A graph database application to analyse social networks

The aim of the thesis is finding suspicious persons or vessel behaviors via Social Network Analysis using Neo4j as Graph database. To detect relationship between suspicious individuals and vessels by building a visual relation network with graph mining study to analyses social networks in marine vessels data for intelligence departments. For example, in the x-ship, 50 people were employed, were they involved in crime, or did they work on ships associated with another crime? To deal with this question drawing a visual network show the answers. It is aimed to create a database collected through the relational database and that includes crime events and related person data on the sea in the form of social networks and view the social networks on this database. SNA provides a roof for the simulation and presentation of a case which interacting units and their relationships. It involves a group of methods and tools to data collection, classification, pattern identification, prediction and visualization. There are limitations of crime networks like accessing the data is restricted reason of the sensitivity of the information and the "innocence factor", meaning that a person who has been suspected or charged for a crime might in fact be innocent and been registered wrongly. Due to these restrictions, the comments of individuals on involvement in crime have been transferred to law-enforcement. In parallel with this purpose, by choosing graph database model that gains a major advantage over traditional databases in social network modelling, an event-person database was built in the Neo4j platform and it is aimed to circulate the relevant authorities on the network by designing a web interface.