Topological Data Analysis Meets Information Theory. New Perspectives for the Analysis of Higher-Order Interactions in Complex Systems
A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Multidisciplinary Applications".
Deadline for manuscript submissions: 30 June 2024 | Viewed by 2384
Special Issue Editors
Interests: complexity; graph theory; information theory; statistical mechanics of networks; pattern detection; network reconstruction; graph combinatorics; systemic risk estimation; (mis)information spreading on social networks; functional brain network analysis; higher-order interactions
2. Institute for Advanced Studies (IAS), University of Amsterdam, 1105 AZ Amsterdam, The Netherlands
Interests: statistical mechanics; applied topology and geometry; network science; information theory; neuroscience
Interests: complex networks; graph theory; statistical physics; randomization techniques for graphs; higher-order interactions; social networks; economics; neuroscience
Special Issues, Collections and Topics in MDPI journals
Interests: complexity; topological data analysis; higher-order interactions; self-adaptive systems; deep learning; information theory; pattern recognition; interpretable machine learning; artificial intelligence; intelligent manufacturing; computer vision; signal processing; robotics
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Complexity lies in the rich variety of interactions taking place among the constituents of a given system. While research has mostly focused on pairwise relationships, recent work has shown that neglecting higher-order interactions can lead to a poor representation of the same systems. Examples of these "group-wise" interactions can be found in many fields, including neuroscience, biology, finance and sociology. Researchers have developed various approaches to quantify and investigate these interactions, including Topological Data Analysis and Information Theory. While the former focuses on defining the structures to be considered and their topological invariants, the latter deals with inferring higher-order interdependencies among the system constituents using, for instance, multivariate information theory. This Special Issue aims to bridge the perspectives of Complex Systems, Topological Data Analysis and Information Theory to better understand higher-order structures. Researchers are encouraged to explore commonalities between these approaches, their integration and the challenges they bring to application domains.
Both theoretical and applied contributions about the following topics fall within the scope of this Special Issue (though well-motivated systematic literature reviews on the same topics may be considered):
- Higher-order representations of interacting systems (e.g., hypergraphs, simplicial complexes);
- Topological data analysis and algebraic topology (e.g., persistent homology, dimension reduction);
- Multivariate information theory for higher-order inference (e.g., higher-order pattern detection);
- Generative models for higher-order interactions;
- Dynamical models of higher-order interactions;
- Entropy (e.g. persistent, Renyi, Shannon, transfer, Tsallis);
- Applications in economics and finance (e.g., cryptocurrencies), neurosciences (e.g., structural and functional brain networks, epilepsy, Alzheimer's disease and dementia), chemistry and biology (protein interactions), cybersecurity, artificial intelligence, machine/deep learning and robotics.
Dr. Tiziano Squartini
Dr. Fernando Antônio Nóbrega Santos
Dr. Rossana Mastrandrea
Dr. Marco Piangerelli
Guest Editors
Manuscript Submission Information
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Keywords
- complexity
- information theory
- topological data analysis
- higher-order interactions
- simplicial complexes
- hypergraphs
