Computer Science Theses and Dissertations
Permanent URI for this collectionhttp://hdl.handle.net/1903/2756
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Item 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.Item MAXIMIZING INFLUENCE OF SIMPLE AND COMPLEX CONTAGION ON REAL-WORLD NETWORKS(2020) Moores, Geoffrey; Srinivasan, Aravind; Computer Science; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Contagion spread over networks is used to model many important real-world processes from a wide variety of domains including epidemiology, marketing, and systems engineering. A large body of research provides strong theoretical guarantees on simple contagion models, but recent research identifies many real-world processes that feature complex contagions whose spread may depend on multiple exposures or other complex criteria. We present a rigorous study of real-world and artificial networks across simple and complex contagion models. We identify domain-dependent features of real-world networks extracted from publicly-available networks as a guide to solving contagion-related decision problems. We then examine the performance of multiple influence-maximization algorithms across a space of networks and contagion models to develop an experimentally justified guide of best practices for related problems. In particular, genetic algorithms are an extremely viable candidate for these problems, especially with complex graphs and processes.