It is essential to build, maintain, and use our transportation systems in a manner that meets our current needs while addressing the social and economic needs of future generations. In today’s world, transportation congestion causes serious negative impacts to our societies. To this end, researchers have been utilizing various statistical methods to better study the flow of traffic into the road networks. However, these valuable studies cannot realize their true potential without solid in-depth understanding of the connectivity between the various traffic intersections. This paper bridges the gap between the engineering and social science domains. To this end, the authors propose a dynamic social network analysis framework to study the centrality of the existing road networks. This approach utilizes the field of network analysis where: (1) visualization and modeling techniques allow capturing the relationships, interactions, and attributes of and between network constituents, and (2) mathematical measurements facilitate analyzing quantitative relationships within the network. Connectivity and the importance of each intersection within the network will be understood using this method. The author conducted social network analysis modeling using three studies in Louisiana and two studies in Mississippi. Four types of centrality analysis were performed to identify the most central and important intersections within each study area. Results indicate intersection social network analysis modeling aligns with current congestion studies and transportation planning decisions.

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Under the very general topic of an historical or temporal central place study, one particular approach to analyzing the centrality of urban places is outlined. Centrality is taken in a very limited context - the influence of an urban place over its umland. The relationship between population of an urban place and number of "labour units", or central labour units, is used to measure the relative centrality for a sample of places. "Labour units" themselves are introduced to denote all persons working in central activities in an urban place. The study is carried out in Kent and Lambton counties in Southwestern Ontario using data obtained from the available national, provincial and county directories. Rather than limit the study to one point in time, as most earlier works have, an analysis is carried out through time, from 1851 to 1857. Small urban places are the focus of attention in this study because of data availability and accuracy. Results show a noticeable decline in centrality over time, which is more marked in the later years. General trends are also drawn for changes in centrality in individual towns of differing and similar sizes. Some of the problems and shortcomings of the study are outlined as a guide to further research. / Thesis / Master of Arts (MA)

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Recent estimates suggest that a large percentage of the population experiences some type of traumatic event over the course of the lifetime, but a relatively small proportion of individuals develop severe, long-lasting problems (e.g., posttraumatic stress disorder; PTSD). One major goal for trauma researchers is to understand what factors contribute to these differential outcomes, and much of this research has examined correlates of posttraumatic stress disorder (PTSD) symptom severity. An important next step in this line of research is the development of conceptual frameworks to foster a deeper understanding of the relationships among these diverse predictors of PTSD and their predictive power in relation to each other. A framework proposed by Rubin, Boals, and Hoyle centers on the influence of narrative centrality (construal of a traumatic experience as central to one's identity and to the life story) and negative affectivity (the tendency to experience negative emotion and to interpret situations and experiences in a negative light), suggesting many variables may correlate with PTSD symptoms via shared variance with these two factors. With a sample of 477 participants recruited from Amazon Mechanical Turk, this dissertation project extended the work of Rubin and colleagues by a) utilizing structural equation modeling techniques to simultaneously examine relationships among variables, b) investigating the utility of the model with a carefully-selected list of PTSD correlates, c) extending the model by including PTSD symptom severity, and d) exploring both direct and indirect effects to assess the roles of narrative centrality and negative affectivity as they relate to known PTSD correlates and PTSD symptom severity. PTSD correlates included social support quality and quantity, peritraumatic dissociation, negative posttraumatic cognitions, perceived injustice, and negative religious coping. Hypotheses were partially supported, and there was some evidence that the model may be effective in distinguishing between variables more and less germane to the individual's construal of a traumatic experience.

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SNA provides a wide range of tools that allow examination of telecommunications graphs. Those graphs contain vertices representing cell phone users and lines standing for established connections. Many sna tools do not incorporate the intensity of interaction. This may lead to wrong conclusions because the difference between best friends and random contacts can be defined by the accumulated duration of talks. To solve this problem, we propose a closeness centrality measure (ewc) that incorporates line values and compare it to Freeman's closeness. Small exemplary networks will demonstrate the characteristics of the weighted closeness compared to other centrality measures. Finally, the ewc will be tested on a real-world telecommunications graph provided by a large Austrian mobile service provider and the advantages of the ewc will be discussed. / Series: Research Report Series / Department of Statistics and Mathematics

In Social Network Analysis (SNA) centrality measures focus on activity (degree), information access (betweenness), distance to all the nodes (closeness), or popularity (pagerank). We introduce a new measure quantifying the distance of nodes to the network center. It is called weighted distance to nearest center (WDNC) and it is based on edge-weighted closeness (EWC), a weighted version of closeness. It combines elements of weighted centrality as well as clustering. The WDNC will be tested on two e-mail networks of the R community, one of the most important open source programs for statistical computing and graphics. We will find that there is a relationship between the WDNC and the formal organization of the R community. / Series: Research Report Series / Department of Statistics and Mathematics

abstract: Research and theory in social psychology and related fields indicates that people simultaneously hold many cultural identities. And it is well evidenced across relevant fields (e.g., sociology, marketing, economics) that salient identities are instrumental in a variety of cognitive and behavioral processes, including decision-making. It is not, however, well understood how the relative salience of various cultural identities factors into the process of making identity-relevant choices, particularly ones that require an actor to choose between conflicting sets of cultural values or beliefs. It is also unclear whether the source of that salience (e.g., chronic or situational) is meaningful in this regard. The current research makes novel predictions concerning the roles of cultural identity centrality and cultural identity situational salience in three distinct aspects of the decision-making process: Direction of decision, speed of decision, and emotion related to decision. In doing so, the research highlights two under-researched forms of culture (i.e., political and religious) and uses as the focal dependent variable a decision-making scenario that forces participants to choose between the values of their religious and political cultures and, to some degree, behave in an identity-inconsistent manner. Results indicate main effects of Christian identity centrality and democrat identity centrality on preference for traditional versus gender-neutral (i.e., non-traditional/progressive) restrooms after statistically controlling for covariates. Additionally, results show a significant main effect of democrat identity centrality and a significant interaction effect of Christian and democrat identity centrality on positive emotion linked to the decision. Post hoc analyses further reveal a significant quadratic relationship between Christian identity centrality and emotion related to the decision. There was no effect of situational strength of democrat identity salience on the decision. Neither centrality or situational strength had any effect on the speed with which participants made their decisions. This research theoretically and empirically advances the study of cultural psychology and carries important implications for identity research and judgment and decision-making across a variety of fields, including management, behavioral economics, and marketing. / Dissertation/Thesis / Doctoral Dissertation Psychology 2019

Ces dernières années, les réseaux sont devenus une source importante d’informations dans différents domaines aussi variés que les sciences sociales, la physique ou les mathématiques. De plus, la taille de ces réseaux n’a cessé de grandir de manière conséquente. Ce constat a vu émerger de nouveaux défis, comme le besoin de mesures précises et intuitives pour caractériser et analyser ces réseaux de grandes tailles en un temps raisonnable. La première partie de cette thèse introduit une nouvelle mesure de similarité entre deux noeuds d’un réseau dirigé et pondéré : la covariance “sum-over-paths”. Celle-ci a une interprétation claire et précise : en dénombrant tous les chemins possibles deux noeuds sont considérés comme fortement corrélés s’ils apparaissent souvent sur un même chemin – de préférence court. Cette mesure dépend d’une distribution de probabilités, définie sur l’ensemble infini dénombrable des chemins dans le graphe, obtenue en minimisant l'espérance du coût total entre toutes les paires de noeuds du graphe sachant que l'entropie relative totale injectée dans le réseau est fixée à priori. Le paramètre d’entropie permet de biaiser la distribution de probabilité sur un large spectre : allant de marches aléatoires naturelles où tous les chemins sont équiprobables à des marches biaisées en faveur des plus courts chemins. Cette mesure est alors appliquée à des problèmes de classification semi-supervisée sur des réseaux de taille moyennes et comparée à l’état de l’art. La seconde partie de la thèse introduit trois nouveaux algorithmes de classification de noeuds en sein d’un large réseau dont les noeuds sont partiellement étiquetés. Ces algorithmes ont un temps de calcul linéaire en le nombre de noeuds, de classes et d’itérations, et peuvent dés lors être appliqués sur de larges réseaux. Ceux-ci ont obtenus des résultats compétitifs en comparaison à l’état de l’art sur le large réseaux de citations de brevets américains et sur huit autres jeux de données. De plus, durant la thèse, nous avons collecté un nouveau jeu de données, déjà mentionné : le réseau de citations de brevets américains. Ce jeu de données est maintenant disponible pour la communauté pour la réalisation de tests comparatifs. La partie finale de cette thèse concerne la combinaison d’un graphe de citations avec les informations présentes sur ses noeuds. De manière empirique, nous avons montré que des données basées sur des citations fournissent de meilleurs résultats de classification que des données basées sur des contenus textuels. Toujours de manière empirique, nous avons également montré que combiner les différentes sources d’informations (contenu et citations) doit être considéré lors d’une tâche de classification de textes. Par exemple, lorsqu’il s’agit de catégoriser des articles de revues, s’aider d’un graphe de citations extrait au préalable peut améliorer considérablement les performances. Par contre, dans un autre contexte, quand il s’agit de directement classer les noeuds du réseau de citations, s’aider des informations présentes sur les noeuds n’améliora pas nécessairement les performances. La théorie, les algorithmes et les applications présentés dans cette thèse fournissent des perspectives intéressantes dans différents domaines. In recent years, networks have become a major data source in various fields ranging from social sciences to mathematical and physical sciences. Moreover, the size of available networks has grow substantially as well. This has brought with it a number of new challenges, like the need for precise and intuitive measures to characterize and analyze large scale networks in a reasonable time. The first part of this thesis introduces a novel measure between two nodes of a weighted directed graph: The sum-over-paths covariance. It has a clear and intuitive interpretation: two nodes are considered as highly correlated if they often co-occur on the same -- preferably short -- paths. This measure depends on a probability distribution over the (usually infinite) countable set of paths through the graph which is obtained by minimizing the total expected cost between all pairs of nodes while fixing the total relative entropy spread in the graph. The entropy parameter allows to bias the probability distribution over a wide spectrum: going from natural random walks (where all paths are equiprobable) to walks biased towards shortest-paths. This measure is then applied to semi-supervised classification problems on medium-size networks and compared to state-of-the-art techniques. The second part introduces three novel algorithms for within-network classification in large-scale networks, i.e., classification of nodes in partially labeled graphs. The algorithms have a linear computing time in the number of edges, classes and steps and hence can be applied to large scale networks. They obtained competitive results in comparison to state-of-the-art technics on the large scale U.S.~patents citation network and on eight other data sets. Furthermore, during the thesis, we collected a novel benchmark data set: the U.S.~patents citation network. This data set is now available to the community for benchmarks purposes. The final part of the thesis concerns the combination of a citation graph with information on its nodes. We show that citation-based data provide better results for classification than content-based data. We also show empirically that combining both sources of information (content-based and citation-based) should be considered when facing a text categorization problem. For instance, while classifying journal papers, considering to extract an external citation graph may considerably boost the performance. However, in another context, when we have to directly classify the network citation nodes, then the help of features on nodes will not improve the results. The theory, algorithms and applications presented in this thesis provide interesting perspectives in various fields.