New Trends in Graph and Complexity Based Data Analysis and Processing, 2nd Edition

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 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 largely been focused on analyzing the underlying graphs rather than signals and data on graphs, which have turned 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 has focused on understanding the nature of complexity measures, their relationships, and proper parameter selection for various real-life applications.

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

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• graph and graph-based data classification and clustering
• graph neural networks
• graph topology learning from data
• dynamic graph structures
• vertex–frequency and wavelet analysis of signals on graphs
• graph complexity and the complexity of signals on graphs
• entropy and entropy-like measures
• complexity measures
• classical signal and image processing assisted by graph theory
• graph filtering and adaptive processing
• interpolation, subsampling and downscaling of graph signals and graphs
• applications of graph data processing
• applications of entropy and complexity-based measures