Theses and Dissertations from UMD

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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 give thesis/dissertation in DRUM

More information is available at Theses and Dissertations at University of Maryland Libraries.

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Author: search.filters.author.Schwartz, CatherineSubject: search.filters.subject.Anomaly Detection

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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.