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.

Cortical and autonomic modulation of attentional control /

Whereas the role of the anterior cingulate cortex (ACC) in cognitive control has received considerable attention, much less work has been done on the role of the ACC in autonomic regulation. Its connections through the vagus nerve to the sinoatrial node of the heart are thought to exert modulatory control over cardiovascular arousal. Therefore, ACC is not only responsible for the implementation of cognitive control, but also for the dynamic regulation of cardiovascular activity that characterizes healthy heart rate and adaptive behaviour. However, cognitive control and autonomic regulation are rarely examined together. Moreover, those studies that have examined the role of phasic vagal cardiac control in conjunction with cognitive performance have produced mixed results, finding relations for specific age groups and types of tasks but not consistently. So, while autonomic regulatory control appears to support effective cognitive performance under some conditions, it is not presently clear just what factors contribute to these relations. The goal of the present study was, therefore, to examine the relations between autonomic arousal, neural responsivity, and cognitive performance in the context of a task that required ACC support. Participants completed a primary inhibitory control task with a working memory load embedded. Pre-test cardiovascular measures were obtained, and ontask ERPs associated with response control (N2/P3) and error-related processes (ERN/Pe) were analyzed. Results indicated that response inhibition was unrelated to phasic vagal cardiac control, as indexed by respiratory sinus arrhythmia (RSA). However, higher resting RSA was associated with larger ERN ampUtude for the highest working memory load condition. This finding suggests that those individuals with greater autonomic regulatory control exhibited more robust ACC error-related responses on the most challenging task condition. On the other hand, exploratory analyses with rate pressure product (RPP), a measure of sympathetic arousal, indicated that higher pre-test RPP (i.e., more sympathetic influence) was associated with more errors on "catch" NoGo trials, i.e., NoGo trials that simultaneously followed other NoGo trials, and consequently, reqviired enhanced response control. Higher pre-test RPP was also associated with smaller amplitude ERNs for all three working memory loads and smaller ampUtude P3s for the low and medium working memory load conditions. Thus, higher pretest sympathetic arousal was associated with poorer performance on more demanding "catch" NoGo trials and less robust ACC-related electrocortical responses. The findings firom the present study highlight tiie interdependence of electrocortical and cardiovascular processes. While higher pre-test parasympathetic control seemed to relate to more robust ACC error-related responses, higher pre-test sympathetic arousal resulted in poorer inhibitory control performance and smaller ACC-generated electrocortical responses. Furthermore, these results provide a base from which to explore the relation between ACC and neuro/cardiac responses in older adults who may display greater variance due to the vulnerabihty of these systems to the normal aging process.

Automatic Inference of Graph Models for Complex Networks with Genetic Programming

Complex networks can arise naturally and spontaneously from all things that act as a part of a larger system. From the patterns of socialization between people to the way biological systems organize themselves, complex networks are ubiquitous, but are currently poorly understood. A number of algorithms, designed by humans, have been proposed to describe the organizational behaviour of real-world networks. Consequently, breakthroughs in genetics, medicine, epidemiology, neuroscience, telecommunications and the social sciences have recently resulted. The algorithms, called graph models, represent significant human effort. Deriving accurate graph models is non-trivial, time-intensive, challenging and may only yield useful results for very specific phenomena. An automated approach can greatly reduce the human effort required and if effective, provide a valuable tool for understanding the large decentralized systems of interrelated things around us. To the best of the author's knowledge this thesis proposes the first method for the automatic inference of graph models for complex networks with varied properties, with and without community structure. Furthermore, to the best of the author's knowledge it is the first application of genetic programming for the automatic inference of graph models. The system and methodology was tested against benchmark data, and was shown to be capable of reproducing close approximations to well-known algorithms designed by humans. Furthermore, when used to infer a model for real biological data the resulting model was more representative than models currently used in the literature.

Priming the Governance System for Climate Change Adaptation: The Application of a Social Ecological Inventory (SEI) to Engage Actors in Niagara, Canada

Adaptive systems of governance are increasingly gaining attention in respect to complex and uncertain social-ecological systems. Adaptive co-management is one strategy to make adaptive governance operational and holds promise with respect to community climate change adaptation as it facilitates participation and learning across scales and fosters adaptive capacity and resilience. Developing tools which hasten the realization of such approaches are growing in importance. This paper describes explores the Social Ecological Inventory (SEI) as a tool to 'prime' a regional climate change adaptation network. The SEI tool draws upon the social-ecological systems approach in which social and ecological systems are considered linked. SEIs bridge the gap between conventional stakeholder analysis and biological inventories and take place through a six phase process. A case study describes the results of applying an SEI to prime an adaptive governance network for climate change adaptation in the Niagara Region of Canada. Lessons learned from the case study are discussed and highlight how the SEI catalyzed the adaptive co-management process in the case. Future avenues for SEIs in relation to climate change adaptation emerge from this exploratory work and offer opportunities to inform research and adaptation planning.

Object-Oriented Genetic Programming for the Automatic Inference of Graph Models for Complex Networks

Complex networks are systems of entities that are interconnected through meaningful relationships. The result of the relations between entities forms a structure that has a statistical complexity that is not formed by random chance. In the study of complex networks, many graph models have been proposed to model the behaviours observed. However, constructing graph models manually is tedious and problematic. Many of the models proposed in the literature have been cited as having inaccuracies with respect to the complex networks they represent. However, recently, an approach that automates the inference of graph models was proposed by Bailey [10] The proposed methodology employs genetic programming (GP) to produce graph models that approximate various properties of an exemplary graph of a targeted complex network. However, there is a great deal already known about complex networks, in general, and often specific knowledge is held about the network being modelled. The knowledge, albeit incomplete, is important in constructing a graph model. However it is difficult to incorporate such knowledge using existing GP techniques. Thus, this thesis proposes a novel GP system which can incorporate incomplete expert knowledge that assists in the evolution of a graph model. Inspired by existing graph models, an abstract graph model was developed to serve as an embryo for inferring graph models of some complex networks. The GP system and abstract model were used to reproduce well-known graph models. The results indicated that the system was able to evolve models that produced networks that had structural similarities to the networks generated by the respective target models.