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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    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.
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    ANALYZING SEMI-LOCAL LINK COHESION TO DETECT COMMUNITIES AND ANOMALIES IN COMPLEX NETWORKS
    (2021) Schwartz, Catherine; Czaja, Wojciech; Applied Mathematics and Scientific Computation; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Link cohesion is a new type of metric used to assess how supported an edge is relativeto other edges, accounting for nearby alternate paths and associated vertex degrees. A deterministic, scalable, and parallelizable link cohesion metric was shown to be useful in supporting edge scoring and simplifying highly connected networks, making key cohesive subgraphs easier to detect. In this dissertation, the link cohesion metric and a modified version of the metric are analyzed to determine their ability to improve the communities detected in different types of networks when used as a pre-weighting step to traditional algorithms like the Louvain method. Additional observations are made on the utility of analyzing the modified metric to gain insights on whether a network has community structure. The two different link cohesion metrics are also used to create vertex-level features that have the potential for being useful in detecting fake accounts in online social networks. These features are used in conjunction with a new interpretable anomaly detection method which performs well with a small amount of training data, yielding the potential for humanin- the-loop interactions that can allow users to tailor the type of anomalies to prioritize.