UMD Theses and Dissertations
Permanent URI for this collectionhttp://hdl.handle.net/1903/3
New submissions to the thesis/dissertation collections are added automatically as they are received from the Graduate School. Currently, the Graduate School deposits all theses and dissertations from a given semester after the official graduation date. This means that there may be up to a 4 month delay in the appearance of a given thesis/dissertation in DRUM.
More information is available at Theses and Dissertations at University of Maryland Libraries.
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Item TOPOLOGICAL ANALYSIS OF DISTANCE WEIGHTED NORTH AMERICAN RAILROAD NETWORK: EFFICIENCY, ECCENTRICITY, AND RELATED ATTRIBUTES(2023) Elsibaie, Sherief; Ayyub, Bilal M.; Reliability Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The North American railroad system can be well represented by a network with 302,943 links (track segments) and 250,388 nodes (stations, junctions, and waypoints), and other points of interest based on publicly accessible geographical information obtained from the Bureau of Transportation Statistics (BTS) and the Federal Railroad Administration (FRA). From this large network a slightly more consolidated subnetwork representing the major freight railroads and Amtrak was selected for analysis. Recent improvements in network and graph theory and improvements in all-pairs shortest path algorithms make it more feasible to process certain characteristics on large networks with reduced computation time and resources. The characteristics of networks at issue to support network-level risk and resilience studies include node efficiency, node eccentricity, and other attributes derived from those measures, such as network arithmetic efficiency, network geometric central node, radius, and diameter, and some distribution measures of the node characteristics. Rail distance weighting factors, representing the length of each rail line derived from BTS data, are mapped to corresponding links, and are used as link weights for the purpose of computing all pair shortest paths and subsequent characteristics. This study also compares the characteristics of North American railroad infrastructure subnetworks divided by Class I carriers, which are the largest railroad carriers classified by the Surface Transportation Board (STB) by annual operating revenue, and which together comprise most of the North American railroad network. These network characteristics can be used to inform placement of resources and plan for natural hazard and disaster scenarios. They relate to many practical applications such as network efficiency to distribute traffic and a network’s ability to recover from disruptions. The primary contribution of this thesis is the novel characterization of a detailed network representation of the North American railroad network and Class I carrier subnetworks, with established as well novel network characteristics.Item Do Intergovernmental Organizations Drive the Growth of Voluntary Cooperation on Climate Change?(2020) Sapatnekar, Poorti; Orr, Robert C; Public Policy; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Voluntary cooperation on climate change has grown rapidly since 2000, and presents a potential pathway to achieve the Paris Agreement goals. Many intergovernmental organizations (IGOs) seek to cultivate such multi-stakeholder partnerships or international cooperative initiatives in greenhouse gas-emitting sectors. But are IGOs an effective class of actors to do so? Evidence has lagged behind practice. This study fills three gaps in empirical knowledge: (1) Have large-scale efforts by IGOs (such as summits) to promote voluntary cooperation caused the growth of cooperation? If so, how? (2) By participating in partnerships within specific sectors, to what degree have IGOs influenced the growth of voluntary cooperation in those sectors? (3) How do large-scale IGO efforts interact with IGOs working within initiatives, and what is their combined effect on the quality of initiatives? This study analyses large-scale efforts during 2000-2015, and conducts three case studies, in forests, short-lived climate pollutants, and land transport. Two methods are employed: qualitative process tracing (including 71 interviews) and dynamic social network analysis of a dataset comprising 252 initiatives and their participants. Community detection and node centrality measures probe for influence over time. This study finds that: (1) Cooperative initiatives form sectoral ecosystems among inter-connected entities. New initiatives represent evolutionary changes to the strength—or quality—of cooperation within sectors. Thus, the quality of cooperation must be assessed at the sectoral level in addition to the initiative level; (2) Many IGOs participate in partnerships, but a select few have become central community-builders and these few wield strong influence over the evolution of the sectoral ecosystems; (3) IGOs (and governments) that have convening power and autonomy can choreograph a surge in the growth of voluntary cooperation. Of all IGOs, having established a ‘good offices’ role on climate change, the office of the UN Secretary-General is uniquely able to do so; (4) The surge requires six organizational attributes, which together characterize “collective choreography of cooperation”: strategic timing, high visibility, sectoral orientation, emphasis on ambitious cooperative commitments; subsidiarity, and leadership with centralized decision-making; and (5) Sustained and adequate institutional support is necessary for the gains of collective choreography to be impactful.Item Development of the Episodic Memory Network in Early Childhood: Insights from Graph Theoretical Analysis(2019) Botdorf, Morgan Anna; Riggins, Tracy; Psychology; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The hippocampal memory network has been identified in both children and adults and shown to be related to episodic memory ability. However, it remains unclear how its organization may differ across development, particularly during periods of large behavioral gains in memory ability. The goal of the present study was to utilize graph theoretical analysis to investigate the integration of the hippocampus within the memory network and segregation from other networks (i.e., fronto-parietal and cingulo-opercular attention networks) in the brain. Results indicated that with age, there was a general increase in connections between the hippocampus and both regions within the memory network and regions in other networks in the brain. These differences may contribute to improvements in memory typically observed in early childhood. Future analyses will examine relations with memory behavior and probe whether segregation is observed using other metrics, a sample of adult data, or other networks (e.g., sensorimotor).Item The Dynamics of Multi-Modal Networks(2012) Sharara, Hossam; Getoor, Lise; Computer Science; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The widespread study of networks in diverse domains, including social, technological, and scientific settings, has increased the interest in statistical and machine learning techniques for network analysis. Many of these networks are complex, involving more than one kind of entity, and multiple relationship types, both changing over time. While there have been many network analysis methods proposed for problems such as network evolution, community detection, information diffusion and opinion leader identification, the majority of these methods assume a single entity type, a single edge type and often no temporal dynamics. One of the main shortcomings of these traditional techniques is their inadequacy for capturing higher-order dependencies often present in real, complex networks. To address these shortcomings, I focus on analysis and inference in dynamic, multi-modal, multi-relational networks, containing multiple entity types (such as people, social groups, organizations, locations, etc.), and different relationship types (such as friendship, membership, affiliation, etc.). An example from social network theory is a network describing users, organizations and interest groups, where users have different types of ties among each other, such as friendship, family ties, etc., as well as affiliation and membership links with organizations and interest groups. By considering the complex structure of these networks rather than limiting the analysis to a single entity or relationship type, I show how we can build richer predictive models that provide better understanding of the network dynamics, and thus result in better quality predictions. In the first part of my dissertation, I address the problems of network evolution and clustering. For network evolution, I describe methods for modeling the interactions between different modalities, and propose a co-evolution model for social and affiliation networks. I then move to the problem of network clustering, where I propose a novel algorithm for clustering multi-modal, multi-relational data. The second part of my dissertation focuses on the temporal dynamics of interactions in complex networks, from both user-level and network-level perspectives. For the user-centric approach, I analyze the dynamics of user relationships with other entity types, proposing a measure of the "loyalty" a user shows for a given group or topic, based on her temporal interaction pattern. I then move to macroscopic-level approaches for analyzing the dynamic processes that occur on a network scale. I propose a new differential adaptive diffusion model for incorporating diversity and trust in the process of information diffusion on multi-modal, multi-relational networks. I also discuss the implications of the proposed diffusion model on designing new strategies for viral marketing and influential detection. I validate all the proposed methods on several real-world networks from multiple domains.