Unveiling the Future of Data-Driven Network Analysis: Beyond Pairwise Interactions

Network Physiology is an multidisciplinary field dedicated to uncovering the complexity of physiological interactions across diverse physiological systems and hierarchical networks to understand integrative functions in health and disease. Manuscripts submitted to this Research Topic should be in the context of Network Physiology and explicitly consider, utilize, or inform perspectives and methodologies from Network Physiology.This Research Topic envisions the future of multivariate data analysis in Network Physiology and beyond through the lens of high-order interactions. While it is nowadays acknowledged that the classical data-driven description of multivariate datasets based on standard graphs where nodes and links encode individual features and pairwise interactions cannot capture the variety of emergent phenomena in complex networks, several issues are still open about the design, validation, implementation and application of methods to detect and quantify high-order interactions involving three or more nodes in a network system. The present research topic aims at fostering interdisciplinary discussions about the characterization of high-order interactions in the fields of network physiology and neuroscience, as well as at expanding the outreach of multivariate data-driven approaches beyond these fields. Contributions are welcome covering (but not limited to) the following topics:• Information Dynamics and Information Decomposition• High-order Mechanisms and High-Order behaviors • Synergy and redundancy• Topological analysis: hypergraphs, simplicial complexes and beyond• Null models for high-order measures• Data-efficient estimation of high-order measures• Parametric modeling of multivariate datasets• Spectral analysis of oscillatory networks• Non-stationary analysis of dynamic networks• Applications to brain networks• Applications to cardiovascular, cardiorespiratory, cerebrovascular networks• High-order interactions and Artificial Intelligence Topic editors: Luca Faes, Hamed Azami, Jesus M Cortes, Thomas F. Varley Source: https://www.frontiersin.org/research-topics/76014/unveiling-the-future-of-data-driven-network-analysis-beyond-pairwise-interactions