146 initial studies were coded. Studies included in the meta-analysis presented at least one empirical effect for SPA between physical activity participants (i.e., athletes or exercisers) and non-physical activity participants. The final sample included thirteen studies, yielding 14 effect sizes, with a total sample size of 2846. Studies were coded for mean SPA between physical activity participants and non-physical activity participants. Moderator variables related to demographic and study characteristics were also coded. Using Hunter and Schmidt's (2004) protocol, statistical artifacts were corrected. Results indicate that, practically speaking, those who were physically active reported lower levels of SPA than the comparison group (dcorr = -.12; SDeorr.-=-;22). Consideration of the magnitude of the ES, the SDeorr, and confidence interval suggests that this effect is not statistically significant. While most moderator analyses reiterated this trend, some differences were worth noting. Previous research has identified SPA to be especially salient for females compared to males, however, in the current investigation, the magnitude of the ES' s comparing physical activity participants to the comparison group was similar (deorr = -.24 for females and deorr = -.23 for males). Also, the type of physical activity was investigated, and results showed that athletes reported lower levels of SP A than the comparison group (deorr = -.19, SDeorr = .08), whereas exercisers reported higher levels of SPA than the comparison group (deorr = .13, SDeorr = .22). Results demonstrate support for the dispositional nature of SP A. Consideration of practical significance suggests that those who are involved in physical activity may experience slightly lower levels of SPA than those not reporting physical activity participation. Results potentially offer support for the bi-directionality of the relationship between physical activity and SP A; however, a causality may not be inferred. More information about the type of physical activity (i.e., frequency/nature of exercise behaviour, sport classificationllevel of athletes) may help clarify the role of physical activity contexts on SPA.