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dc.contributor.advisorHoare, Terrien
dc.contributor.authorNarvekar, Sharvari Devdas
dc.date.accessioned2022-04-12T17:37:11Z
dc.date.available2022-04-12T17:37:11Z
dc.date.issued2022
dc.identifier.citationNarvekar, S. D. (2022). Contact Tracing using Graph Algorithms and exploring graph analytics in a Graph Database (Neo4j). Masters Thesis, Dublin Business School.en
dc.identifier.urihttps://esource.dbs.ie/handle/10788/4342
dc.description.abstractThe SARS-COVID-19 epidemic is still spreading rapidly globally. Contact Tracing has played an important role during this emergency in identifying the people most likely to be high spreaders of the virus. Graph Data Science has recently come to the forefront in analysing connected data on a knowledge graph. It included various types of algorithms supporting graph analytics and machine learning workflows. Using GDS we can execute these algorithms we find the optimized result in Neo4j platform This research explores the use of graph data science in the context of contact tracing. Common real-world scenarios are explored using Neo4j, the leading graph database management system. Graph Data Science algorithms including PageRank, betweenness centrality, Louvain community detection, label propagation, shortest path algorithms are explored, and results visualised. Results prove to be insightful.en
dc.language.isoenen
dc.publisherDublin Business Schoolen
dc.rightsItems in eSource are protected by copyright. Previously published items are made available in accordance with the copyright policy of the publisher/copyright holder.
dc.rights.urihttp://esource.dbs.ie/copyrighten
dc.subjectGraph databasesen
dc.subjectGraph algorithmsen
dc.subjectContact tracing (Epidemiology)en
dc.titleContact Tracing using Graph Algorithms and exploring graph analytics in a Graph Database (Neo4j)en
dc.typeThesisen
dc.rights.holderCopyright: The authoren
dc.type.degreenameMSc in Data Analytics
dc.type.degreelevelMSc


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