Theses and Dissertations at Montana State University (MSU)
Permanent URI for this collectionhttps://scholarworks.montana.edu/handle/1/733
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Item Relationship between social support and substance use among American Indian people with substance use disorder(Montana State University - Bozeman, College of Letters & Science, 2023) Neavill, Morgan Eva; Chairperson, Graduate Committee: Monica SkewesAmerican Indians and Alaska Natives (AI/ANs) have endured trauma over generations and still experience systemic racism and oppression today. Historical trauma has contributed to health problems among AI/ANs, including high rates of substance use disorder. Social support is a protective factor for substance use in other populations; however, little is known about the role of social support and substance use in AI/AN communities. The current study employed secondary data analysis to understand the relationship between social support and substance use among AI/AN adults with substance use disorder. Using a Community-Based Participatory Research framework, a cross-sectional survey was conducted in partnership with an AI reservation community in Montana to examine risk and protective factors for substance use. Participants were 198 tribal members who self-identified as having a substance use problem. Social network characteristics were assessed using a modified version of the Important People Drug and Alcohol (IPDA) interview and substance use was assessed using the Timeline Followback. Consistent with previous research, the current study found that network substance use behavior was a better predictor of participant substance use outcomes than general support, substance specific support, or support for recovery/treatment. Variables associated with greater drug and alcohol abstinence among participants included living in larger household, having a greater percentage of the household that is sober, not having attended boarding school, having a larger percentage of the social network that does not accept one's substance use, having a smaller percentage of the social network rated as moderate or heavy substance users, and having a smaller percentage of the social network that uses substances frequently. An additional analysis was conducted to test whether the association between social support and participant substance use was moderated by the substance use behavior of the network, but the interaction was not significant. Contrary to prior research, the size, general supportiveness, and importance of the social network were not significantly associated with participant substance use. Results suggest that the IPDA may benefit from modifications to improve its usefulness in addiction research with AI/ANs. Implications for tribal members with substance use problems, their loved ones, and community leaders are discussed.Item An adaptive genetic algorithm for fitting DeGroot opinion diffusion models on social networks(Montana State University - Bozeman, College of Letters & Science, 2022) Johnson, Kara Layne; Chairperson, Graduate Committee: John J. BorkowskiWhile a variety of options are available for modeling opinion diffusion--the process through which opinions change and spread through a social network--current methods focus on modeling the process on online social networks where large quantities of opinion data are readily available. For in-person networks, where data are more difficult to collect, models that predict the opinions of the individuals in the network require that the structure of social influence--who is influenced by whom and to what degree--is specified by the researcher instead of informed by data. In order to fit data-driven opinion diffusion models on small networks with limited data, we developed a genetic algorithm for fitting the DeGroot opinion diffusion model. We detail the algorithm and present simulation studies to assess the algorithm's performance. We find the algorithm is able to recover model parameters across a variety of network and data set conditions, it continues to perform well under the assumption violations expected in practical applications, and the algorithm performance is robust to most choices of hyperparameters. Finally, we present an analysis of data from the study that motivated the methodological development.Item Hierarchical fuzzy spectral clustering in campaign finance social networks(Montana State University - Bozeman, College of Engineering, 2021) Wahl, Scott Allen; Chairperson, Graduate Committee: John SheppardCommunity detection in networks is an important tool in understanding complex systems. Finding these communities in complex real-world systems is important in many disciplines, such as computer science, sociology, biology, and others. In this research, we develop an algorithm for performing hierarchical fuzzy spectral clustering. The clustering algorithm is applied to small benchmark problems, as well as a large real-world campaign finance network. Afterwards, we extend the hierarchical fuzzy spectral clustering for use in evolving networks. The discovered communities are tracked through the evolving network and their underlying properties analyzed. Third, we apply association rule mining on community-based partitions of the data. A comparison of the results within and between communities show the effectiveness of this method for adding interpretability to the underlying system. Fourth, we examine the ability of hierarchical fuzzy spectral clustering on a graph to predict behavior that is not present in the graph itself. The results are shown to be effective in predicting votes in the United States legislature based on the campaign finance networks. Finally, we develop an orthogonal spectral autoencoder that is used to perform graph embedding. This approximation model avoids the eigenvector decomposition of the full network, as well as allows out-of-sample spectral clustering. The results show the embedding performs comparably to the full spectral clustering.Item With a little help from my friends: investigating communal mastery as a contributor to resilient outcomes among American Indians with substance use disorder(Montana State University - Bozeman, College of Letters & Science, 2019) Lahiere, Amanda Nicole; Chairperson, Graduate Committee: Monica SkewesAmerican Indians and Alaskan Natives (AI/ANs) have endured significant historical and individual adversity across several generations. Such adversity is associated with negative mental, physical, and behavioral outcomes. However, this adversity is not always associated with negative outcomes for all AI/ANs. In order to understand the differences in AI/AN outcomes, protective factors buffering against negative outcomes need to be examined. Communal mastery, a relatively unexplored construct, has been posited as a potential protective factor for AI/AN people. The present study aimed to understand the relationship between communal mastery, risk factors specific to historical trauma and discrimination, and substance use. Moreover, we hoped to understand if communal mastery moderated the effects of historical loss and discrimination on substance use. Participants (N = 197) included tribal members from a Northern Plains reservation in recovery from substance use disorder or with the desire to change their substance use behavior. Data were analyzed using hierarchical multiple regression to understand if communal mastery is protective for substance use in the current AI sample. Contrary to my hypotheses, communal mastery did not moderate the relationship between culturally-specific risk factors (i.e. historical trauma and discrimination) and substance use. However, the present sample had high levels of communal mastery indicating that restriction of range may have affected the findings. Moreover, communal mastery may be protective at certain times in the recovery process but not when people are using substances. Also, historical loss was associated with greater levels of abstinence from substance use, contrary to predictions. Thus, future research should focus on the role of historical loss awareness in AI/AN recovery and investigate how communal mastery interacts with other risk factors to predict substance use outcomes in Indigenous populations.Item The emergence of collective behavior on social and biological networks(Montana State University - Bozeman, College of Letters & Science, 2018) Wilander, Adam Troy Charles; Chairperson, Graduate Committee: Scott McCallla; Dissertation contains an article of which Adam Troy Charles Wilander is not the main author.In this thesis, we broadly examine collective behaviors in various social and biological contexts. Aggregation, for instance, is a natural phenomenon that occurs in a variety of contexts; it is observed in schools of fish, flocks of birds, and colonies of bacteria, among others. This behavior can be found in some agent-based models, where it is typically assumed every pair of individuals interact according to a simple set of rules. In the first half of this thesis, we study a particular, well-understood aggregation model upon relaxation of the assumption that every individual interacts with every other. We review prior results on this topic -- when the underlying structure of interactions is an Erdos-Renyi graph. Seeking to incorporate community structure into the network, we establish the analogous problem under a class of networks called stochastic block graphs; a particular aspect of the system's metastable dynamics is explored upon varying the graph's connection densities. Finally, we evaluate the potential to leverage this system's dynamics in order to recover community structure (given a known graph as input). In the second half of this thesis, we similarly explore the aggregate behaviors of synchronization and desynchronization, appearing in diverse settings such as the study of metabolic oscillations and cell behaviors over time, respectively. Previous studies have leveraged a model in which repressilator entities are connected by a diffusive quorum sensing mechanism; these have shown (numerically) that the complex composition of observable behaviors depends upon the insertion point of the upregulating protein in the feedback loop. We rigorously prove a version of this; for negative feedback, negative signaling (Nf-Ns) systems we find only a unique stable equilibrium or a stable oscillation is possible. Additionally, we observe (numerically) the complex multistable dynamics that arise when a positive signal is included in the feedback loop and characterize this shift as a saddle node bifurcation of a cubic curve.Item Dual enrollment's influence on the socialization of students as future college students: a grounded theory study(Montana State University - Bozeman, College of Education, Health & Human Development, 2017) Frost, Leanne Hadley; Chairperson, Graduate Committee: Carrie B. MyersThis qualitative grounded theory study explored how the dual enrollment (DE) experience influenced the socialization of students to become future college students at a small, rural two-year college. The researcher interviewed 40 students within one year of completing DE courses through the college. The population included students who passed and did not pass their DE courses, enrolled in college and did not enroll in college, and who had completed their DE coursework in one or more of three delivery modes: concurrently in the high school, online from the college, and on the college campus. The study found the DE experience did affect participants' socialization as future college students, largely due to their interactions with teachers, other students, and the environment. In addition, their ability to complete college-level coursework affected their self-efficacy. The students viewed DE as a 'transition' to college and recognized it was not 'the full college experience.' They also identified increased autonomy as part of becoming a college student. Differences among the three delivery modes existed, with the online format having the smallest effect on students' socialization. This grounded theory study followed a constructivist approach; therefore, the resultant theory has been influenced by the interpretations of the researcher.Item Exploring timeliness for accurate location recommendation on location-based social networks(Montana State University - Bozeman, College of Engineering, 2017) Xu, Yi; Chairperson, Graduate Committee: Qing YangAn individual's location history in the real world implies his or her interests and behaviors. Accordingly, people who share similar location histories are likely to have common interest and behavior. This thesis analyzes and understands the process of Collaborative Filtering (CF) approach, which mines an individual's preference from his/her geographic location histories and recommends locations based on the similarities between the user and others. We find that a CF-based recommendation process can be summarized as a sequence of multiplications between a transition matrix and visited-location matrix. The transition matrix is usually approximated by the user's interest matrix that reflect the similarity among users, regarding to their interest in visiting different locations. The visited-location matrix provides the history of visited locations of all users, which is currently available to the recommendation system. We find that recommendation results will converge if and only if the transition matrix remains unchanged; otherwise, the recommendations will be valid for only a certain period of time. Based on our analysis, a novel location-based accurate recommendation (LAR) method is proposed, which considers the semantic meaning and category information of locations, as well as the timeliness of recommending results, to make accurate recommendations. We evaluated the precision and recall rates of LAR, using a large-scale real-world data set collected from Brightkite. Evaluation results confirm that LAR offers more accurate recommendations, comparing to the state-of-art approaches.Item Trust assessment in online social networks(Montana State University - Bozeman, College of Engineering, 2017) Liu, Guangchi; Chairperson, Graduate Committee: Qing YangAssessing trust in online social networks (OSNs) is critical for many applications such as online marketing and network security. It is a challenging problem, however, due to the difficulties of handling complex social network topologies and conducting accurate assessment in these topologies. To address these challenges, we model trust by proposing the three-valued subjective logic (3VSL) model. 3VSL properly models the uncertainties that exist in trust, thus is able to compute trust in arbitrary graphs. We theoretically prove the capability of 3VSL based on the Dirichlet-Categorical (DC) distribution and its correctness in arbitrary OSN topologies. Based on the 3VSL model, we further design the AssessTrust (AT) algorithm to accurately compute the trust between any two users connected in an OSN. AT is able to accurately conduct one-to-one trustworthiness, however, it is inefficient in addressing the massive trust assessment (MTA) problem, i.e., computing one-to-many trustworthiness in OSNs. MTA plays a vital role in OSNs, e.g., identifying trustworthy opinions in a crowdsourcing system. If the AssessTrust algorithm is applied directly to solve the MTA problem, its time complexity is exponential. To efficiently address MTA, we propose the OpinionWalk algorithm that yields an polynomial-time complexity. OpinionWalk uses a matrix to represent a social network's topology and a vector to store the trustworthiness of all users in the network. The vector is iteratively updated when the algorithm 'walks' through the entire network. To validate the 3VSL model, we first conduct a numerical analysis. An online survey system is then implemented to validate the correctness and accuracy of 3VSL in the real world. Finally, we validate 3VSL against two real-world OSN datasets: Advogato and Pretty Good Privacy (PGP). Experimental results indicate that 3VSL can accurately model the trust between any pair of indirectly connected users in the Advogato and PGP. To evaluate the performance of the AssessTrust and OpinionWalk algorithms, we use the same datasets. Compared to the state-of-art solutions, e.g., EigenTrust and MoleTrust, OpinionWalk yields the same order of time complexity and a higher accuracy in trust assessment.Item Finding disjoint dense clubs in an undirected graph(Montana State University - Bozeman, College of Engineering, 2016) Zou, Peng; Chairperson, Graduate Committee: Binhai ZhuFor over a decade, software like Twitter, Facebook and WeChat have changed people's lives by creating social groups and networks easily. They give people a new convenient 'world' where we can share everything that happens around us, and social networks have grown enormously in recent years. In essence, social networks are full of data and have become an indispensable part of our life. Trust is an important feature of the relationship between two users in a social network. With the development of social networks, the trust among its members has become a big issue. In a social network, the trust among its members usually cannot be carried over many users. In the corresponding social network modeled as a graph, a user is denoted by a vertex and an edge between two vertices means that these two users communicate a lot above some threshold and they trust each other. An online social community is usually corresponding to a dense region in such a graph. A complex social network is usually composed of several groups/communities (the regions with a lot of edges), and this characterization of community structure means the appearance of densely connected groups of vertices, with only sparse connections between groups. For analyzing the structure of social networks and the relationship between users, it is important to find disjoint groups/communities with a small diameter and with a decent size, formally called dense clubs in this thesis. We focus on handling this NP-complete problem in this thesis. First, from the parameterized computational complexity point of view, we show that this problem does not admit a polynomial kernel (implying that it is unlikely to apply some reduction rules to obtain a practically small problem size). Then, we focus on the dual version of the problem, i.e., deleting 'd' vertices to obtain some disjoint dense clubs. We show that this dual problem admits a simple FPT algorithm using a bounded search tree method (the running time is still too high for practical datasets). Finally, we combine a simple reduction rule together with some heuristic methods to obtain a practical solution (verified by extensive testing on practical datasets). Empirical results show that this heuristic algorithm is very sensitive to all parameters. This algorithm is suitable on graphs which have a mixture of dense and sparse regions. These graphs are very common in the real world.Item Social support in pediatric oncology(Montana State University - Bozeman, College of Nursing, 1996) Roope, Beverly Colleen; Chairperson, Graduate Committee: Ardella Fraley