Thesis final v7

Comparative Analysis of Louvain and K-Means Methods for Community Detection By Firstname LastnameUniversity Name Abstract Community detection in the graphs of complex real network systems is a significant area of data science research. A community or a cluster is one that has many edges joining the vertices included within the cluster while fewer edges joining with the vertices not included. The criteria for inclusion in a community is based on the data of the vertices and edges. A system’s data helps form communities of a system. These communities help the data analysts in getting a high-level view of the system’s constituents. Such a view is crucial for executing behavioral analysis, taking managerial decision-making, strategizing marketing plans, recommendations, etc. A lot of community detection algorithms have been suggested in the research community. From these, the Louvain algorithm and K-Means algorithm are two popular algorithm choices. This paper performs a comparative analysis of the two community detection algorithms by using various real-world data sets of different complexities and evaluating their performance against them. The comparisons will highlight the strengths and limitations of each algorithm and suggest the ideal scenarios for their applications. Keywords: community detection, clustering, Louvain algorithm, K-means algorithm, data sets. Table of Contents TOC o "1-3" h z u 1.Introduction PAGEREF _Toc12514285 h 11.1.Related Works PAGEREF _Toc12514286 h 22.Background Concepts PAGEREF…

Free Thesis final v7 Dissertation Example