The importance of the knowledge acquired through scientific studies and publications discussing innovations has been constantly increasing in the recent decades. The reason is that these studies have a great impact on our modern society. Countless researchers put enormous effort into inventing something that potentially has the biggest impact on our lives, thereby making a name for themselves, and leaving their legacy to the coming generations. We should not forget the importance of supporting the findings with studies, as the reputation of the researcher is greatly based on his list of publications.
We can assume that the aim of the researchers is not to fake their index-numbers; but, there are occasions when excessively linking to each others publications the researchers are trying to achieve a deeper scientific impression. This thesis is focusing on the behaviour of a minor segment of researchers. The main goal of this paper is to identify the form and frequency of these citation collusions in todays academic papers.
In the first part of thesis-related studies are detailed theories, followed by the goal of this work, as well as requirements and operations of the data set. The link networks and mutual favors are described as well, which play a key role in this study. The hypothesis and related questions are closing this first part. The second part includes methods of the analysis: basic pattern finding methods using statistical and background information finishing with the design and implementation of the simulation framework.
The main section is about the data analysis. Findings are presented using graphs, each of them being explained in detail. The initial hypothesis are accepted or rejected here as well. In the last part, the conclusions of this paper are summarized and ideas are explained about possible future improvements that can be incorporated into the framework in order to increase its efficiency.