ANALYZING SEMI-LOCAL LINK COHESION TO DETECT COMMUNITIES AND ANOMALIES IN COMPLEX NETWORKS

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dc.contributor.advisorCzaja, Wojciechen_US
dc.contributor.authorSchwartz, Catherineen_US
dc.contributor.departmentApplied Mathematics and Scientific Computationen_US
dc.contributor.publisherDigital Repository at the University of Marylanden_US
dc.contributor.publisherUniversity of Maryland (College Park, Md.)en_US
dc.date.accessioned2021-09-22T05:34:52Z
dc.date.available2021-09-22T05:34:52Z
dc.date.issued2021en_US
dc.description.abstractLink 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.en_US
dc.identifierhttps://doi.org/10.13016/2roq-aryo
dc.identifier.urihttp://hdl.handle.net/1903/27936
dc.language.isoenen_US
dc.subject.pqcontrolledApplied mathematicsen_US
dc.subject.pquncontrolledAnomaly Detectionen_US
dc.subject.pquncontrolledCommunity Detectionen_US
dc.subject.pquncontrolledGraph Theoryen_US
dc.subject.pquncontrolledNetwork Scienceen_US
dc.titleANALYZING SEMI-LOCAL LINK COHESION TO DETECT COMMUNITIES AND ANOMALIES IN COMPLEX NETWORKSen_US
dc.typeDissertationen_US

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