arXiv subjects

G. Reza Jafari

Publications and source records attributed to G. Reza Jafari.

17 recordsLinked to original sources

arXiv2026

Persistent Imbalance in Open Networks with Coevolutionary dynamics

Societies are quintessential open systems, shaped by internal dynamics as well as external influences. The question is how these external influences alter the collective behavior and network dynamics. To answer this, we investigate coevolutionary balance dynamics in a system of independent and open networks. Here, the system consists of two interacting networks with directed (asymmetric) coupling: an independent network evolving autonomously and an open (dependent) network whose dynamics are influenced by the former. Using a mean-field framework, we demonstrate a transition temperature: below the transition temperature, the independent network reaches a state of structural balance, while the open network is destabilized by persistent imbalance states and enters a sustained imbalance phase. This coupling also induces a measurable upward shift in the transition temperature. Direct numerical simulations robustly confirm these analytical predictions.

physics.soc-ph↗

arXiv2026

Identifying preferred routes of sharing information on social networks

The spread of information has become faster and wider than ever with the advent of social network platforms. The question raised in this study is whether information dissemination in social networks is random or follows a discernible structure. Our results from real-world hashtag data suggest that the spread of hashtags is not random and follows specific patterns. This study proposes two preferential models to explore how news spreads on social media. Specifically, we examine global and local preferential selection models and demonstrate that information dissemination aligns with these patterns. According to these two models, information flows are distributed through specific paths on networks. This suggests that new information tends to propagate along the same paths as previous news, with the specific pathways varying depending on the type of content. Finally, an examination of the propagation of political hashtags on Twitter confirms the existence of these paths that also emerge from the two preferential models.

cs.SI↗

arXiv2026

Coevolutionary balance of resting-state brain networks in autism

Autism spectrum disorder (ASD) is associated with atypical large-scale brain organization, yet the functional principles underlying these alterations remain incompletely understood. We examined whether coevolutionary balance, a network-level energy measure derived from signed interactions and nodal activity states, captures disruptions in resting-state functional connectivity in autistic adults. Using resting-state fMRI data from ABIDE I with ComBat harmonization to mitigate multi-site batch effects, we constructed whole-brain networks by combining binarized fALFF activity with signed functional correlations and quantified their coevolutionary energy. In the primary analysis with global signal regression (GSR), the ASD group showed significantly more negative global coevolutionary energy (pFDR < 0.002), higher proportions of agreement links, and lower proportions of imbalanced-same links, indicating a systematic redistribution of local motifs rather than a uniform increase in balance. Because GSR can introduce artifactual negative correlations, we repeated all analyses without GSR. In this sensitivity analysis, whole-brain energy and motif differences were attenuated, but bipolarity, a measure of global two-block signed network organization, became the only FDR-significant metric (pFDR = 0.047), with ASD showing higher bipolarity. Intra-network energy differences did not survive FDR correction under either pipeline. Coevolutionary energy showed modest associations with ADI-R and ADOS scores, none of which survived correction across 720 tests. Machine learning classification achieved 77.8% test accuracy (AUC = 0.79) with GSR and 64.7% (AUC = 0.65) without GSR. These findings suggest that coevolutionary balance captures altered signed network organization in ASD, though the specific metric driving group differences depends on preprocessing choices regarding global signal regression.

q-bio.NC↗

arXiv2025

Patterns of imbalance states between sub-brain regimes during development in the resting state

The functional brain network emerges from the complex, coordinated activity of distinct yet connected regions, which underlie the diverse repertoire of human cognitive functions. Structural Balance Theory (SBT) has been successfully applied to model such nontrivial connections through the analysis of balance and unbalance triadic configurations. In this study, using SBT, we examine the network of imbalanced triads in the resting-state brain subnetworks, which undergo dynamic changes during development. We demonstrate that anticorrelation patterns evolve across the lifespan, reflecting a developmental trajectory from a locally modular organization in childhood to a flexible and reconfigurable architecture during adolescence and finally to a highly segregated and functionally specialized network system in adulthood. This developmental trajectory indicates that the spread of anticorrelations is not an inherent feature of brain organization. This mature organization facilitates a balance between self-referential, internally generated cognitive processes and externally oriented, goal-directed cognition, enabling efficient and adaptive cognitive control. This balance is underpinned by prominent anticorrelations between the Default Mode Network (DMN) and the Frontoparietal Network (FPN) in adulthood. This is while during adolescence these anticorrelations are substantially weaker, suggesting that the maturation of these network connections from adolescence to adulthood establishes a functional architecture that supports the segregation of internal and external cognitive processes. These findings elucidate how the dynamic evolution of anticorrelation patterns in brain networks supports cognitive development across the lifespan, offering new insights into the neural basis of adaptive cognitive control.

q-bio.NC↗

arXiv2025

Exploring Quantum Heider Balance Theory

Classical Heider balance theory models the evolution of social networks towards balanced states with stress minimization. Triad relationships are classically either balanced or imbalanced. However, real-world relationships often exhibit uncertainty, complexity, and interconnected dynamics that transcend this classical framework, and we will sometimes see the synchronicity of balance and imbalance states. When these triadics are simultaneously balanced and imbalanced, they form superposition and entanglement states that necessitate the introduction of quantum balance. This study introduces a framework that extends classical Heider balance theory from social network analysis by incorporating principles of quantum mechanics. In the framework, each triad is a quantum state of spin systems embedded in various network topologies. Using a quantum transformation operator, we investigate the emergence of balanced and imbalanced states and identify ground and steady states. In the quantum state, developments towards balance will occur by introducing the Hamiltonian within the creation and annihilation operators framework. In these developments, we witness the emergence of superposition and entanglement quantum states with no classical equivalent, which form an uncertainty in the states. When the quantum evolution is a functional temperature, we investigate the emergence of balanced and imbalanced states, analyze ground and steady states, and examine phase transitions as a function of temperature. Our results reveal novel dynamical behaviors unique to quantum social systems and offer a new perspective on collective decision-making, conflict resolution, and emergent order in complex networks.

physics.soc-ph↗

arXiv2025

Hierarchical Balance Theory: Emergence of Instability in Follower Layer Below Critical Temperatures

Hierarchy significantly shapes interactions in social structures by organizing individuals or groups based on status, power, or privilege. This study investigates how hierarchy affects structural balance as temperature variations, which measure an individual's average irrationality in society. To address this question, we develop a two-layer balance model, the \enquote{leader layer}, which maintains structural balance exclusively through intra-layer interactions. Conversely, the \enquote{follower layer} maintains structural equilibrium through both inter- and intra-layer interactions. The Hamiltonian of the leading layer is independent, while the follower layer depends on its parameters as well as those of the leading layer. Analytical results from the mean-field approximation and exact Monte Carlo simulations show that instability arises in the equilibrium states of the follower layer when the temperature is below the critical threshold ($T<T_c$), which is different from the structural Heider equilibrium. Furthermore, our findings indicate that the critical temperature is elevated in the follower layer.

physics.soc-ph↗

arXiv2024

Single replica spin-glass phase detection using field variation and machine learning

The Sherrington-Kirkpatrick spin-glass model used the replica symmetry method to find the phase transition of the system. In 1979-1980, Parisi proposed a solution based on replica symmetry breaking (RSB), which allowed him to identify the underlying phases of complex systems such as spin-glasses. Regardless of the method used for detection, the intrinsic phase of a system exists whether or not replicas are considered. We introduce a single replica method of spin-glass phase detection using the field's variation experienced by each spin in a system configuration. This method focuses on a single replica with quenched random couplings. Each spin inevitably observes a different field from the others. Our results show that the mean and variance of fields named "Spontaneous Configurational Field" experienced by spins are suitable indicators to explore different ferromagnetic, paramagnetic, and mixed phases. To classify different phases of the system with defined indicators we have developed an algorithm based on machine learning to analyze the desired samples.

cond-mat.dis-nn↗

arXiv2022

Quantum Bohmian Inspired Potential to Model Non-Gaussian Events and the Application in Financial Markets

We have implemented quantum modeling mainly based on Bohmian Mechanics to study time series that contain strong coupling between their events. We firstly propose how compared to normal densities, our target time series seem to be associated with a higher number of rare events, and Gaussian statistics tend to underestimate these events' frequency drastically. To this end, we suggest that by imposing Gaussian densities to the natural processes, one will seriously neglect the existence of extreme events in many circumstances. The central question of our study concerns the consideration of the effects of these rare events in the corresponding probability densities and studying their role from the point of view of quantum measurements. To model the non-Gaussian behavior of these time-series, we utilize the multifractal random walk (MRW) approach and control the non-Gaussianity parameter $λ$ accordingly. Using the framework of quantum mechanics, we then examine the role of $λ$ in quantum potentials derived for these time series. Our Bohmian quantum analysis shows that the derived potential takes some negative values in high frequencies (its mean values), then substantially increases, and the value drops again for the rare events. We thus conclude that these events could generate a potential barrier that the system, lingering in a non-Gaussian high-frequency region, encounters, and their role becomes more prominent when it comes to transversing this barrier. In this study, as an example of the application of quantum potential outside of the micro-world, we compute the quantum potentials for the S\&P financial market time series to verify the presence of rare events in the non-Gaussian densities for this real data and remark the deviation from the Gaussian case.

q-fin.MF↗

arXiv2022

High participation ratio genes in the interaction network structure

Genes have specific functional roles, however, since they are dependent on each other, they can play a structural role within a network structure of their interactions. In this study, we analyze the structure of the gene interaction network and detect the most contributing genes through the random matrix theory. Specifically, we compare the interaction network of essential and nonessential genes of the yeast Saccharomyces cerevisiae. Most remarkably, this well-established combined framework by measuring the node participation ratio $(NPR)$ index helps detect important genes, which control the insightful structural patterns in the underlying networks. Results indicate that the essential genes have higher values of $NPR$ rather than the nonessential ones which means that they have the most contribution to the network structure. It is worth mentioning that among all essential genes, the $NPR$ value of 5 significant ones is considerably higher than the other essential genes, and also the same is for 15 significant nonessential genes compared to the others. Thus, the significant essential genes strongly manage their network structure, while the significant nonessential genes, besides their global contributions, have weak effects on their network structure. Most strikingly, these genes existing in a limited number of structural patterns are responsible for the specific bioprocesses which are the signature of their networks.

physics.bio-ph↗

arXiv2022

Financial Crisis in the Framework of Non-zero Temperature Balance Theory

Financial crises are known as crashes that result in a sudden loss of value of financial assets in large part and they continue to occur from time to time surprisingly. In order to discover features of the financial network, the pairwise interaction of stocks has been considered in many research, but the existence of the strong correlation of stocks and their collective behavior in crisis made us address higher-order interactions. Hence, in this study, we investigate financial networks by triplet interaction in the framework of balance theory. Due to detecting the contribution of higher-order interactions in understanding the complex behavior of stocks we take the advantage of the orders parameters of the higher-order interactions. Looking at real data of financial market obtained from $S\&P500$ through the lens of balance theory for the quest of network structure in different periods of time near and far from crisis reveals the existence of a structural difference of the network that corresponds to different periods of time. Here, we address two well-known crises the Great regression (2008) and the Covid-19 recession (2020). Results show an ordered structure forms on-crisis in the financial network while stocks behave independently far from a crisis. The formation of the ordered structure of stocks in crisis makes the network resistant against disorder. The resistance of the ordered structure against applying a disorder (temperature) can measure the crisis strength and determine the temperature at which the network transits. There is a critical temperature, $T_{c}$, in the language of statistical mechanics and mean-field approach which above, the ordered structure destroys abruptly and a first-order phase transition occurs. The stronger the crisis, the higher the critical temperature.

q-fin.ST↗

arXiv2021

Individual versus Social Benefit on the Heterogeneous Networks

The focus of structural balance theory is dedicated to social benefits, while in a real network individual benefits sometimes get the importance as well. In Strauss's model, the local minima are modeled by considering an individual term besides a social one and the assumption is based on equal strength of individual benefits. The results show that the competition between two terms leads to a phase transition between individual and social benefits and there is a critical point, $CP$, that represents a first-order phase transition in the network. Concerning a real network of relations, individuals adjust the strength of their relationships based on the benefits they acquire from. Therefore, addressing heterogeneity in the individual interactions, we study a modified version of Strauss's model in which the first term represents the heterogeneous individual benefit by $θ_{ij}$, and the coefficient of the second term, $α$, measures the strength of social benefit. Our studies show that there is a region where the triangles are in a crumpled state rather than being dispersed in the network and increasing the heterogeneity of individual benefits results in the narrower region of crumpled state. Out of this region, the network is a mixture of links and triangles and the value of $α$ determines whether the individual benefit or social benefit overcomes. For the small value of $α$ the individual benefit dominates whereas in the large value of $α$ the social benefit overcomes.

physics.soc-ph↗

arXiv2021

The Footprint of Campaign Strategies in Farsi Twitter: A case for 2021 Iranian presidential election

The rise of social media accompanied by the Covid-19 Pandemic has instigated a shift in paradigm in the presidential campaigns in Iran from the real world to social media. Unlike previous presidential elections, there was a decrease in physical events and advertisements for the candidates; in turn, the online presence of presidential candidates is significantly increased. Farsi Twitter played a specific role in this matter, as it became the platform for creating political content. In this study, we found traces of organizational activities in Farsi Twitter. Our investigations reveals that the discussion network of the 2021 election is heterogeneous and highly polarized. However, unlike other elections, candidates' supporters are very close, and "Anti-voters" who endorse boycotting the election is at the discussions opposite end. Furthermore, high presence of the bot activity is observed among the most influential users in all of the involved communities.

cs.SI↗

arXiv2021

Competitive Balance Theory: Modeling conflict of interest in a heterogeneous network

The dynamics of networks on Heider balance theory moves toward reducing the tension by constantly reevaluating the interactions to achieve a state of balance. Conflict of interest, however, is inherent in most complex systems; frequently, there are multiple ideals or states of balance, and moving towards one could work against another. In this paper, by introducing the competitive balance theory, we study the evolution of balance in the presence of conflicts of interest. In our model, the assumption is that different states of balance compete in the evolution process to dominate the system. We ask, whether, through these interactions, different states of balance compete to prevail their own ideals or a set of co-existing ideals in a balanced condition is a possible outcome. The results show that although there is a symmetry in the type of balance, the system either evolves towards a symmetry breaking where one of the states of balance dominates the system, or, less frequently, the competing states of balance co exist in a jammed state.

physics.soc-ph↗

arXiv2020

Stability of Imbalanced Triangles in Gene Regulatory Networks of Cancerous and Normal Cells

Genes communicate with each other through different regulatory effects, which lead to the emergence of complex structures in cells, and such structures are expected to be different for normal and cancerous cells. To study breast cancer differences, we have investigated the Gene Regulatory Network (GRN) of cells as inferred from RNA-sequencing data. The GRN is a signed weighted network corresponding to the inductive or inhibitory interactions. Here we focus on a particular of motifs in the GRN, the triangles, which are imbalanced if the number of negative interactions are odd. By studying the stability of imbalanced triangles in the GRN, we show that the network of cancerous cells has fewer imbalanced triangles compared to normal. Moreover, in the normal cells, imbalanced triangles are isolated from the main part of the network, while such motifs are part of the network's giant component in cancerous cells. Our result demonstrates that due to genes' collective behavior the complex structures are different in cancerous cells from those in normal ones.

q-bio.MN↗

arXiv2020

Scaling Features of Price-Volume Cross-Correlation

Price without transaction makes no sense. Trading volume authenticates its corresponding price, so there exist mutual information and correlation between price and trading volume. We are curious about fractal features of this correlation and need to know how structures in different scales translate information. To explore the influence of investment size (trading volume), price-wise (gain/loss), and time-scale effects, we analyzed the price and trading volume and their coupling by applying the MF-DXA method. Our results imply that price, trading volume, and price-volume coupling exhibit a power law and are also multifractal. Meanwhile, considering developed markets, the price-volume couplings are significantly negatively correlated. However, in emerging markets, the price has less of a contribution to price-volume coupling. In emerging markets in comparison with the developed markets, trading volume and price are more independent.

q-fin.CP↗

arXiv2020

Mapping Coupled Time-series Onto Complex Network

In order to extract hidden joint information from two possibly uncorrelated time-series, we explored the measures of network science. Alongside common methods in time-series analysis of the economic markets, mapping the joint structure of two time-series onto a network provides insight into hidden aspects embedded in the couplings. We discretize the amplitude of two time-series and investigate relative simultaneous locations of those amplitudes. Each segment of a discretized amplitude is considered as a node. The simultaneity of the amplitudes of the two time-series is considered as the edges in the network. The frequency of occurrences forms the weighted edges. In order to extract information, we need to measure that to what extent the coupling deviates from the coupling of two uncoupled series. Also, we need to measure that to what extent the couplings inherit their characteristics from a Gaussian distribution or a non-Gaussian distribution. We mapped the network from two surrogate time-series. The results show that the couplings of markets possess some features which diverge from the same features of the network mapped from white noise, and from the network mapped from two surrogate time-series. These deviations prove that there exist joint information and cross-correlation therein. By applying the network's topological and statistical measures and the deformation ratio in the joint probability distribution, we distinguished basic structures of cross-correlation and coupling of cross-markets. It was discovered that even two possibly known uncorrelated markets may possess some joint patterns with each other. Thereby, those markets should be examined as coupled and \textit{weakly} coupled markets.

q-fin.CP↗

arXiv2020

Analysis of the Global Banking Network by Random Matrix Theory

Since 2008, the network analysis of financial systems is one of the most important subjects in economics. In this paper, we have used the complexity approach and Random Matrix Theory (RMT) for analyzing the global banking network. By applying this method on a cross border lending network, it is shown that the network has been denser and the connectivity between peripheral nodes and the central section has risen. Also, by considering the collective behavior of the system and comparing it with the shuffled one, we can see that this network obtains a specific structure. By using the inverse participation ratio concept, we can see that after 2000, the participation of different modes to the network has increased and tends to the market mode of the system. Although no important change in the total market share of trading occurs, through the passage of time, the contribution of some countries in the network structure has increased. The technique proposed in the paper can be useful for analyzing different types of interaction networks between countries.

q-fin.ST↗