arXiv subjects

Bosiljka Tadic

Publications and source records attributed to Bosiljka Tadic.

At least 19 recordsLinked to original sources

arXiv2026

Antiferromagnetic Barkhausen noise induced by weak random-field disorder

This study numerically investigates magnetisation reversal processes driven by an external magnetic field in three-dimensional antiferromagnetic spin models with weak random field disorder. Considering an extremely weak disorder and low temperature, we observe a step-wise hysteresis loop and the appearance of short magnetisation bursts of a characteristic triangular shape; the number of bursts increases with disorder, indicative of Barkhausen-type noise. These phenomena are attributed to the simultaneous reversal at a given external field of segments composed of spins with identical neighbourhoods. A local random field orients one or more spin neighbours, resulting in small, ferromagnetic-like clusters distributed throughout the system. As disorder increases, these clusters may merge to form a labyrinthine structure within the antiferromagnetic background, facilitating brief avalanche propagation. The results demonstrate that, compared with familiar random-field ferromagnets, the observed antiferromagnetic Barkhausen noise and the related avalanche sequence have a profoundly different structure, organised into peaks associated with the transition between magnetisation plateaus. They exhibit prominent cyclical trends and disorder-dependent multifractal fluctuations, with the singularity spectrum quantifying the degree of disorder. The activity avalanches exhibit scale invariance resembling that recently found in experiments with disordered ferr\textit{i}magnets and martensites, as well as in quantum Barkhausen noise, which are associated with active geometric regions rather than individual-spin dynamics. The observed scaling behaviour is interpreted in terms of self-organised critical dynamics.

cond-mat.dis-nn↗

arXiv2025

Multiscale phase oscillations induced by cluster synchronisation in human connectome core network

Brain imaging data mapping onto human connectome networks enables the investigation of global brain dynamics, where the brain hubs play an essential role in transferring activity between different brain parts. At this scale, the synchronisation processes are increasingly investigated as one of the key mechanisms revealing many aspects of brain functional coherence in healthy brains and revealing deviations due to various brain disorders. For the human connectome core network, consisting of the eight brain hubs and the higher-order structure attached to them, previous simulations of Kuramoto phase oscillators at network nodes indicate instability of the global order parameter for a range of positive coupling strengths. In this work, we investigate the multiscale oscillations of the global phase order parameter and show that they are connected with the cluster synchronisation processes occurring in this range of couplings below the master stability threshold. We use the spectral graph analysis and eigenvector localisation methodology, where the clusters of nodes playing a similar role in the synchronisation processes have a small mutual distance in the eigenvector space. We determine three significant clusters of brain regions and show the position of hubs in them. With the parallel analysis of the weighted core network and its binary version, we demonstrate the primary role of the network's topology in the formation of synchronised clusters. Meanwhile, the wights of edges contribute to the hub's synchronisation with the surrounding cluster, stabilise the order parameter variations and reduce the multifractal spectrum.

nlin.PS↗

arXiv2024

Fundamental interactions in self-organized critical dynamics on higher-order networks

In functionally complex systems, higher-order connectivity is often revealed in the underlying geometry of networked units. Furthermore, such systems often show signatures of self-organized criticality, a specific type of non-equilibrium collective behaviour associated with an attractor of internal dynamics with long-range correlations and scale invariance, which ensures the robust functioning of complex systems, such as the brain. Here, we highlight the intertwining of features of higher-order geometry and self-organized critical dynamics as a plausible mechanism for the emergence of new properties on a larger scale, representing the central paradigm of the physical notion of complexity. Considering the time scale of the structural evolution with the known separation of the time scale in self-organized criticality, i.e., internal dynamics and external driving, we distinguish three classes of geometries that can shape the self-organized dynamics on them differently. We provide an overview of current trends in the study of collective dynamics phenomena, such as the synchronization of phase oscillators and discrete spin dynamics with higher-order couplings embedded in the faces of simplicial complexes. For a representative example of self-organized critical behaviour induced by higher-order structures, we present a more detailed analysis of the dynamics of field-driven spin reversal on the hysteresis loops in simplicial complexes composed of triangles. These numerical results suggest that two fundamental interactions representing the edge-embedded and triangle-embedded couplings must be taken into account in theoretical models to describe the influence of higher-order geometry on critical dynamics.

cond-mat.stat-mech↗

arXiv2024

Self-organised dynamics beyond scaling of avalanches: Cyclic stress fluctuations in critical sandpiles

Recognising changes in collective dynamics in complex systems is essential for predicting potential events and their development. Possessing intrinsic attractors with laws associated with scale invariance, self-organised critical dynamics represent a suitable example for quantitatively studying changes in collective behaviour. We consider two prototypal models of self-organised criticality, the sandpile automata with deterministic (Bak-Tang-Wiesenfeld) and probabilistic (Manna model) dynamical rules, focusing on the nature of stress fluctuations induced by driving - adding grains during the avalanche propagation, and dissipation through avalanches that hit the system boundary. Our analysis of stress evolution time series reveals robust cycles modulated by collective fluctuations with dissipative avalanches. These modulated cycles are multifractal within a broad range of time scales. Features of the associated singularity spectra capture the differences in the dynamic rules behind the self-organised critical states and their response to the increased driving rate, altering the process stochasticity and causing a loss of avalanche scaling. In the related sequences of outflow current, the first return distributions are found to follow modified laws that describe different pathways to the gradual loss of cooperative behaviour in these two models. The spontaneous appearance of cycles is another characteristic of self-organised criticality. It can also help identify the prominence of self-organisational phenomenology in an empirical time series when underlying interactions and driving modes remain hidden.

cond-mat.stat-mech↗

arXiv2023

Effect of hidden geometry and higher-order interactions on the synchronization and hysteresis behaviour of phase oscillators on 5-cliques simplicial assemblies

The hidden geometry of simplicial complexes can influence the collective dynamics of nodes in different ways depending on the simplex-based interactions of various orders and competition between local and global structural features. We study a system of phase oscillators attached to nodes of 4-dimensional simplicial complexes and interacting via positive/negative edges-based pairwise $K_1$ and triangle-based triple $K_2\geq 0$ couplings. Three prototypal simplicial complexes are grown by aggregation of 5-cliques, controlled by the chemical affinity parameter $\nu$, resulting in sparse, mixed, and compact architecture, all of which have 1-hyperbolic graphs but different spectral dimensions. By changing the interaction strength $K_1\in[-4,2]$ along the forward and backward sweeps, we numerically determine individual phases of each oscillator and a global order parameter to measure the level of synchronisation. Our results reveal how different architectures of simplicial complexes, in conjunction with the interactions and internal-frequency distributions, impact the shape of the hysteresis loop and lead to patterns of locally synchronised groups that hinder global network synchronisation.

cond-mat.stat-mech↗

arXiv2021

Hysteresis and synchronization processes of Kuramoto oscillators on high-dimensional simplicial complexes with the competing simplex-encoded couplings

Recent studies of dynamic properties in complex systems point out the profound impact of hidden geometry features known as simplicial complexes, which enable geometrically conditioned many-body interactions. Studies of collective behaviours on the controlled-structure complexes can reveal the subtle interplay of geometry and dynamics. Here, we investigate the phase synchronisation dynamics under the competing interactions embedded on 1-simplex (edges) and 2-simplex (triangles) faces of a homogeneous 4-dimensional simplicial complex. Its underlying network is a 1-hyperbolic graph with the assortative correlations among the node's degrees and the spectral dimension that exceeds $d_s=4$. We determine the time-averaged system's order parameter to characterise the synchronisation level. In the absence of higher interactions, the complete synchrony is continuously reached with the increasing positive pairwise interactions ($K_1>0$), and a partial synchronisation for the negative couplings ($K_1<0$) with no apparent hysteresis. Similar behaviour occurs in the degree-preserved randomised network. In contrast, the synchronisation is absent for the negative pairwise coupling in the entirely random graph and simple scale-free networks. Increasing the strength $K_2\neq 0$ of the triangle-based interactions gradually hinders the synchronisation promoted by pairwise couplings, and the non-symmetric hysteresis loop opens with an abrupt desynchronisation transition towards the $K_1<0$ branch. However, for substantial triangle-based interactions, the frustration effects prevail, preventing the complete synchronisation, and the abrupt transition disappears. These findings shed new light on the mechanisms by which the high-dimensional simplicial complexes in natural systems, such as human connectomes, can modulate their native synchronisation processes.

cond-mat.stat-mech↗

arXiv2020

Hidden geometry and dynamics of complex networks: Spin reversal in nanoassemblies with pairwise and triangle-based interactions

Recent studies of networks representing complex systems from the brain to social graphs have revealed their higher-order architecture, which can be described by aggregates of simplexes (triangles, tetrahedrons, and higher cliques). Current research aims at quantifying these hidden geometries by the algebraic topology methods and deep graph theory and understanding the dynamic processes on simplicial complexes. Here, we use the recently introduced model for geometrical self-assembly of cliques to grow nano-networks of triangles and study the filed-driven spin reversal processes on them. With the antiferromagnetic interactions between the Ising spins attached to the nodes, this assembly ideally supports the geometric frustration, which is recognized as the origin of some new phenomena in condensed matter physics. In the dynamical model, a gradual switching from the pairwise to triangle-based interactions is controlled by a parameter. Thus, the spin frustration effects on each triangle give way to the mesoscopic ordering conditioned by a complex arrangement of triangles. We show how the balance between these interactions changes the shape of the hysteresis loop. Meanwhile, the fluctuations in the accompanying Barkhausen noise exhibit robust indicators of self-organized criticality, which is induced by the network geometry alone without any magnetic disorder.

cond-mat.soft↗

arXiv2020

The topology of higher-order complexes associated with brain-function hubs in human connectomes

Higher-order connectivity in complex systems described by simplexes of different orders provides a geometry for simplex-based dynamical variables and interactions. Simplicial complexes that constitute a functional geometry of the human connectome can be crucial for the brain complex dynamics. In this context, the best-connected brain areas, designated as hub nodes, play a central role in supporting integrated brain function. Here, we study the structure of simplicial complexes attached to eight global hubs in the female and male connectomes and identify the core networks among the affected brain regions. These eight hubs (Putamen, Caudate, Hippocampus and Thalamus-Proper in the left and right cerebral hemisphere) are the highest-ranking according to their topological dimension, defined as the number of simplexes of all orders in which the node participates. Furthermore, we analyse the weight-dependent heterogeneity of simplexes. We demonstrate changes in the structure of identified core networks and topological entropy when the threshold weight is gradually increased. These results highlight the role of higher-order interactions in human brain networks and provide additional evidence for (dis)similarity between the female and male connectomes.

q-bio.NC↗

arXiv2019

Topology of nanonetworks grown by aggregation of simplexes with defects

Motivated by the relevance of higher-order interactions in quantum physics and materials science at the nanoscale, recently a model has been introduced for new classes of networks that grow by the geometrically constrained aggregation of simplexes (triangles, tetrahedra and higher-order cliques). Their key features are hyperbolic geometry and hierarchical architecture with simplicial complexes, which can be described by the algebraic topology of graphs. Based on the model of chemically tunable self-assembly of simplexes [Šuvakov et al., Sci.Rep 8, 1987 (2018)], here we study the impact of defect simplexes on the course of the process and their organisation in the grown nanonetworks for varied chemical affinity parameter and the size of building simplexes. Furthermore, we demonstrate how the presence of patterned defect bonds can be utilised to alter the structure of the assembly after the growth process is completed. In this regard, we consider the structure left by the removal of defect bonds and quantify the changes in the structure of simplicial complexes as well as in the underlying topological graph, representing 1-skeleton of the simplicial complex. By introducing new types of nanonetworks, these results open a promising application of the network science for the design of complex materials. They also provide a deeper understanding of the mechanisms underlying the higher-order connectivity in many complex systems.

math.AT↗

arXiv2019

Spectral properties of hyperbolic nano-networks with tunable aggregation of simplexes

Cooperative self-assembly can result in complex nano-networks with new hyperbolic geometry. However, the relation between the hyperbolicity and spectral and dynamical features of these structures remains unclear. Using the model of aggregation of simplexes introduced in I [Sci. Rep., 8:1987, 2018], here we study topological and spectral properties of a large class of self-assembled structures consisting of monodisperse building blocks (cliques of size $n=3,4,5,6$) which self-assemble via sharing the geometrical shapes of a lower order. The size of the shared sub-structure is tunned by varying the chemical affinity $ν$ such that for significant positive $ν$ sharing the largest face is the most probable, while for $ν< 0$, attaching via a single node dominates. Our results reveal that, while the parameter of hyperbolicity remains $δ_{max}=1$ across the assemblies, their structure and spectral dimension $d_s$ vary with the size of cliques $n$ and the affinity when $ν\geq 0$. In this range, we findthat $d_s >4$ can be reached for $n\geq 5$ and sufficiently large $ν$. For the aggregates of triangles and tetrahedra, the spectral dimension remains in the range $d_s\in [2,4)$, as well as for the higher cliques at vanishing affinity. On the other end, for $ν< 0$, we find $d_s\eqsim 1.57$ independently on $n$. Moreover, the spectral distribution of the normalised Laplacian eigenvalues has a characteristic shape with peaks and a pronounced minimum, representing the hierarchical architecture of the simplicial complexes. These findings show how the structures compatible with complex dynamical properties can be assembled by controlling the higher-order connectivity among the building blocks.

cond-mat.stat-mech↗

arXiv2019

Functional Geometry of Human Connectome and Robustness of Gender Differences

Mapping the brain imaging data to networks, where each node represents a specific area of the brain, has enabled an objective graph-theoretic analysis of human connectome. However, the latent structure on higher-order connections remains unexplored, where many brain regions acting in synergy perform complex functions. Here we analyse this hidden structure using the simplicial complexes parametrisation where the shared faces of simplexes encode higher-order relationships between groups of nodes and emerging hyperbolic geometry. Based on data collected within the Human Connectome Project, we perform a systematic analysis of consensus networks of 100 female (F-connectome) and 100 male (M-connectome) subjects by varying the number of fibres launched. Our analysis reveals that the functional geometry of the common F\&M-connectome coincides with the M-connectome and is characterized by a complex architecture of simplexes to the 14th order, which is built in six anatomical communities, and short cycles among them. Furthermore, the F-connectome has additional connections that involve different brain regions, thereby increasing the size of simplexes and introducing new cycles. By providing new insights into the internal organisation of anatomical brain modules as well as into the links between them that are essential to dynamics, these results also highlight the functional gender-related differences

q-bio.NC↗

arXiv2018

The critical Barkhausen avalanches in thin random-field ferromagnets with an open boundary

The interplay between the critical fluctuations and the sample geometry is investigated numerically using thin random-field ferromagnets exhibiting the field-driven magnetisation reversal on the hysteresis loop. The system is studied along the theoretical critical line in the plane of random-field disorder and thickness. The thickness is varied to consider samples of various geometry between a two-dimensional plane and a complete three-dimensional lattice with an open boundary in the direction of the growing thickness. We perform a multi-fractal analysis of the Barkhausen noise signals and scaling of the critical avalanches of the domain wall motion. Our results reveal that, for sufficiently small thickness, the sample geometry profoundly affects the dynamics by modifying the spectral segments that represent small fluctuations and promoting the time-scale dependent multi-fractality. Meanwhile, the avalanche distributions display two distinct power-law regions, in contrast to those in the two-dimensional limit, and the average avalanche shapes are asymmetric. With increasing thickness, the scaling characteristics and the multi-fractal spectrum in thicker samples gradually approach the hysteresis loop criticality in three-dimensional systems. Thin ferromagnetic films are growing in importance technologically, and our results illustrate some new features of the domain wall dynamics induced by magnetisation reversal in these systems.

cond-mat.dis-nn↗

arXiv2018

Self-Organised Criticality and Emergent Hyperbolic Networks -- Blueprint for Complexity in Social Dynamics

Online social dynamics based on human endeavours exhibit prominent complexity in the emergence of new features embodied in the appearance of collective social values. The vast amount of empirical data collected at various websites provides a unique opportunity to quantitative study od the underlying social dynamics in full analogy with complex systems in the physics laboratory. Here, we briefly describe the extent of these analogies and indicate the methods from other science disciplines that the physics theory can incorporate to provide the adequate description of human entities and principles of their self-organisation. We demonstrate the approach on two examples using the empirical data regarding the knowledge creation processes in online chats and questions-and-answers. Precisely, we describe the self-organised criticality as the acting mechanisms in the social knowledge-sharing dynamics and demonstrate the emergence of the hyperbolic geometry of the co-evolving networks that underlie these stochastic processes.

physics.soc-ph↗

arXiv2017

Hidden geometries in networks arising from cooperative self-assembly

Multilevel self-assembly involving small structured groups of nano-particles provides new routes to development of functional materials with a sophisticated architecture. Apart from the inter-particle forces, the geometrical shapes and compatibility of the building blocks are decisive factors in each phase of growth. Therefore, a comprehensive understanding of these processes is essential for the design of large assemblies of desired properties. Here, we introduce a computational model for cooperative self-assembly with simultaneous attachment of structured groups of particles, which can be described by simplexes (connected pairs, triangles, tetrahedrons and higher order cliques) to a growing network, starting from a small seed. The model incorporates geometric rules that provide suitable nesting spaces for the new group and the chemical affinity $ν$ of the system to accepting an excess number of particles. For varying chemical affinity, we grow different classes of assemblies by binding the cliques of distributed sizes. Furthermore, to characterise the emergent large-scale structures, we use the metrics of graph theory and algebraic topology of graphs, and 4-point test for the intrinsic hyperbolicity of the networks. Our results show that higher Q-connectedness of the appearing simplicial complexes can arise due to only geometrical factors, i.e., for $ν= 0$, and that it can be effectively modulated by changing the chemical potential and the polydispersity of the size of binding simplexes. For certain parameters in the model we obtain networks of mono-dispersed clicks, triangles and tetrahedrons, which represent the geometrical descriptors that are relevant in quantum physics and frequently occurring chemical clusters.

physics.soc-ph↗

arXiv2017

Dynamical implications of sample shape for avalanches in 2-dimensional random-field Ising model with saw-tooth domain wall

We study dynamics of an injected domain wall (DW) in 2-dimensional disordered ferromagnets with different sample shapes using random-field Ising model on a square lattice rotated by 45 degrees. The saw-tooth DW of the length $L_x$ is created along one side and swept through the sample by slow ramping of the external field until the complete magnetisation reversal and the wall annihilation at the open top boundary at a distance $L_y$. By fixing the number of spins $N=L_x\times L_y=10^6$ and the random-field distribution above the critical disorder, we vary the ratio $L_x/L_y\in[1/16,16]$. The periodic boundary conditions are applied in the y-direction so that these ratios comprise different samples, i.e., surfaces of cylinders with the changing perimeter $L_x$ and height $L_y$. We analyse the avalanches of the DW slips between following field updates, and the multifractal structure of the magnetisation fluctuation time series. Our findings are that the domain-wall lengths materialised in different sample shapes have an impact on the dynamics at all scales.

cond-mat.dis-nn↗

arXiv2017

The mechanisms of self-organised criticality in social processes of knowledge creation

In the online social dynamics, a robust scaling behaviour appears as a key feature of many collaborative efforts that lead to the new social value. The underlying empirical data thus offer a unique opportunity to study the origin of self-organised criticality in social systems. In contrast to physical systems in the laboratory, various human attributes of the actors play an essential role in the process along with the contents (cognitive, emotional) of the communicated artefacts. As a prototypal example, we consider the social endeavour of knowledge creation via Questions\ \& Answers (Q\&A). Using a large empirical dataset from one of such Q\&A sites and theoretical modelling, we examine the temporal correlations at all scales and the role of cognitive contents to the avalanches of the knowledge creation process. Our analysis shows that the long-range correlations and the event clustering are primarily determined by the universal social dynamics, providing the external driving of the system by the arrival of new users. While the involved cognitive contents (systematically annotated in the data and observed in the model) are crucial for a fine structure of the developing knowledge networks, they only affect the values of the scaling exponents and the geometry of large avalanches and shape the multifractal spectrum. Furthermore, we find that the level of the activity of the communities that share the knowledge correlates with the fluctuations of the innovation rate, implying that the increase of innovation may serve as the active principle of self-organisation. To identify relevant parameters and unravel the role of the network evolution underlying the process in the social system under consideration, we compare the social avalanches to the avalanche sequences occurring in the field-driven physical model of disordered solids.

physics.soc-ph↗

arXiv2017

Geometrical features of time series provide new perspectives on collective fluctuations in driven disordered systems

Mapping time series onto graphs and the use of graph theory methods opens up the possibility to study the structure of the phase space manifolds underlying the fluctuations of a dynamical variable. Here, we propose to go beyond the standard graph measures and analyze the higher-order structures such as triangles, tetrahedra and higher-order cliques and their complexes occurring in the time-series networks, which are detectable by the algebraic topology methods. We apply the methodology to the signal of Barkhausen noise accompanying the domain-wall dynamics on the hysteresis loop of disordered ferromagnets driven by the external field. Our analysis demonstrates how the appearance of the complexes with cliques of a high order correlates to the enhanced collective fluctuations in the central part of the hysteresis loop, in contrast to the fractional Gaussian noise fluctuations at the beginning of the loop, which correspond to the graph of a simpler topology. The multifractal analysis of the corresponding segments of these time series confirms that we deal with different types of the stochastic process.

cond-mat.dis-nn↗

arXiv2016

Multifractal structure of Barkhausen noise: A signature of collective dynamics at hysteresis loop

The field-driven magnetisation reversal processes in disordered systems exhibit a collective behaviour that is manifested in the scale-invariance of avalanches, closely related to underlying dynamical mechanisms. Using the multifractal time series analysis, we study the structure of fluctuations at different scales in the accompanying Barkhausen noise. The stochastic signal represents the magnetisation discontinuities along the hysteresis loop of a 3-dimensional random field Ising model simulated for varied disorder strength and driving rates. The analysis of the spectrum of the generalised Hurst exponents reveals that the segments of the signal with large fluctuations represent two distinct classes of stochastic processes in weak and strong pinning regimes. Furthermore, increased driving rates have a profound effect on the small fluctuation segments and broadening of the spectrum. The study of the temporal correlations, sequences of avalanches, and their scaling features complements the quantitative measures of the collective dynamics at the hysteresis loop. The multifractal properties of Barkhausen noise describe the dynamical state of domains and precisely discriminate the weak pinning, permitting the motion of individual walls, from the mechanisms occurring in strongly disordered systems. The multifractal nature of the reversal processes is particularly relevant for currently investigated memory devices that utilize a controlled motion of individual domain walls.

cond-mat.dis-nn↗