arXiv subjects

Ixandra Achitouv

Publications and source records attributed to Ixandra Achitouv.

At least 19 recordsLinked to original sources

arXiv2026

D-MODD: A Diffusion Model of Opinion Dynamics Derived from Online Data

We present the first empirical derivation of a continuous-time stochastic model for real-world opinion dynamics. Using longitudinal social-media data to infer users opinion on a binary climate-change topic, we reconstruct the underlying drift and diffusion functions governing individual opinion updates. We show that the observed dynamics are well described by a Langevin-type stochastic differential equation, with persistent attractor basins and spatially sensitive drift and diffusion terms. The empirically inferred one-step transition probabilities closely reproduce the transition kernel generated from the D-MODD model we introduce. Our results provide the first direct evidence that online opinion dynamics on a polarized topic admit a Markovian description at the operator level, with empirically reconstructed transition kernels accurately reproduced by a data-driven Langevin model, bridging sociophysics, behavioral data, and complex-systems modeling.

physics.soc-ph↗

arXiv2025

Unveiling Political Influence Through Social Media: Network and Causal Dynamics in the 2022 French Presidential Election

During the 2022 French presidential election, we collected daily Twitter messages on key topics posted by political candidates and their close networks. Using a data-driven approach, we analyze interactions among political parties, identifying central topics that shape the landscape of political debate. Moving beyond traditional correlation analyses, we apply a causal inference technique: Convergent Cross Mapping, to uncover directional influences among political communities, revealing how some parties are more likely to initiate changes in activity while others tend to respond. This approach allows us to distinguish true influence from mere correlation, highlighting asymmetric relationships and hidden dynamics within the social media political network. Our findings demonstrate how specific issues, such as health and foreign policy, act as catalysts for cross-party influence, particularly during critical election phases. These insights provide a novel framework for understanding political discourse dynamics and have practical implications for campaign strategists and media analysts seeking to monitor and respond to shifts in political influence in real time.

cs.SI↗

arXiv2025

Constructing the low-temperature phase diagram for the $2+p$-quantum spin glass using the nonperturbative renormalization group

In this paper, we use a nonperturbative renormalization group approach to construct the dynamical phase space of a quantum spin glass in the large $N$ limit. The disordered Hamiltonian is of ``$2 + p$" type, and we perform a coarse-graining procedure over the Wigner spectrum for the matrix-like disorder. The phase space reconstruction relies on phase transitions derived from the Luttinger-Ward functional, which accounts for interactions that are forbidden by perturbation theory. Various phases are identified, characterized by large correlations between replicas and/or the breaking of time translation symmetry.

cond-mat.dis-nn↗

arXiv2025

Time-translation invariance symmetry breaking hidden by finite-scale singularities

In this paper, we consider a renormalization group perspective on the quantum dynamics of a particle moving in the Euclidean $\mathbb{R}^N$ space through the complex landscape provided by a disordered Hamiltonian of type $2+p$. We focus on the large $N$ limit, where the coarse-graining procedure is unconventional: it is based on the Wigner spectrum of the rank-2 disorder. The main consequence of this choice is that canonical dimensions depend on the scale, and the flow equations fail to become autonomous, preventing the existence of global fixed points. One of the main features of the underlying renormalization group flow is the existence of finite-scale singularities for initial conditions sufficiently close to the Gaussian region and for rank-$p$ disorder intensity large enough. Using the Luttinger-Ward formalism, we show that these finite-scale singularities hide (and should be resolved by) a phase transition that breaks time-translation invariance.

cond-mat.dis-nn↗

arXiv2024

Signal inference in financial stock return correlations through phase-ordering kinetics in the quenched regime

Financial stock return correlations have been analyzed through the lens of random matrix theory to differentiate the underlying signal from spurious correlations. The continuous spectrum of the eigenvalue distribution derived from the stock return correlation matrix typically aligns with a rescaled Marchenko-Pastur distribution, indicating no detectable signal. In this study, we introduce a stochastic field theory model to establish a detection threshold for signals present in the limit where the eigenvalues are within the continuous spectrum, which itself closely resembles that of a random matrix where standard methods such as principal component analysis fail to infer a signal. We then apply our method to Standard & Poor's 500 financial stocks' return correlations, detecting the presence of a signal in the largest eigenvalues within the continuous spectrum.

q-fin.ST↗

arXiv2024

Dynamical analysis of financial stocks network: improving forecasting using network properties

Applying a network analysis to stock return correlations, we study the dynamical properties of the network and how they correlate with the market return, finding meaningful variables that partially capture the complex dynamical processes of stock interactions and the market structure. We then use the individual properties of stocks within the network along with the global ones, to find correlations with the future returns of individual S&P 500 stocks. Applying these properties as input variables for forecasting, we find a 50% improvement on the R2score in the prediction of stock returns on long time scales (per year), and 3% on short time scales (2 days), relative to baseline models without network variables.

q-fin.ST↗

arXiv2024

Inferring financial stock returns correlation from complex network analysis

Financial stock returns correlations have been studied in the prism of random matrix theory, to distinguish the signal from the "noise". Eigenvalues of the matrix that are above the rescaled Marchenko Pastur distribution can be interpreted as collective modes behavior while the modes under are usually considered as noise. In this analysis we use complex network analysis to simulate the "noise" and the "market" component of the return correlations, by introducing some meaningful correlations in simulated geometric Brownian motion for the stocks. We find that the returns correlation matrix is dominated by stocks with high eigenvector centrality and clustering found in the network. We then use simulated "market" random walks to build an optimal portfolio and find that the overall return performs better than using the historical mean-variance data, up to 50% on short time scale.

q-fin.ST↗

arXiv2024

Testing network clustering algorithms with Natural Language Processing

The advent of online social networks has led to the development of an abundant literature on the study of online social groups and their relationship to individuals' personalities as revealed by their textual productions. Social structures are inferred from a wide range of social interactions. Those interactions form complex -- sometimes multi-layered -- networks, on which community detection algorithms are applied to extract higher order structures. The choice of the community detection algorithm is however hardily questioned in relation with the cultural production of the individual they classify. In this work, we assume the entangled nature of social networks and their cultural production to propose a definition of cultural based online social groups as sets of individuals whose online production can be categorized as social group-related. We take advantage of this apparently self-referential description of online social groups with a hybrid methodology that combines a community detection algorithm and a natural language processing classification algorithm. A key result of this analysis is the possibility to score community detection algorithms using their agreement with the natural language processing classification. A second result is that we can assign the opinion of a random user at >85% accuracy.

cs.SI↗

arXiv2024

Dynamical evolution of social network polarization and its impact on the propagation of a virus

The COVID-19 pandemic that emerged in 2020 has highlighted the complex interplay between vaccine hesitancy and societal polarization. In this study, we analyse the dynamical polarization within a social network as well as the network properties before and after a vaccine was made available. Our results show that as the network evolves from a less structured state to one with more clustered communities. Then using an agent-based modeling approach, we simulate the propagation of a virus in a polarized society by assigning vaccines to pro-vaccine individuals and none to the anti-vaccine individuals. We compare this propagation to the case where the same number of vaccines is distributed homogeneously across the population. In polarized networks, we observe a significantly more widespread diffusion of the virus, highlighting the importance of considering polarization for epidemic forecasting.

cs.SI↗

arXiv2023

Natural Language Processing for Financial Regulation

This article provides an understanding of Natural Language Processing techniques in the framework of financial regulation, more specifically in order to perform semantic matching search between rules and policy when no dataset is available for supervised learning. We outline how to outperform simple pre-trained sentences-transformer models using freely available resources and explain the mathematical concepts behind the key building blocks of Natural Language Processing.

cs.CL↗

arXiv2022

Propagation of epidemics in a polarized society: impact of clustering among unvaccinated individuals

Polarization of opinions about vaccination can have a negative impact on pandemic control. In this work we quantify this negative impact for the transmission of COVID-19, using an agent based simulation in an heterogeneous population with multi-type networks, representing different types of social interactions. We show that the clustering of unvaccinated individuals, associated with polarization of opinion, can lead to significant differences in the evolution of the pandemic compared to deterministic model predictions. Under our realistic baseline scenario these differences are a 33pc increase of the effective reproduction number, a 157pc increase of infections at the peak and a 30pc increase in the final cumulative attack rate.

physics.soc-ph↗

arXiv2019

New constraints on the linear growth rate using cosmic voids in the SDSS DR12 datasets

We present a new analysis of the inferred growth rate of cosmic structure measured around voids, using the LOWZ and the CMASS samples in the twelfth data release (DR12) of SDSS. Using a simple multipole analysis we recover a value consistent with $Λ$CDM for the inferred linear growth rate normalized by the linear bias: the $β$ parameter. This is true in both the mock catalogues and the data, where we find $β=0.33\pm0.06$ for the LOWZ sample and $β=0.36\pm0.05$ for the CMASS sample. This work demonstrates that we can expect redshift-space distortions around voids to provide unbiased and accurate constraints on the growth rate, complementary to galaxy clustering, using simple linear modelling.

astro-ph.CO↗

arXiv2018

The environmental dependence of the baryon acoustic peak in the Baryon Oscillation Spectroscopic Survey CMASS sample

The environmental dependence of galaxy clustering encodes information about the physical processes governing the growth of cosmic structure. We analyze the baryon acoustic peak as a function of environment in the galaxy correlation function of the Baryon Oscillation Spectroscopic Survey CMASS sample. Dividing the sample into three subsets by smoothed local overdensity, we detect acoustic peaks in the six separate auto-correlation and cross-correlation functions of the sub-samples. Fitting models to these correlation functions, calibrated by mock galaxy and dark matter catalogues, we find that the inferred distance scale is independent of environment, and consistent with the result of analyzing the combined sample. The shape of the baryon acoustic feature, and the accuracy of density-field reconstruction in the Zeldovich approximation, varies with environment. By up-weighting underdense regions and down-weighting overdense regions in their contribution to the full-sample correlation function, by up to 50%, we achieve a fractional improvement of a few per cent in the precision of baryon acoustic oscillation fits to the CMASS data and mock catalogues: the scatter in the preferred-scale fits to the ensemble of mocks improves from 1.45% to 1.34% (pre-reconstruction) and 1.03% to 1.00% (post-reconstruction). These results are consistent with the notion that the acoustic peak is sharper in underdense environments.

astro-ph.CO↗

arXiv2018

Modelling the environmental dependence of the growth rate

The growth rate of cosmic structure is a powerful cosmological probe for extracting information on the gravitational interactions and dark energy. In the late time Universe, the growth rate becomes non-linear and is usually probed by measuring the two point statistics of galaxy clustering in redshift space up to a limited scale, retaining the constraint on the linear growth rate f. In this letter, we present an alternative method to analyse the growth of structure in terms of local densities, i.e. f(Delta). Using N-body simulations, we measure the function of f(Delta) and show that structure grows faster in high density regions and slower in low density regions. We demonstrate that f(Delta) can be modelled using a log-normal Monte Carlo Random Walk approach, which provides a means to extract cosmological information from f(Delta). We discuss prospects for applying this approach to galaxy surveys.

astro-ph.CO↗

arXiv2018

The Taipan Galaxy Survey: Scientific Goals and Observing Strategy

Taipan is a multi-object spectroscopic galaxy survey starting in 2017 that will cover 2pi steradians over the southern sky, and obtain optical spectra for about two million galaxies out to z<0.4. Taipan will use the newly-refurbished 1.2m UK Schmidt Telescope at Siding Spring Observatory with the new TAIPAN instrument, which includes an innovative 'Starbugs' positioning system capable of rapidly and simultaneously deploying up to 150 spectroscopic fibres (and up to 300 with a proposed upgrade) over the 6-deg diameter focal plane, and a purpose-built spectrograph operating from 370 to 870nm with resolving power R>2000. The main scientific goals of Taipan are: (i) to measure the distance scale of the Universe (primarily governed by the local expansion rate, H_0) to 1% precision, and the structure growth rate of structure to 5%; (ii) to make the most extensive map yet constructed of the mass distribution and motions in the local Universe, using peculiar velocities based on improved Fundamental Plane distances, which will enable sensitive tests of gravitational physics; and (iii) to deliver a legacy sample of low-redshift galaxies as a unique laboratory for studying galaxy evolution as a function of mass and environment. The final survey, which will be completed within 5 years, will consist of a complete magnitude-limited sample (i<17) of about 1.2x10^6 galaxies, supplemented by an extension to higher redshifts and fainter magnitudes (i<18.1) of a luminous red galaxy sample of about 0.8x10^6 galaxies. Observations and data processing will be carried out remotely and in a fully-automated way, using a purpose-built automated 'virtual observer' software and an automated data reduction pipeline. The Taipan survey is deliberately designed to maximise its legacy value, by complementing and enhancing current and planned surveys of the southern sky at wavelengths from the optical to the radio.

astro-ph.GA↗

arXiv2017

An improved model of redshift-space distortions around voids: application to quintessence dark energy

Using cosmic voids to probe the growth rate of cosmic structure, and hence the nature of dark energy, is particularly interesting in the context of modified gravity theories that rely on the screening mechanism. In this work we improve the modelling of redshift-space distortions around voids in the dark matter density field, and thus reduce systematic errors in the derivation of cosmological parameters. We also show how specific types of voids can be used to better probe the growth rate, using a flexible void finder. We apply our results to test for a quintessence type of dark energy vs. a LCDM model, and find a good agreement with the fiducial cosmology after implementing an analytical correction to the radial velocity profiles around voids. We additionally outline characteristic imprints of dark energy in the dark matter velocity distributions around voids.

astro-ph.CO↗

arXiv2017

Testing the imprint of non-standard cosmologies on void profiles using Monte Carlo random walks

Using a Monte Carlo random walks of a log-normal distribution, we show how to qualitatively study void properties for non-standard cosmologies. We apply this method to an f(R) modified gravity model and recover the N-body simulation results of (Achitouv et al. 2016) for the void profiles and their deviation from GR. This method can potentially be extended to study other properties of the large scale structures such as the abundance of voids or overdense environments. We also introduce a new way to identify voids in the cosmic web, using only a few measurements of the density fluctuations around random positions. This algorithm allows to select voids with specific profiles and radii. As a consequence, we can target classes of voids with higher differences between f(R) and standard gravity void profiles. Finally we apply our void criteria to galaxy mock catalogues and discuss how the flexibility of our void finder can be used to reduce systematics errors when probing the growth rate in the galaxy-void correlation function.

astro-ph.CO↗

arXiv2017

Consistency of the growth rate in different environments with the 6dF Galaxy Survey: measurement of the void-galaxy and galaxy-galaxy correlation functions

We present a new test of gravitational physics by comparing the growth rate of cosmic structure measured around voids with that measured around galaxies in the same large-scale structure dataset, the low-redshift 6-degree Field Galaxy Survey. By fitting a self-consistent Redshift Space Distortion model to the 2D galaxy-galaxy and void-galaxy correlation functions, we recover growth rate values fσ_8 = 0.42 \pm 0.06 and 0.39 \pm 0.11, respectively. The environmental-dependence of cosmological statistics can potentially discriminate between modified-gravity scenarios which modulate the growth rate as a function of scale or environment and test the underlying assumptions of homogeneity and isotropy.

astro-ph.CO↗