Although higher-order interactions are known to affect the typical state of dynamical processes giving rise to new collective behavior, how they drive the emergence of rare events and fluctuations is still an open problem. We investigate how fluctuations of a dynamical quantity of a random walk exploring a higher-order network arise over time. In the quenched case, where the hypergraph structure is fixed, through large deviation theory we show that the appearance of rare events is hampered in nodes with many higher-order interactions, and promoted elsewhere. Dynamical fluctuations are further boosted in an annealed scenario, where both the diffusion process and higher-order interactions evolve in time. Here, extreme fluctuations generated by optimal higher-order configurations can be predicted in the limit of a saddle-point approximation. Our study lays the groundwork for a wide and general theory of fluctuations and rare events in higher-order networks.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1103/PhysRevLett.133.107401 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Experimental datasets on synchronization in simplicial complexes.
Data Brief
December 2024
Complex System Group & GISC, Universidad Rey Juan Carlos, Madrid, 28933, Spain.
Some real-world phenomena and human-made problems have been modeled as networks where the objects form pairwise interactions. However, this is a limited approach when the existence of high-order interactions is inherent in a system, such as the brain, social networks and ecosystems. The way in which these high-order interactions affect the collective behavior of a complex system is still an open question.
View Article and Find Full Text PDFMulti-level cognitive state classification of learners using complex brain networks and interpretable machine learning.
Cogn Neurodyn
December 2025
National Engineering Research Center of Educational Big Data, Central China Normal University, Luoyu Road, Wuhan, 430079 Hubei China.
Identifying the cognitive state can help educators understand the evolving thought processes of learners, and it is important in promoting the development of higher-order thinking skills (HOTS). Cognitive neuroscience research identifies cognitive states by designing experimental tasks and recording electroencephalography (EEG) signals during task performance. However, most of the previous studies primarily concentrated on extracting features from individual channels in single-type tasks, ignoring the interconnection across channels.
View Article and Find Full Text PDFMass Spectrometry-Based Protein Footprinting for Protein Structure Characterization.
Acc Chem Res
January 2025
Department of Chemistry, Washington University, St. Louis, Missouri 63130, United States.
ConspectusProtein higher-order structure (HOS) is key to biological function because the mechanisms of protein machinery are encoded in protein three-dimensional structures. Mass spectrometry (MS)-based protein footprinting is advancing protein structure characterization by mapping solvent-accessible regions of proteins and changes in H-bonding, thereby providing higher order structural information. Footprinting provides insights into protein dynamics, conformational changes, and interactions, and when conducted in a differential way, can readily reveal those regions that undergo conformational change in response to perturbations such as ligand binding, mutation, thermal stress, or aggregation.
View Article and Find Full Text PDFDynamics of Nucleosomes and Chromatin Fibers Revealed by Single-Molecule Measurements.
BMB Rep
January 2025
Department of Biomedical Engineering, Ulsan National Institute of Science and Technology, Ulsan (44919), Republic of Korea.
The nucleosome is the fundamental structural unit of chromosome fibers. A DNA wraps around a histone octamer to form a nucleosome, while neighboring nucleosomes interact to form higher-order structures and fit gigabase-long DNAs into a small volume of the nucleus. Nucleosomes interrupt the access of transcription factors to a genomic region, and provide regulatory controls of gene expression.
View Article and Find Full Text PDFUnifying topological structure and self-attention mechanism for node classification in directed networks.
Sci Rep
January 2025
College of Computer and Information Engineering, Nanjing Tech University, Nanjing, 211800, China.
Graph data is essential for modeling complex relationships among entities. Graph Neural Networks (GNNs) have demonstrated effectiveness in processing low-order undirected graph data; however, in complex directed graphs, relationships between nodes extend beyond first-order connections and encompass higher-order relationships. Additionally, the asymmetry introduced by edge directionality further complicates node interactions, presenting greater challenges for extracting node information.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!