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.

Electrophysiology of Oculomotor Delayed Response Tasks: A Model for the Maturation of Visual-Spatial Working Memory Networks

In the literature, persistent neural activity over frontal and parietal areas during the delay period of oculomotor delayed response (ODR) tasks has been interpreted as an active representation of task relevant information and response preparation. Following a recent ERP study (Tekok-Kilic, Tays, & Tkach, 2011 ) that reported task related slow wave differences over frontal and parietal sites during the delay periods of three ODR tasks, the present investigation explored developmental differences in young adults and adolescents during the same ODR tasks using 128-channel dense electrode array methodology and source localization. This exploratory study showed that neural functioning underlying visual-spatial WM differed between age groups in the Match condition. More specifically, this difference is localized anteriorly during the late delay period. Given the protracted maturation of the frontal lobes, the observed variation at the frontal site may indicate that adolescents and young adults may recruit frontal-parietal resources differently.

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.

The effect of synchronized group activities on pain threshold as a predictor of cooperation

Recent research suggests that participating in vigorous synchronized physical activity may result in elevated levels of endorphins, which may in turn affect social bonding (Cohen et. al., 2009). The present research aimed to examine whether or not the change in pain tolerance would be able to predict participants’ willingness to cooperate after statistically controlling for the groups’ condition. Participants were asked to run on a treadmill for 30 minutes under one of two conditions (control vs. synchronized). Prior to and after the run participants underwent a pain tolerance test. Once completed, a second activity was introduced to the participants; a cooperative game. A public goods game was used to measure an individual’s willingness to cooperate. The results showed the synchronized condition was able to predict that participants cooperated more during the public goods game (p = .009), however the change in pain threshold was unable to significantly predict cooperation (p = .32).

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.

Community Detection in Multi-Layer Networks

In the scope of the current thesis we review and analyse networks that are formed by nodes with several attributes. We suppose that different layers of communities are embedded in such networks, besides each of the layers is connected with nodes' attributes. For example, examine one of a variety of online social networks: an user participates in a plurality of different groups/communities – schoolfellows, colleagues, clients, etc. We introduce a detection algorithm for the above-mentioned communities. Normally the result of the detection is the community supplemented just by the most dominant attribute, disregarding others. We propose an algorithm that bypasses dominant communities and detects communities which are formed by other nodes' attributes. We also review formation models of the attributed networks and present a Human Communication Network (HCN) model. We introduce a High School Texting Network (HSTN) and examine our methods for that network.

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.

Properties and Algorithms of the KCube Interconnection Networks

The KCube interconnection network was first introduced in 2010 in order to exploit the good characteristics of two well-known interconnection networks, the hypercube and the Kautz graph. KCube links up multiple processors in a communication network with high density for a fixed degree. Since the KCube network is newly proposed, much study is required to demonstrate its potential properties and algorithms that can be designed to solve parallel computation problems. In this thesis we introduce a new methodology to construct the KCube graph. Also, with regard to this new approach, we will prove its Hamiltonicity in the general KC(m; k). Moreover, we will find its connectivity followed by an optimal broadcasting scheme in which a source node containing a message is to communicate it with all other processors. In addition to KCube networks, we have studied a version of the routing problem in the traditional hypercube, investigating this problem: whether there exists a shortest path in a Qn between two nodes 0n and 1n, when the network is experiencing failed components. We first conditionally discuss this problem when there is a constraint on the number of faulty nodes, and subsequently introduce an algorithm to tackle the problem without restrictions on the number of nodes.

Vertical R&D Sprillovers, Cooperation, Market Structure, and Innovation.

This paper studies vertical R&D spillovers between upstream and downstream firms. The model incorporates two vertically related industries, with horizontal spillovers within each industry and vertical spillovers between the two industries. Four types of R&D cooperation are studied : no cooperation, horizontal cooperation, vertical cooperation, and simultaneous horizontal and vertical cooperation. Vertical spillovers always increase R&D and welfare, while horizontal spillovers may increase or decrease them. The comparison of cooperative settings in terms of R&D shows that no setting uniformly dominates the others. Which type of cooperation yields more R&D depends on horizontal and vertical spillovers, and market structure. The ranking of cooperative structures hinges on the signs and magnitudes of three competitive externalities (vertical, horizontal, and diagonal) which capture the effect of the R&D of a firm on the profits of other firms. One of the basic results of the strategic investment literature is that cooperation between competitors increases (decreases) R&D when horizontal spillovers are high (low); the model shows that this result does not necessarily hold when vertical spillovers and vertical cooperation are taken into account. The paper proposes a theory of innovation and market structure, showing that the relation between innovation and competition depends on horizontal spillovers, vertical spillovers, and cooperative settings. The private incentives for R&D cooperation are addressed. It is found that buyers and sellers have divergent interests regarding the choice of cooperative settings and that spillovers increase the likelihood of the emergence of cooperation in a decentralized equilibrium.

Information Sharing and the Stability of Cooperation in Research Joint Ventures.

The model studies information sharing and the stability of cooperation in cost reducing Research Joint Ventures (RJVs). In a four-stage game-theoretic framework, firms decide on participation in a RJV, information sharing, R&D expenditures, and output. An important feature of the model is that voluntary information sharing between cooperating firms increases information leakage from the RJV to outsiders. It is found that it is the spillover from the RJV to outsiders which determines the decision of insiders whether to share information, while it is the spillover affecting all firms which determines the level of information sharing within the RJV. RJVs representing a larger portion of firms in the industry are more likely to share information. It is also found that when sharing information is costless, firms never choose intermediate levels of information sharing : they share all the information or none at all. The size of the RJV is found to depend on three effects : a coordination effect, an information sharing effect, and a competition effect. Depending on the relative magnitudes of these effects, the size of the RJV may increase or decrease with spillovers. The effect of information sharing on the profitability of firms as well as on welfare is studied.

La collaboration interprofessionnelle vers une transformation des pratiques au sein d’un GMF de deuxième vague

INTRODUCTION : Les soins de première ligne au Québec vivent depuis quelques années une réorganisation importante. Les GMF, les cliniques réseaux, les CSSS, les réseaux locaux de service, ne sont que quelques exemples des nouveaux modes d’organisation qui voient le jour actuellement. La collaboration interprofessionnelle se trouve au cœur de ces changements. MÉTHODOLOGIE : Il s’agit d’une étude de cas unique, effectuée dans un GMF de deuxième vague. Les données ont été recueillies par des entrevues semi-dirigées auprès du médecin responsable du GMF, des médecins et des infirmières du GMF, et du cadre responsable des infirmières au CSSS. Les entrevues se sont déroulées jusqu’à saturation empirique. Des documents concernant les outils cliniques et les outils de communication ont aussi été consultés. RÉSULTATS : À travers un processus itératif touchant les éléments interactionnels et organisationnels, par l’évolution vers une culture différente, des ajustements mutuels ont pu être réalisés et les pratiques cliniques se sont réellement modifiées au sein du GMF étudié. Les participants ont souligné une amélioration de leurs résultats cliniques. Ils constatent que les patients ont une meilleure accessibilité, mais l’effet sur la charge de travail et sur la capacité de suivre plus de patients est évaluée de façon variable. CONCLUSION : Le modèle conceptuel proposé permet d’observer empiriquement les dimensions qui font ressortir la valeur ajoutée du développement de la collaboration interprofessionnelle au sein des GMF, ainsi que son impact sur les pratiques professionnelles.

Optimization of p-cycle protection schemes in optical networks

La survie des réseaux est un domaine d'étude technique très intéressant ainsi qu'une préoccupation critique dans la conception des réseaux. Compte tenu du fait que de plus en plus de données sont transportées à travers des réseaux de communication, une simple panne peut interrompre des millions d'utilisateurs et engendrer des millions de dollars de pertes de revenu. Les techniques de protection des réseaux consistent à fournir une capacité supplémentaire dans un réseau et à réacheminer les flux automatiquement autour de la panne en utilisant cette disponibilité de capacité. Cette thèse porte sur la conception de réseaux optiques intégrant des techniques de survie qui utilisent des schémas de protection basés sur les p-cycles. Plus précisément, les p-cycles de protection par chemin sont exploités dans le contexte de pannes sur les liens. Notre étude se concentre sur la mise en place de structures de protection par p-cycles, et ce, en supposant que les chemins d'opération pour l'ensemble des requêtes sont définis a priori. La majorité des travaux existants utilisent des heuristiques ou des méthodes de résolution ayant de la difficulté à résoudre des instances de grande taille. L'objectif de cette thèse est double. D'une part, nous proposons des modèles et des méthodes de résolution capables d'aborder des problèmes de plus grande taille que ceux déjà présentés dans la littérature. D'autre part, grâce aux nouveaux algorithmes, nous sommes en mesure de produire des solutions optimales ou quasi-optimales. Pour ce faire, nous nous appuyons sur la technique de génération de colonnes, celle-ci étant adéquate pour résoudre des problèmes de programmation linéaire de grande taille. Dans ce projet, la génération de colonnes est utilisée comme une façon intelligente d'énumérer implicitement des cycles prometteurs. Nous proposons d'abord des formulations pour le problème maître et le problème auxiliaire ainsi qu'un premier algorithme de génération de colonnes pour la conception de réseaux protegées par des p-cycles de la protection par chemin. L'algorithme obtient de meilleures solutions, dans un temps raisonnable, que celles obtenues par les méthodes existantes. Par la suite, une formulation plus compacte est proposée pour le problème auxiliaire. De plus, nous présentons une nouvelle méthode de décomposition hiérarchique qui apporte une grande amélioration de l'efficacité globale de l'algorithme. En ce qui concerne les solutions en nombres entiers, nous proposons deux méthodes heurisiques qui arrivent à trouver des bonnes solutions. Nous nous attardons aussi à une comparaison systématique entre les p-cycles et les schémas classiques de protection partagée. Nous effectuons donc une comparaison précise en utilisant des formulations unifiées et basées sur la génération de colonnes pour obtenir des résultats de bonne qualité. Par la suite, nous évaluons empiriquement les versions orientée et non-orientée des p-cycles pour la protection par lien ainsi que pour la protection par chemin, dans des scénarios de trafic asymétrique. Nous montrons quel est le coût de protection additionnel engendré lorsque des systèmes bidirectionnels sont employés dans de tels scénarios. Finalement, nous étudions une formulation de génération de colonnes pour la conception de réseaux avec des p-cycles en présence d'exigences de disponibilité et nous obtenons des premières bornes inférieures pour ce problème.