New Trends in Graph and Complexity Based Data Analysis and Processing

Dear Colleagues,

Analysis and processing of data may significantly benefit from an appropriate relation of the sensing points, signal values, or analyzed objects. In this way, a new data domain in the form of a graph arises and naturally and comprehensively takes into account irregular data relations in the problem definition, together with the corresponding data connectivity. The introduction of new graph-based relations between the time-series samples, in well-defined time and space domains, may also lead to new insights into signal analysis and provide enhanced data processing. Although graph theory, as a branch of mathematics, was established a long time ago, it has been largely focused on analyzing the underlying graphs rather than signals and data on graphs, which turn out to be a hot recent research topic.

In addition, in recent years, classical complexity and entropy measures have been upgraded by new entropy-like measures focusing on multidimensional generalizations of the concepts with special attention directed to the quantification of similarity and coupling between time series and system components behind them. Recent research is focused on understanding the nature of complexity measures, their relationships, and proper parameter selection for various real-life applications.

Prof. Dr. Jonatan Lerga
Prof. Dr. Ljubisa Stankovic
Prof. Dr. Nicoletta Saulig
Prof. Dr. Cornel Ioana
Guest Editors

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