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    Complex Network Analysis of EEG Signals of Epilepsy Patients
    (Institute of Electrical and Electronics Engineers Inc., 2024) Olgun, N.; Özkaynak, E.
    Electroencephalography (EEG) signals have significant potential for understanding brain dynamics and cognitive processes. Analyzing EEG signals using traditional methods often faces difficulties in revealing complex relationships between different brain regions. In this study, complex connection structures in EEG data of epilepsy patients were revealed and interpreted with the complex network theory, whose application area has expanded in recent years. The average degree, diameter, density, modularity and clustering coefficients of EEG signals of different epileptic conditions converted into complex networks were calculated. Differences between EEG signals were interpreted by interpreting the calculated metrics. Topological metrics obtained from EEG signals show that complex network theory is applicable in classifying epilepsy patients. © 2024 IEEE.
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    Link prediction on networks created from UEFA European competitions
    (Springer, 2020) Findik, O.; Özkaynak, E.
    Link prediction is widely used in network analysis to identify future links between nodes. Link prediction has an important place in terms of being applicable to many real-world networks with dynamic structure. Networks with dynamic structure, such as social networks, scientific collaboration networks and metabolic networks, are networks in which link prediction studies are performed. In addition, it is seen that there are few studies showing the feasibility of link prediction by creating networks from different areas. In this study, in order to show the applicability of link prediction processes in different fields link prediction was made by applying traditional link prediction methods in the networks formed from the data of football competitions played after the groups between the years 2004–2017 in the UEFA European League. The AUC metric was used to measure the success of forecasting. The results show that link prediction methods can be used in sports networks. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2020.