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Found 14 papers, 9 papers with code
Geodesic statistics for random network families
A key task in the study of networked systems is to derive local and global properties that impact connectivity, synchronizability, and robustness.
Modularity maximisation for graphons
The detection of communities or other meso-scale structures is a prominent topic in network science as it allows the identification of functional building blocks in complex systems.
Inference of a universal social scale and segregation measures using social connectivity kernels
How people connect with one another is a fundamental question in the social sciences, and the resulting social networks can have a profound impact on our daily lives.
Social and Information Networks Physics and Society Methodology
CompEngine: a self-organizing, living library of time-series data
Modern biomedical applications often involve time-series data, from high-throughput phenotyping of model organisms, through to individual disease diagnosis and treatment using biomedical data streams.
Databases Data Analysis, Statistics and Probability
catch22: CAnonical Time-series CHaracteristics
Capturing the dynamical properties of time series concisely as interpretable feature vectors can enable efficient clustering and classification for time-series applications across science and industry.
Mitochondrial network state scales mtDNA genetic dynamics
The role of the resulting mitochondrial networks in the time evolution of the cellular proportion of mutated mtDNA molecules (heteroplasmy), and cell-to-cell variability in heteroplasmy (heteroplasmy variance), remains incompletely understood.
Mitochondrial heterogeneity
The diverse sources of mitochondrial heterogeneity, as well as their increasingly recognised role in contributing to cellular heterogeneity, highlights the need for future single-cell mitochondrial measurements in the context of cellular noise studies.
Community detection in networks without observing edges
We develop a Bayesian hierarchical model to identify communities in networks for which we do not observe the edges directly, but instead observe a series of interdependent signals for each of the nodes.
hctsa: A Computational Framework for Automated Time-Series Phenotyping Using Massive Feature Extraction
Phenotype measurements frequently take the form of time series, but we currently lack a systematic method for relating these complex data streams to scientifically meaningful outcomes, such as relating the movement dynamics of organisms to their genotype or measurements of brain dynamics of a patient to their disease diagnosis.
Automatic time-series phenotyping using massive feature extraction
Across a far-reaching diversity of scientific and industrial applications, a general key problem involves relating the structure of time-series data to a meaningful outcome, such as detecting anomalous events from sensor recordings, or diagnosing patients from physiological time-series measurements like heart rate or brain activity.
Unified treatment of the asymptotics of asymmetric kernel density estimators
We extend balloon and sample-smoothing estimators, two types of variable-bandwidth kernel density estimators, by a shift parameter and derive their asymptotic properties.
Methodology Statistics Theory Statistics Theory
Highly comparative fetal heart rate analysis
A database of fetal heart rate (FHR) time series measured from 7221 patients during labor is analyzed with the aim of learning the types of features of these recordings that are informative of low cord pH.
Highly comparative feature-based time-series classification
A highly comparative, feature-based approach to time series classification is introduced that uses an extensive database of algorithms to extract thousands of interpretable features from time series.
Highly comparative time-series analysis: The empirical structure of time series and their methods
This new approach to comparing across diverse scientific data and methods allows us to organize time-series datasets automatically according to their properties, retrieve alternatives to particular analysis methods developed in other scientific disciplines, and automate the selection of useful methods for time-series classification and regression tasks.
