arXiv subjects

Rohit Sahasrabuddhe

Publications and source records attributed to Rohit Sahasrabuddhe.

9 recordsLinked to original sources

arXiv2026

Structure-aware divergences for comparing probability distributions

Many natural and social science systems are described using probability distributions over elements that are related to each other: for instance, occupations with shared skills or species with similar traits. Standard information theory quantities such as entropies and $f$-divergences treat elements interchangeably and are blind to the similarity structure. We introduce a family of divergences that are sensitive to the geometry of the underlying domain. By virtue of being the Bregman divergences of structure-aware entropies, they provide a framework that retains several advantages of Kullback-Leibler divergence and Shannon entropy. Structure-aware divergences recover planted patterns in a synthetic clustering task that conventional divergences miss and are orders of magnitude faster than optimal transport distances. We demonstrate their applicability in economic geography and ecology, where structure plays an important role. Modelling different notions of occupation relatedness yields qualitatively different regionalisations of their geographic distribution. Our methods also reproduce established insights into functional $β$-diversity in ecology obtained with optimal transport methods.

cs.IT↗

arXiv2026

From First Principles to Multi-scale Decomposition:Mutual Information as a Segregation Index

Segregation is a multi-scale phenomenon that requires careful measurement. A segregation index implicitly defines how the demographic compositions of locations are compared. We identify two properties -- mean-minimisation and invariance -- that uniquely characterise the Kullback-Leibler divergence as a measure of demographic difference. Mean-minimiser makes the comparison consistent with population aggregation and invariance ensures that it behaves intuitively under demographic coarse-graining. The corresponding segregation index is mutual information, which can be decomposed across geographic and demographic scales to identify the contributions of regions and supergroups. We demonstrate how this reveals insights into ethnic residential segregation in England and Wales that would be inaccessible otherwise. By deriving mutual information from first principles, we identify situations in which it is the only suitable segregation index, and provide open source software to support multi-scale analysis.

physics.soc-ph↗

arXiv2025

Mapping memory-biased dynamics with compact models reveals overlapping communities in large networks

Many real-world systems, from social networks to protein-protein interactions and species distributions, exhibit overlapping flow-based communities that reflect their functional organisation. However, reliably identifying such overlapping flow-based communities requires higher-order relational data, which are often unavailable. To address this challenge, we capitalise on the flow model underpinning the representation-learning algorithm node2vec and model higher-order flows through memory-biased random walks on first-order networks. Instead of simulating these walks, we model their higher-order dynamic constraints with compact models and control model complexity with an information-theoretic approach. Using the map equation framework, we identify overlapping modules in the resulting higher-order networks. Our compact-model approach proves robust across synthetic benchmark networks, reveals interpretable overlapping communities in empirical networks, and scales to large networks.

physics.soc-ph↗

arXiv2025

Concise network models of memory dynamics reveal explainable patterns in path data

Networks are a powerful tool to model the structure and dynamics of complex systems across scales. Direct connections between system components are often represented as edges, while paths and walks capture indirect interactions. This approach assumes that flows in the system are sequences of independent transitions. Path data from real-world systems often have higher-order dependencies, which require more sophisticated models. In this work, we propose a method to construct concise networks from path data that interpolate between first and second-order models. We prioritise simplicity and interpretability by creating state nodes that capture latent modes of second-order effects and introducing an interpretable measure to balance model size and accuracy. In both synthetic and real-world applications, our method reveals large-scale memory patterns and constructs concise networks that provide insights beyond the first-order model at the fraction of the size of a second-order model.

physics.soc-ph↗

arXiv2024

Modularity, Hierarchical Flows and Symmetry of the Drosophila Connectome

This report investigates the modular organisation of the Central region in the Drosophila connectome. We identify groups of neurones amongst which information circulates rapidly before spreading to the rest of the network using Infomap. We find that information flows along pathways linking distant neurones, forming modules that span across the brain. Remarkably, these modules, derived solely from neuronal connectivity patterns, exhibit a striking left-right symmetry in their spatial distribution as well as in their connections. We also identify a hierarchical structure at the coarse-grained scale of these modules, demonstrating the directional nature of information flow in the system.

q-bio.NC↗

arXiv2024

Detection of anomalous spatio-temporal patterns of app traffic in response to catastrophic events

In this work, we uncover patterns of usage mobile phone applications and information spread in response to perturbations caused by unprecedented events. We focus on categorizing patterns of response in both space and time and tracking their relaxation over time. To this end, we use the NetMob2023 Data Challenge dataset, which provides mobile phone applications traffic volume data for several cities in France at a spatial resolution of 100$m^2$ and a time resolution of 15 minutes for a time period ranging from March to May 2019. We analyze the spread of information before, during, and after the catastrophic Notre-Dame fire on April 15th and a bombing that took place in the city centre of Lyon on May 24th using volume of data uploaded and downloaded to different mobile applications as a proxy of information transfer dynamics. We identify different clusters of information transfer dynamics in response to the Notre-Dame fire within the city of Paris as well as in other major French cities. We find a clear pattern of significantly above-baseline usage of the application Twitter (currently known as X) in Paris that radially spreads from the area surrounding the Notre-Dame cathedral to the rest of the city. We detect a similar pattern in the city of Lyon in response to the bombing. Further, we present a null model of radial information spread and develop methods of tracking radial patterns over time. Overall, we illustrate novel analytical methods we devise, showing how they enable a new perspective on mobile phone user response to unplanned catastrophic events, giving insight into how information spreads during a catastrophe in both time and space.

physics.soc-ph↗

arXiv2023

Spaces of innovation and venture formation: the case of biotech in the United Kingdom

Patents serve as valuable indicators of innovation and provide insights into the spaces of innovation and venture formation within geographic regions. In this study, we utilise patent data to examine the dynamics of innovation and venture formation in the biotech sector across the United Kingdom (UK). By analysing patents, we identify key regions that drive biotech innovation in the UK. Our findings highlight the crucial role of biotech incubators in facilitating knowledge exchange between scientific research and industry. However, we observe that the incubators themselves do not significantly contribute to the diversity of innovations which might be due to the underlying effect of geographic proximity on the influences and impact of the patents. These insights contribute to our understanding of the historical development and future prospects of the biotech sector in the UK, emphasising the importance of promoting innovation diversity and fostering inclusive enterprise for achieving equitable economic growth.

physics.soc-ph↗

arXiv2021

From centre to centres: polycentric structures in individual mobility

The availability of large-scale datasets collected via mobile phones has opened up opportunities to study human mobility at an individual level. The granular nature of these datasets calls for the design of summary statistics that can be used to describe succinctly mobility patterns. In this work, we show that the radius of gyration, a popular summary statistic to quantify the extent of an individual's whereabouts, suffers from a sensitivity to outliers, and is incapable of capturing mobility organised around multiple centres. We propose a natural generalisation of the radius of gyration to a polycentric setting, as well as a novel metric to assess the quality of its description. With these notions, we propose a method to identify the centres in an individual's mobility and apply it to two large mobility datasets with socio-demographic features, showing that a polycentric description can capture features that a monocentric model is incapable of.

physics.soc-ph↗

arXiv2020

Modelling Non-Linear Consensus Dynamics on Hypergraphs

The basic interaction unit of many dynamical systems involves more than two nodes. In such situations where networks are not an appropriate modelling framework, it has recently become increasingly popular to turn to higher-order models, including hypergraphs. In this paper, we explore the non-linear dynamics of consensus on hypergraphs, allowing for interactions within hyperedges of any cardinality. After discussing the different ways in which non-linearities can be incorporated in the dynamical model, building on different sociological theories, we explore its mathematical properties and perform simulations to investigate them numerically. After focussing on synthetic hypergraphs, namely on block hypergraphs, we investigate the dynamics on real-world structures, and explore in detail the role of involvement and stubbornness on polarisation.

physics.soc-ph↗