Attention-deficit/hyperactivity disorder (ADHD) is one of the most prevalent and impairing childhood disorders (5%; American Psychiatric Association, 2013), yet it is often studied in isolation. Such an approach is at odds with the clinical reality, where ADHD has a high comorbidity with oppositional defiant disorder, anxiety, and depression (Jensen, Martin, & Cantwell, 1997). Based on the possible presentations of ADHD with both externalizing and internalizing symptoms, there may be differences in associated characteristics, areas of impairment, and resulting assessment interventions. Therefore, the present study investigated how ADHD comorbidities manifested in a population of 233 elementary age children and how these profiles varied in already established characteristics (i.e., traits, social behaviors) and areas of deficit for children with ADHD (i.e., social functioning, academics, narrative comprehension). Characteristics and outcomes were examined using rating scales, behavior observations, laboratory tasks, and grades. Based on latent profile analyses, different patterns of comorbidity were identified using both parent and teacher ratings of ADHD. Based on parent and teacher report, those with high ADHD/ODD symptoms had more negative characteristics and outcomes. Network analyses corroborated these results, showing that internalizing symptoms were less relevant for associated characteristics and outcomes compared to ADHD and ODD symptoms. Overall, these results suggest that ADHD comorbidities may be primarily driven by ADHD and ODD symptoms, with this profile displaying more severe negative characteristics and outcomes.

Callous – unemotional (CU) traits are a key factor in understanding the persistence and severity of conduct problems. The factor structure of CU traits has been primarily examined through confirmatory factor analysis (CFA) in childhood and adolescent samples, yet little research has examined the structure of CU traits in preschool. Further, current CFA models have yielded poor – to – marginally acceptable fit, suggesting the need for a more nuanced approach in understanding the structure of CU traits in early childhood using an interitem approach (i.e., network analysis). Within a sample of 109 preschool children (M age = 4.77, SD = 1.10), CFA results supported a two – factor structure of the ICU, comprised of “callous” and “uncaring” factors. Results of the network analysis identified seems cold and uncaring as most central to the CU network. Results from the CFA demonstrated that CU traits can be assessed in preschool children using 12 of the original 24 items from the ICU, which is consistent with a small portion of research. Further, results of the network analysis suggested that seems cold and uncaring may be useful in screening for psychopathic traits in preschool children. Clinical implications, including ICU measure refinement, are explored.

Not only has gang membership been expanding, but the formation of cooperative ties with Mexican drug trafficking organizations (MDTOs) has been increasing as well. Collaborative relationships with MDTOs appear to be the driving force behind the continuing gang expansion and its subsequent effects. Using social network analysis, this study examines the linkage between MDTOs and American-based gang activity and the potential influence that MDTOs may have in U.S. drug market through their associations with American street gangs. Findings show the MDTOs to be extensively linked to each other by their affiliations with U.S. gangs, and a high level of connectivity exists between U.S. gangs and MDTOs. In addition, various centrality measures indicate the Sinaloa Cartel to have the broadest reach into the illicit drug market, as this cartel is affiliated with the highest number of gangs. The current study provides support for the continuance of multijurisdictional collaboration, and reaffirms the need for law enforcement to continue to explore the non-traditional approaches to crime and intelligence analysis.

With the rapid urbanization, the urban residents' demand for urban public parks is increasing. As a unique and representative age group, older adults put forward new requirements for the evaluation and rational planning of urban parks. Park accessibility is an important index reflecting the rationality of park layout, the accessibility of residents to the park and the social equity of park services. In this paper, buffer analysis and network analysis based on the ArcGIS platform were selected to analyze service accessibility and green transportation accessibility of The Villages metropolitan area of Florida respectively and then make a summary analysis. In particular, this paper chooses service area, common facilities, and recreational amenities as the evaluation factors of service accessibility. Besides, the coverage area of three modes of green transportation, namely walking, public transportation and bicycle, in different periods is selected as the evaluation factor of green transportation accessibility in this paper. The results show that: 1) The accessibility level of the study area is generally low, and more than half of the study area is not within the service scope of the park. 2) The urban parks serving the study area are relatively unevenly distributed; the road network is imperfect, and there are open circuit and blank area. 3) Park accessibility ratio of four modes of transportation in different time levels motor vehicles > bicycles > walking > public transportation. The research results can provide a reference for the optimization of the spatial layout of public parks in age-friendly cities.

What is the relationship between networks and unit-level outcomes, such as the international trade network among states and domestic rule of law or repression? Do these effects hold after accounting for actors' strategic selection of network ties? I explore these questions by building a multi-player game, in which players make two simultaneous decisions: (1) whether to form trade links and with who, and (2) whether to increase their trade benefits by improving their type, associated with the level of domestic economic risk factors. The model predicts an endogenous relationship between the number of direct trade partners and the probability of playing High Type: High Type states have more direct trade partners, and the number of trade partners has a positive effect on the probability of choosing High Type. A state's type is also affected by indirect trade connections--counter-intuitively, indirect trade has a negative effect on the probability of choosing High Type. In Chapters 3 and 4, I test the general predictions of the theoretical model, by applying them to two distinct areas of international research. In Chapter 3, I conceptualize a state's type as the level of domestic rule of law enforcement. States with strong rule of law enforcement are regarded as High Type states, because they guarantee lower cost of operations within their borders, by enforcing property rights and contractual law. Weak rule of law states, on the other hand, can be thought of as Low Type states, as business operations within such states are constantly threatened by a risk of expropriations, inefficiencies associated with corruption within the judicial system, and other manifestations of poor business practices. In Chapter 4, I recast the theoretical model by showing how a state's type can be conceptualized as a state's domestic respect for human rights. Highlighting the economic costs of repression, such as higher economic risk, negative publicity, and decreased quality of human capital, I argue that these costs are suffered by both the domestic economic elites and their international business partners. These business elites can, however, alleviate their losses resulting from such costs by either pressuring their government to embrace stronger human rights protections or, when this option is unavailable, by setting up channels for indirect economic transactions through states with more favorable political environments. To test each Chapter's empirical predictions, model the simultaneity between network formation and effect, using a statistical estimator developed by Ripley, Snijders, and Preciado (2012). This statistical estimator, referred to as a continuous Markov Chain exponential random graph model (MC ERGM), allows for a close mimicking of the theoretical model by simultaneously modeling two dependent variables: network formation and its effect on actors' behavior. The results of the statistical tests provide some support the theoretical predictions.

The past decades have seen a fast-growing and dynamic trend of network science and its applications. From the Internet to Facebook, from telecommunications to power grids, from protein interactions to paper citations, networks are everywhere and the network paradigm is pervasive. Network analysis and mining has become an important tool for scientific research and industrial applications to diverse domains. For example, finding communities within social networks enables us to identify groups of densely connected customers who may share similar interests and behaviors and thus generate more effective recommender systems; investigating the supply-network topological structure and growth model improves the resilience of supply networks against disruptions; and modeling influence diffusion in social networks provides insights into viral marketing strategies. However, none of these tasks is trivial. In fact, community detection, resilience analysis, and influence-diffusion modeling are all important challenges in complex networks. My PhD research contributes to these endeavors by exploring the implicit knowledge of connectivity and proximity encoded in the network graph topology. Our research originated from an attempt to find communities in networks. After carefully examining real-life communities and the features and limitations of a set of widely-used centrality measures, we develop a simple but powerful reachability-based influence-diffusion model. Based upon this model, we propose a new influence centrality and a novel shared-influence-neighbor (SIN) similarity. The former differentiates the comprehensive influence significance more precisely, and the latter gives rise to a refined vertex-pair closeness metric. Then we develop an influence-guided spherical K-means (IGSK) algorithm for community detection. Further, we propose two novel influence-guided label propagation (IGLP) algorithms for finding hierarchical communities in complex networks. Experiments on both real-life networks and synthetic benchmarks demonstrate superior performance of our algorithms in both undirected/directed and unweighted/weighted networks. Another research topic we investigated is resilience analysis of supply networks. Supply networks play an important role in product distribution, and survivability is a critical concern in supply-network design and analysis. We exploit the resilience embedded in supply-network topology by exploring the multiple-path reachability of each demand node to other nodes, and propose a novel resilience metric. We also develop new supply-network growth mechanisms that reflect the heterogeneous roles of different types of units in supply networks. We incorporate them into two fundamental network topologies (random-graph topology and scale-free topology), and evaluate the resilience under random disruptions and targeted attacks using the new resilience metric. The experimental results verify the validity of our resilience metric and the effectiveness of our growth model. This research provides a generic framework and important insights into the construction of robust supply networks. Finally, we investigate activation-based influence-diffusion modeling for viral marketing. One of the fundamental problems in viral marketing is to find a small set of initial adopters who can trigger the largest further adoptions through word-of-mouth-based influence propagation in the network. We propose a novel multiple-path asynchronous threshold (MAT) model, in which we quantitatively measure influence and keep track of its diffusion and aggregation during the diffusion process. Our MAT model captures both direct and indirect influence, influence attenuation along diffusion paths, temporal influence decay, and individual diffusion dynamics. Our work is an important step toward a more realistic diffusion model. Further, we develop two effective and efficient heuristics (IV-Greedy and IV-Community) to tackle the influence-maximization problem. Our experiments on four real-life networks demonstrate their excellent performance in terms of both influence spread and efficiency. Our work provides preliminary but significant insights and implications for diffusion research and marketing practice.

Using data from the National Longitudinal Study of Adolescent to Adult Health (Add Health), I investigate the influence of adolescent friendship network on the likelihood of college enrollment, and whether and how this influence is affected by stratification factors (e.g., gender, race/ethnicity, and socioeconomic status). However, there is a challenge in evaluating this influence process since adolescents usually non-randomly select their friends. A selection process needs to be taken into consideration simultaneously with the influence process of adolescents’ friendship network on their likelihood of college enrollment. Previous research on peer effects has methodological issues and limitations. Traditional methods (e.g., multivariate regression, multilevel modeling, or propensity score matching) using limited data (e.g., cross-sectional) and measures of friendship network (e.g., one best friend) could not solve the problem of integrating selection process and influence process in one model. In addition, the dyadic and triadic (or even higher level) dependency among friends in the network makes it more difficult to estimate selection and influence processes using traditional methods. To address these concerns, I employ longitudinal network analysis with stochastic actor-based models (SABMs) to account for the influence of friendship network on adolescent college enrollment when simultaneously considering the selection of friendship. The co-evolution model of network dynamics (selection) and behavioral dynamics (influence) also addresses the problem of endogeneity between network change and behavioral change. However, the co-evolution model requires network data and behavioral data measured in multiple time points, so in the first stage of this research, I generate the predicted probability of college enrollment at three time points of Add Health using traditional logistic regression. Then in the second stage of this research, I use the transformed likelihood of college enrollment, a statistical artifact, as the behavior variable in the co-evolution model to examine how the likelihood of college enrollment affect the friendship selection and in turn friend’s average likelihood of college enrollment in the network influences an adolescent’s own likelihood of college enrollment. In the first stage, I find that there are some levels of gender, race/ethnicity, and SES inequalities in the college enrollment, even after controlling for previous academic achievement, other individual characteristics, family backgrounds, and school level variables. In the second stage, the results of dynamic network analysis indicate significant selection (partial deselection) and influence effects of adolescent friendship networks on the likelihood of college enrollment. In the selection process, adolescents have high tendency to select friends who are similar to them in the likelihood of college enrollment, or terminate friendships with other students of dissimilar likelihood of college enrollment. In the influence process, the average alter effect is found consistently significant and positive across all models and schools, which indicates that there is strong social influence of friendship network on adolescents’ likelihood of college enrollment. The higher the average friends’ likelihood of college enrollment, the more likely the adolescent will increase own likelihood of college enrollment. I also discuss the significance of results and many important policy and practical implications.

O principal objetivo desta dissertação foi analisar a conformação e as características de arranjos intersetoriais, desde a formulação até a implementação dos projetos, considerando o papel dos atores estatais e da sociedade civil e suas relações como dimensões integrantes do estudo. A fim de superar as abordagens normativas em torno da intersetorialidade, analisamos empiricamente dois casos: o projeto Oficina Boracea, cuja análise foi dividida em momento inicial de implementação (T0 2002/2004) e momento atual (T1 2007/2016); e o Programa De Braços Abertos (DBA), desde sua formulação, em 2013, até 2016. Inscritos em contextos e tempos diferentes, ambos foram desenvolvidos pela Prefeitura de São Paulo enquanto experiências inaugurais de acolhida e atenção à população em situação de rua, embora atendam públicos em situações de vulnerabilidade social com nuances distintas. Por meio da metodologia de análise de redes sociais, o trabalho aqui realizado investigou a dinâmica relacional entre os atores e como essas relações estabelecidas ajudaram a definir padrões de intersetorialidade distintos. Em linhas gerais, observamos os fatores que influenciaram a consolidação de três arranjos de intersetorialidade. No caso do Boracea T0, os arranjos intersetorial e de governança apresentaram um claro destaque da pasta de assistência social e de organizações da sociedade civil (OSCs) conveniadas, bem como uma articulação efetiva com a saúde e relações pontuais com outros setores. No Boracea T1 observamos uma intensa articulação entre agentes implementadores das OSCs da assistência social e da saúde, porém com dificuldades para chegar ao alto escalão e influenciar em decisões mais estruturantes. Já no DBA, o arranjo observado envolveu a efetiva articulação de cinco setores de governo e OSCs, com fluxos bem definidos entre os três escalões burocráticos envolvidos. Desse modo, verificamos empiricamente que a intersetorialidade, mais do que um modelo de gestão bem formulado, é produto de interações cotidianas, sendo permeada por combinações distintas de fatores que definem a efetivação de arranjos diversos / The main goal of this dissertation was to analyze both form and characteristics of intersectoral arrangements, from the formulation to implementation instances of the projects, considering the role of state actors and civil society and their relations as inherently dimensions of the analysis. In order to overcome the normative approaches around intersectoriality, we empirically analyzed two cases: the Oficina Boracea project, whose analysis was divided into initial moment of implementation (T0 - 2002/2004) and current moment (T1 - 2007/2016); and the De Braços Abertos (DBA), from its formulation in 2013 to 2016. Registered in different contexts and times, both were developed by São Paulo City Hall as breaking through experiences of homeless welcoming and caring, although attending to public in situations of social vulnerability with different nuances. Through the methodology of social network analysis, this dissertation investigated the relational dynamics among the actors and how these established relationships helped to define different patterns of intersectoriality. In general terms, we observed the factors that influenced the consolidation of three intersectoral arrangements. In the case of Boracea T0, the intersectoral and governance arrangements presented a clear focus on the Social Assistance Department and on the civil society organizations (CSOs), as well as an effective articulation with Health Department and some punctual relations with other sectors. In Boracea T1, we observed an intense articulation between implementing agents from CSOs and from Social Assistance and Health Departments, but with difficulties in reaching the top command in order to influence more structuring decisions. In DBA, the observed arrangement presents an effective articulation of five sectors of government and the CSOs, with flows well defined among the three bureaucratic levels involved. In this way, we empirically verify that intersectorality, rather than a well-formulated management model, is the product of everyday interactions, imbued by distinct combinations of factors that define the effectiveness of diverse arrangements

This dissertation studies bad behavior at large-scale using data traces from online video games. Video games provide a natural laboratory for exploring bad behavior due to their popularity, explicitly defined (programmed) rules, and a competitive nature that provides motivation for bad behavior. More specifically, we look at two forms of bad behavior: cheating and toxic behavior. Cheating is most simply defined as breaking the rules of the game to give one player an edge over another. In video games, cheating is most often accomplished using programs, or "hacks," that circumvent the rules implemented by game code. Cheating is a threat to the gaming industry in that it diminishes the enjoyment of fair players, siphons off money that is paid to cheat creators, and requires investment in anti-cheat technologies. Toxic behavior is a more nebulously defined term, but can be thought of as actions that violate social norms, especially those that harm other members of the society. Toxic behavior ranges from insults or harassment of players (which has clear parallels to the real world) to domain specific instances such as repeatedly "suiciding"" to help an enemy team. While toxic behavior has clear parallels to bad behavior in other online domains, e.g., cyberbullying, if gone unchecked it has the potential to "kill" a game by driving away its players. We first present a distributed architecture and reference implementation for the collection and analysis of large-scale social data. Using this implementation we then study the social structure of over 10 million gamers collected from a planetary scale Online Social Network, about 720 thousand of whom have been labeled cheaters, finding a significant correlation between social structure and the probability of partaking in cheating behavior. We additionally collect over half a billion daily observations of the cheating status of these gamers. Using about 10 months of detailed server logs from a community owned and operated game server we next analyze how relationships in the aforementioned online social network are backed by in-game interactions. Next, we use the insights gained and find evidence for a contagion process underlying the spread of cheating behavior and perform a data driven simulation using mathematical models for contagion. Finally, we build a model using millions of crowdsourced decisions for predicting toxic behavior in online games. To the best of our knowledge, this dissertation presents the largest study of bad behavior to date. Our findings confirm theories about cheating and unethical behavior that have previously remained untested outside of controlled laboratory experiments or only with small, survey based studies. We find that the intensity of interactions between players is a predictor of a future relationship forming. We provide statistically significant evidence for cheating as a contagion. Finally, we extract insights from our model for detecting toxic behavior on how human reviewers perceive the presence and severity of bad behavior.

by Dimitri P. Bertsekas. / Includes bibliographies. / Research supported by National Science Foundation Grant ENG-79-06332 (87649)