  In the last few decades, Database Management Systems (DBMSs) became powerful
tools for storing large amount of data and executing complex queries over them.
In the recent years, the growing amount of unstructured or semi-structured data
has seen a shift from representing data in the relational model towards
alternative data models. Graph Databases and Graph Database Management Systems
(GDBMSs) have seen an increase in use due to their ability to manage
highly-interconnected, continuously evolving data.
  This thesis is a documentation of the work done in implementing a system to
identify clusters in graph modeled data using a Label Propagation Community
Detection Algorithm. The graph was built using datasets of academic
publications in the field of Computer Science obtained from dblp.org . The
system developed is a FullStack WebApp consisting of a web-based user
interface, an API and the data (nodes, edges, graph) stored in a Graph Database
Management System (GDBMS).
  Described in this document are: - the process of manipulation pre-import and
import of the data in a Graph Database Management System (GDBMS) such as
ArangoDB, creation of nodes, relations (edges) between the nodes and a graph
composed of these nodes and edges; - the GraphQL API implemented in NodeJS to
request data from the Graph Database Management System (GDBMS); - the frontend
interface made with TypeScript and React consisting of the search
functionalities and ability to visualize results in Cytoscape Network Graphs; -
the Label Propagation Community Detection Algorithm execution on the graph, the
found clusters which are stored and visualized to the user whenever requested.
  This thesis hopes to contribute with a practical hands-on approach on the
graph representation, integration and analysis of interconnected data.
