arXiv subjects

Javier M. Buldú

Publications and source records attributed to Javier M. Buldú.

18 recordsLinked to original sources

arXiv2026

Vector field theory in motion: Revealing latent potentials in soccer dynamics

Team sports offer a natural laboratory for studying collective motion in competitive environments. While recent tracking technologies enabled detailed analysis of player and ball trajectories, most existing approaches rely on discrete events and local kinematic descriptors. This limits the possibility of studying team collective behavior. Here we introduce a physics-based framework to characterize collective dynamics using empirically reconstructed velocity vector fields defined over the pitch. Taking soccer as a paradigmatic example, we show that both ball and player movements generate smooth, predominantly irrotational vector fields. Each of these fields is generated by a potential providing compact, physically interpretable representations of collective motion and encoding how teams control, or constrain play across space during attacking and defensive phases in each match. Applied to an entire season of top-level competition, the resulting potentials reveal stable team-specific fingerprints and quantitative associations between potential gradients and effective ball progression.

physics.soc-ph↗

arXiv2026

Harder, shorter, sharper, forward: A comparison of women's and men's elite football gameplay (2020-2025)

Elite football is believed to have evolved in recent years, yet systematic evidence for the pace and form of that change remains sparse. Drawing on event-level records for 13,018 matches across ten top-tier men's and women's leagues in England, Spain, Germany, Italy, and the United States (2020-2025), we quantify match dynamics through two complementary lenses: conventional performance statistics and pitch-passing networks that track ball movement across spatial regions of the field. Between 2020 and 2025, average passing volume, pass accuracy, and the proportion of passes made under pressure all increased, with the largest year-on-year changes occurring in women's competitions. Network measures reveal that normalized outreach decreased, indicating teams increasingly concentrate ball circulation into shorter-range passing connections rather than wide spatial distribution. These trends are consistent across countries and tiers, yet persistent national differences indicate that stylistic diversity remains. Notably, women's competitions exhibit stronger rates of change across most metrics, consistent with an accelerating professionalization, while the systematic decline in network outreach across all competitions points to a sport-wide tactical convergence toward shorter, more concentrated passing structures.

physics.soc-ph↗

arXiv2025

A complex network perspective on brain disease

If brain anatomy and dynamics have a genuine complex network structure as it has become standard to posit, it is also reasonable to assume that such a structure should play a key role not only in brain function but also in brain dysfunction. However, exactly how network structure is implicated in brain damage and whether at least some pathologies can be thought of as "network diseases" is not entirely clear. Here we discuss ways in which a complex network representation can help characterising brain pathology, but also subjects' vulnerability to and likelihood of recovery from disease. We show how the way disease is defined is related to the way function is defined and this, in turn, determines which network property may be functionally relevant to brain disease. Thus, addressing brain disease "networkness" may shed light not only on brain pathology, with potential clinical implications, but also on functional brain activity, and what is functional in it.

q-bio.NC↗

arXiv2025

Biological detail and graph structure in network neuroscience

Endowing brain anatomy, dynamics, and function with a network structure is becoming standard in neuroscience. In its simplest form, a network is a collection of units and relationships between them. The pattern of relations among the units encodes numerous properties which have been shown to have a profound effect on networked systems' dynamics and function. In an effort to strike a balance between idealization and detail, network neuroscience studies typically involve simplifying assumptions at both neural and network modeling levels. However, the extent to which existing neural models depend on such approximations is as yet poorly understood. Here, we discuss whether and how increasing neurophysiological detail and generalizing the basic simple network structure often adopted in network neuroscience may help improve our understanding of brain phenomenology and function.

q-bio.NC↗

arXiv2020

The resumption of sports competitions after COVID-19 lockdown: The case of the Spanish football league

In this work, we present a stochastic discrete-time SEIR (Susceptible-Exposed-Infectious-Recovered) model adapted to describe the propagation of COVID-19 during a football tournament. Specifically, we are concerned about the re-start of the Spanish national football league, La Liga, which is currently -May 2020- stopped with 11 fixtures remaining. Our model includes two additional states of an individual, confined and quarantined, which are reached when an individual presents COVID-19 symptoms or has undergone a virus test with a positive result. The model also accounts for the interaction dynamics of players, considering three different sources of infection: the player social circle, the contact with his/her team colleagues during training sessions, and the interaction with rivals during a match. Our results highlight the influence of the days between matches, the frequency of virus tests and their sensitivity on the number of players infected at the end of the season. Following our findings, we finally present a variety of strategies to minimize the probability that COVID-19 propagates in case the season of La Liga was re-started after the current lockdown.

physics.soc-ph↗

arXiv2019

Using symbolic networks to analyse dynamical properties of disease outbreaks

We introduce a new methodology to analyze the evolution of epidemic time series, which is based on the construction of epidemic networks. First, we translate the time series into ordinal patterns containing information about local fluctuations of the disease prevalence. Each pattern is associated to a node of a network, whose (directed) connections arise from consecutive appearances in the series. The analysis of the network structure and the role of each pattern, allows classifying them according to the enhancement of entropy/complexity along the series, giving a different point of view about the evolution of a given disease.

physics.soc-ph↗

arXiv2018

Ordinal Synchronization: Using ordinal patterns to capture interdependencies between time series

We introduce Ordinal Synchronization ($OS$) as a new measure to quantify synchronization between dynamical systems. $OS$ is calculated from the extraction of the ordinal patterns related to two time series, their transformation into $D$-dimensional ordinal vectors and the adequate quantification of their alignment. $OS$ provides a fast and robust-to noise tool to assess synchronization without any implicit assumption about the distribution of data sets nor their dynamical properties, capturing in-phase and anti-phase synchronization. Furthermore, varying the length of the ordinal vectors required to compute $OS$ it is possible to detect synchronization at different time scales. We test the performance of $OS$ with data sets coming from unidirectionally coupled electronic Lorenz oscillators and brain imaging datasets obtained from magnetoencephalographic recordings, comparing the performance of $OS$ with other classical metrics that quantify synchronization between dynamical systems.

q-bio.QM↗

arXiv2018

Brain synchronizability, a false friend

Synchronization plays a fundamental role in healthy cognitive and motor function. However, how synchronization depends on the interplay between local dynamics, coupling and topology and how prone to synchronization a network with given topological organization is are still poorly understood issues. To investigate the synchronizability of both anatomical and functional brain networks various studies resorted to the Master Stability Function (MSF) formalism, an elegant tool which allows analysing the stability of synchronous states in a dynamical system consisting of many coupled oscillators. Here, we argue that brain dynamics does not fulfil the formal criteria under which synchronizability is usually quantified and, perhaps more importantly, what this measure itself quantifies refers to a global dynamical condition that never holds in the brain (not even in the most pathological conditions), and therefore no neurophysiological conclusions should be drawn based on it. We discuss the meaning of synchronizability and its applicability to neuroscience and propose alternative ways to quantify brain networks synchronization.

q-bio.NC↗

arXiv2018

Multiplex networks of musical artists: the effect of heterogeneous inter-layer links

The way the topological structure goes from a decoupled state into a coupled one in multiplex networks has been widely studied by means of analytical and numerical studies, involving models of artificial networks. In general, these experiments assume uniform interconnections between layers offering, on the one hand, an analytical treatment of the structural properties of multiplex networks but, on the other hand, loosing applicability to real networks where heterogeneity of the links' weights is an intrinsic feature. In this paper, we study 2-layer multiplex networks of musicians whose layers correspond to empirical datasets containing, and linking the information of: (i) collaboration between them and (ii) musical similarities. In our model, connections between the collaboration and similarity layers exist, but they are not ubiquitous for all nodes. Specifically, inter-layer links are created (and weighted) based on structural resemblances between the neighborhood of an artist, taking into account the level of interaction at each layer. Next, we evaluate the effect that the heterogeneity of the weights of the inter-layer links has on the structural properties of the whole network, namely the second smallest eigenvalue of the Laplacian matrix (algebraic connectivity). Our results show a transition in the value of the algebraic connectivity that is far from classical theoretical predictions where the weight of the inter-layer links is considered to be homogeneous.

physics.soc-ph↗

arXiv2017

Functional brain networks reveal the existence of cognitive reserve and the interplay between network topology and dynamics

We investigated how the organization of functional brain networks was related to cognitive reserve (CR) during a memory task in healthy aging. We obtained the magnetoencephalographic functional networks of 20 elders with a high or low CR level to analyse the differences at network features. We reported a negative correlation between synchronization of the whole network and CR, and observed differences both at the node and at the network level in: the average shortest path and the network outreach. Individuals with high CR required functional networks with lower links to successfully carry out the memory task. These results may indicate that those individuals with low CR level exhibited a dual pattern of compensation and network impairment, since their functioning was more energetically costly to perform the task as the high CR group. Additionally, we evaluated how the dynamical properties of the different brain regions were correlated to the network parameters obtaining that entropy was positively correlated with the strength and clustering coefficient, while complexity behaved conversely. Consequently, highly connected nodes of the functional networks showed a more stochastic and less complex signal. We consider that network approach may be a relevant tool to better understand brain functioning in aging.

q-bio.NC↗

arXiv2017

Frequency-based brain networks: From a multiplex framework to a full multilayer description

We explore how to study dynamical interactions between brain regions using functional multilayer networks whose layers represent the different frequency bands at which a brain operates. Specifically, we investigate the consequences of considering the brain as a multilayer network in which all brain regions can interact with each other at different frequency bands, instead of as a multiplex network, in which interactions between different frequency bands are only allowed within each brain region and not between them. We study the second smallest eigenvalue of the combinatorial supra-Laplacian matrix of the multilayer network in detail, and we thereby show that the heterogeneity of interlayer edges and, especially, the fraction of missing edges crucially modify the spectral properties of the multilayer network. We illustrate our results with both synthetic network models and real data sets obtained from resting state magnetoencephalography. Our work demonstrates an important issue in the construction of frequency-based multilayer brain networks.

q-bio.NC↗

arXiv2016

Beware of the Small-World neuroscientist!

The SW has undeniably been one of the most popular network descriptors in the neuroscience literature. Two main reasons for its lasting popularity are its apparent ease of computation and the intuitions it is thought to provide on how networked systems operate. Over the last few years, some pitfalls of the SW construct and, more generally, of network summary measures, have widely been acknowledged.

q-bio.NC↗

arXiv2013

Functional Hubs in Mild Cognitive Impairment

We investigate how hubs of functional brain networks are modified as a result of mild cognitive impairment (MCI), a condition causing a slight but noticeable decline in cognitive abilities, which sometimes precedes the onset of Alzheimer's disease. We used magnetoencephalography (MEG) to investigate the functional brain networks of a group of patients suffering from MCI and a control group of healthy subjects, during the execution of a short-term memory task. Couplings between brain sites were evaluated using synchronization likelihood, from which a network of functional interdependencies was constructed and the centrality, i.e. importance, of their nodes quantified. The results showed that, with respect to healthy controls, MCI patients were associated with decreases and increases in hub centrality respectively in occipital and central scalp regions, supporting the hypothesis that MCI modifies functional brain network topology, leading to more random structures.

q-bio.NC↗

arXiv2013

Successful strategies for competing networks

Competitive interactions represent one of the driving forces behind evolution and natural selection in biological and sociological systems. For example, animals in an ecosystem may vie for food or mates; in a market economy, firms may compete over the same group of customers; sensory stimuli may compete for limited neural resources in order to enter the focus of attention. Here, we derive rules based on the spectral properties of the network governing the competitive interactions between groups of agents organized in networks. In the scenario studied here the winner of the competition, and the time needed to prevail, essentially depend on the way a given network connects to its competitors and on its internal structure. Our results allow assessing the extent to which real networks optimize the outcome of their interaction, but also provide strategies through which competing networks can improve on their situation. The proposed approach is applicable to a wide range of systems that can be modeled as networks.

physics.soc-ph↗

arXiv2006

The Social Network of Contemporary Popular Musicians

In this paper we analyze two social network datasets of contemporary musicians constructed from allmusic.com (AMG), a music and artists' information database: one is the collaboration network in which two musicians are connected if they have performed in or produced an album together, and the other is the similarity network in which they are connected if they where musically similar according to music experts. We find that, while both networks exhibit typical features of social networks such as high transitivity, several key network features, such as degree as well as betweenness distributions suggest fundamental differences in music collaborations and music similarity networks are created.

physics.soc-ph↗

arXiv2006

Coincidence detection of inharmonic pulses in a nonlinear crystal

Two trains of light pulses at periods that are equally shifted from the harmonics of a missing fundamental, are combined in a nonlinear crystal. As a result of a noncollinear phase matched second order nonlinear generation, a new train of pulses is obtained. When the temporal width of the input pulses is large, the frequency of the resulting pulse train follows the observations from classical experiments on the perception of virtual pitch by the brain. On the other hand, when the width of the input pulses is small, the generated pulse train exhibits much lower frequencies, analogous to those observed in the motor neural system. Our experimental set up allows us to explore, systematically and continuously, the transition between these two regimes, while at the same time demonstrate that coincidence detection in quadratic nonlinear systems has functionalities similar to those observed in the nervous system.

cond-mat.other↗

arXiv2006

Synchronization by dynamical relaying in electronic circuit arrays

We experimentally study the synchronization of two chaotic electronic circuits whose dynamics is relayed by a third parameter-matched circuit, to which they are coupled bidirectionally in a linear chain configuration. In a wide range of operating parameters, this setup leads to synchronization between the outer circuits, while the relaying element remains unsynchronized. The specifics of the synchronization differ with the coupling level: for low couplings a state of intermittent synchronization between the outer circuits coexists with one of antiphase synchronization. Synchronization becomes in phase for moderate couplings, and for strong coupling identical synchronization is observed between the outer elements, which are themselves synchronized in a generalized way with the relaying element. In the latter situation, the middle element displays a triple scroll attractor that is not possible to obtain when the chaotic oscillator is isolated.

nlin.CD↗

arXiv2005

The Topology of Music Recommendation Networks

We study the topology of several music recommendation networks, which rise from relationships between artist, co-occurrence of songs in playlists or experts' recommendation. The analysis uncovers the emergence of complex network phenomena in this kind of recommendation networks, built considering artists as nodes and their resemblance as links. We observe structural properties that provide some hints on navigation and possible optimizations on the design of music recommendation systems. Finally, the analysis derived from existing music knowledge sources provides a deeper understanding of the human music similarity perceptions.

physics.soc-ph↗