25 citations · 71 across the 15 of their papers we have counts for
16 papers
Graph-theoretic design of lasing networks for physical vision
Paul Obernolte, Jakub Dranczewski, Yixiu Yin +10
Physical neural networks perform learning through the intrinsic nonlinear dynamics of matter. Optimising their design presents a considerable challenge: complex many-body physics c…
Information-theoretic signatures of causality in Bayesian networks and hypergraphs
Sung En Chiang, Zhaolu Liu, Robert L. Peach +1
Analyzing causality in multivariate systems involves establishing how information is generated, distributed and combined. Traditional causal discovery frameworks are capable of mul…
Permutation-Free High-Order Interaction Tests
Zhaolu Liu, Robert L. Peach, Mauricio Barahona
Kernel-based hypothesis tests offer a flexible, non-parametric tool to detect high-order interactions in multivariate data, beyond pairwise relationships. Yet the scalability of su…
Information-Theoretic Measures on Lattices for High-Order Interactions
Zhaolu Liu, Mauricio Barahona, Robert L. Peach
Traditional measures based solely on pairwise associations often fail to capture the complex statistical structure of multivariate data. Existing approaches for identifying informa…
Interaction Measures, Partition Lattices and Kernel Tests for High-Order Interactions
Zhaolu Liu, Robert L. Peach, Pedro A. M. Mediano +1
Models that rely solely on pairwise relationships often fail to capture the complete statistical structure of the complex multivariate data found in diverse domains, such as socio-…
A unified framework for Simplicial Kuramoto models
Marco Nurisso, Alexis Arnaudon, Maxime Lucas +4
Simplicial Kuramoto models have emerged as a diverse and intriguing class of models describing oscillators on simplices rather than nodes. In this paper, we present a unified frame…
Kernel-based Joint Independence Tests for Multivariate Stationary and Non-stationary Time Series
Zhaolu Liu, Robert L. Peach, Felix Laumann +2
Multivariate time series data that capture the temporal evolution of interconnected systems are ubiquitous in diverse areas. Understanding the complex relationships and potential d…
Interpretable statistical representations of neural population dynamics and geometry
Adam Gosztolai, Robert L. Peach, Alexis Arnaudon +2
The dynamics of neuron populations commonly evolve on low-dimensional manifolds. Thus, we need methods that learn the dynamical processes over neural manifolds to infer interpretab…
PyGenStability: Multiscale community detection with generalized Markov Stability
Alexis Arnaudon, Juni Schindler, Robert L. Peach +4
We present PyGenStability, a general-use Python software package that provides a suite of analysis and visualisation tools for unsupervised multiscale community detection in graphs…
Connecting Hodge and Sakaguchi-Kuramoto: a mathematical framework for coupled oscillators on simplicial complexes
Alexis Arnaudon, Robert L. Peach, Giovanni Petri +1
We formulate a general Kuramoto model on weighted simplicial complexes where phases oscillators are supported on simplices of any order . Crucially, we introduce linear and non-…
Relative, local and global dimension in complex networks
Robert L. Peach, Alexis Arnaudon, Mauricio Barahona
Dimension is a fundamental property of objects and the space in which they are embedded. Yet ideal notions of dimension, as in Euclidean spaces, do not always translate to physical…
Network memory in the movement of hospital patients carrying drug-resistant bacteria
Ashleigh C. Myall, Robert L. Peach, Andrea Y. Weiße +4
Hospitals constitute highly interconnected systems that bring into contact an abundance of infectious pathogens and susceptible individuals, thus making infection outbreaks both co…
Data-driven modelling and characterisation of task completion sequences in online courses
Robert L. Peach, Sam F. Greenbury, Iain G. Johnston +3
The intrinsic temporality of learning demands the adoption of methodologies capable of exploiting time-series information. In this study we leverage the sequence data framework and…
Semi-supervised classification on graphs using explicit diffusion dynamics
Robert L. Peach, Alexis Arnaudon, Mauricio Barahona
Classification tasks based on feature vectors can be significantly improved by including within deep learning a graph that summarises pairwise relationships between the samples. In…
Scale-dependent measure of network centrality from diffusion dynamics
Alexis Arnaudon, Robert L. Peach, Mauricio Barahona
Classic measures of graph centrality capture distinct aspects of node importance, from the local (e.g., degree) to the global (e.g., closeness). Here we exploit the connection betw…
Data-driven unsupervised clustering of online learner behaviour
Robert L. Peach, Sophia N. Yaliraki, David Lefevre +1
The widespread adoption of online courses opens opportunities for the analysis of learner behaviour and for the optimisation of web-based material adapted to observed usage. Here w…