Predicting business ownership based on board collaboration networks

The purpose of this thesis was the realization of a prediction model, with which we could predict the change of ownership in a network. The prediction model is a result of the analysis process of board collaboration networks, where the networks are in fact a representation of data for stockholders and their presence in a certain company. The ownership is represented with a node's degree in the networks which were analyzed. In the model realization process, data processing and their proper representation in the form of a network is essential. For that purpose, we used the Java programming language, coupled with the Application programming interfaces (APIs) OWL and Gephi. The resulting networks, represented as graphs, needed to be further analyzed in order for us to acquire the node importance metrics in the network, which were crucial for the prediction process. The acquired metrics are the basis for various statistical, data mining and machine learning methods. The results of those methods lead us to the creation of the model that we imagined in the first place. The end result can be the basis for a desktop or a web application who could predict not just ownership change, but also other data